R language packages for Anaconda

The R language packages are available to install with conda at http://repo.anaconda.com/pkgs/r/. You can install any of these R language packages into your current environment with the conda command conda install -c r package-name.

Note

Replace package-name with the name of the package. For example, you can install the package``r-acepack`` with the command conda install -c r r-acepack.

Many Comprehensive R Archive Network (CRAN) packages are available as conda packages. Anaconda does not provide builds of the entire CRAN repository, so there are some packages in CRAN that are not available as conda packages.

Tip

You can also search for any R package if you know the name, such as conda search -f r-EXACTNAME. Replace EXACTNAME with the desired CRAN or MRAN R package name. For example, for rbokeh, you would use conda search -f r-rbokeh.

R Essentials bundle

Rather than install each R language package individually, you can get the R Essentials bundle. It includes approximately 80 of the most popular scientific packages for the R programming language.

You can install the R Essentials bundle with this command:

conda install -c r r-essentials

More resources

List of R packages for Anaconda

Number of supported packages: 4885

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Misc
Name Version Summary/License Platforms
_r-mutex 1.0.0 A mutex package to ensure environment exclusivity between Anaconda R and MRO. / BSD linux-32, linux-64, osx-64, win-32, win-64
_r-xgboost-mutex 2.0 None / None linux-64, osx-64, win-64
A
Name Version Summary/License Platforms
abind 1.4_5 Combine multidimensional arrays into a single array. This is a generalization of ‘cbind’ and ‘rbind’. Works with vectors, matrices, and higher-dimensional arrays. Also provides functions ‘adrop’, ‘asub’, and ‘afill’ for manipulating, extracting and replacing data in arrays. / LGPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
acepack 1.4.1 Two nonparametric methods for multiple regression transform selection are provided. The first, Alternative Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. Estimating Optimal Transformations for Multiple Regression and Correlation. Journal of the American Statistical Association. 80:580-598. <doi:10.1080/01621459.1985.10478157>]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low [see Tibshirani, R.. 1986. Estimating Transformations for Regression via Additivity and Variance Stabilization. Journal of the American Statistical Association. 83:394-405. <doi:10.1080/01621459.1988.10478610>]. A good introduction to these two methods is in chapter 16 of Frank Harrel’s Regression Modeling Strategies in the Springer Series in Statistics. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
adgoftest 0.3 Anderson-Darling GoF test with p-value calculation based on Marsaglia’s 2004 paper Evaluating the Anderson-Darling Distribution / GPL linux-32, linux-64, noarch, osx-64, win-32, win-64
aer 1.2_6 Functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette AER for a package overview.) / GPL-2 | GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
afex 0.23_0 Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software). / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
anomalydetection 1.0 A technique for detecting anomalies in seasonal univariate time series. / Apache License 2.0 linux-32, linux-64, osx-64, win-32, win-64
ape 5.3 Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel’s test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
argparse 2.0.1 A command line parser to be used with Rscript to write #! shebang scripts that gracefully accept positional and optional arguments and automatically generate usage. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
askpass 1.0 Cross-platform utilities for prompting the user for credentials or a passphrase, for example to authenticate with a server or read a protected key. Includes native programs for MacOS and Windows, hence no ‘tcltk’ is required. Password entry can be invoked in two different ways: directly from R via the askpass() function, or indirectly as password-entry back-end for ‘ssh-agent’ or ‘git-credential’ via the SSH_ASKPASS and GIT_ASKPASS environment variables. Thereby the user can be prompted for credentials or a passphrase if needed when R calls out to git or ssh. / MIT file LICENSE linux-64, osx-64, win-32, win-64
assertthat 0.2.1 An extension to stopifnot() that makes it easy to declare the pre and post conditions that you code should satisfy, while also producing friendly error messages so that your users know what’s gone wrong. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
B
Name Version Summary/License Platforms
backports 1.1.4 Functions introduced or changed since R v3.0.0 are re-implemented in this package. The backports are conditionally exported in order to let R resolve the function name to either the implemented backport, or the respective base version, if available. Package developers can make use of new functions or arguments by selectively importing specific backports to support older installations. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
base64enc 0.1_3 This package provides tools for handling base64 encoding. It is more flexible than the orphaned base64 package. / GPL-2 | GPL-3 linux-32, linux-64, osx-64, win-32, win-64
bcp 4.0.3 Provides an implementation of the Barry and Hartigan (1993) product partition model for the normal errors change point problem using Markov Chain Monte Carlo. It also extends the methodology to regression models on a connected graph (Wang and Emerson, 2015); this allows estimation of change point models with multivariate responses. Parallel MCMC, previously available in bcp v.3.0.0, is currently not implemented. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
bdsmatrix 1.3_3 This is a special case of sparse matrices, used by coxme. / LGPL-2 linux-32, linux-64, osx-64, win-32, win-64
bestglm 0.37 Best subset glm using information criteria or cross-validation. Implements PCR and PLS using AIC/BIC. Implements one-standard deviation rule for use with the caret package. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
bh 1.69.0_1 Boost provides free peer-reviewed portable C source libraries. A large part of Boost is provided as C template code which is resolved entirely at compile-time without linking. This package aims to provide the most useful subset of Boost libraries for template use among CRAN package. By placing these libraries in this package, we offer a more efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. As of release 1.69.0-1, the following Boost libraries are included: ‘algorithm’ ‘align’ ‘any’ ‘atomic’ ‘bimap’ ‘bind’ ‘circular_buffer’ ‘compute’ ‘concept’ ‘config’ ‘container’ ‘date_time’ ‘detail’ ‘dynamic_bitset’ ‘exception’ ‘filesystem’ ‘flyweight’ ‘foreach’ ‘functional’ ‘fusion’ ‘geometry’ ‘graph’ ‘heap’ ‘icl’ ‘integer’ ‘interprocess’ ‘intrusive’ ‘io’ ‘iostreams’ ‘iterator’ ‘math’ ‘move’ ‘mpl’ ‘multiprcecision’ ‘numeric’ ‘pending’ ‘phoenix’ ‘preprocessor’ ‘propery_tree’ ‘random’ ‘range’ ‘scope_exit’ ‘smart_ptr’ ‘sort’ ‘spirit’ ‘tuple’ ‘type_traits’ ‘typeof’ ‘unordered’ ‘utility’ ‘uuid’. / BSL-1.0 linux-32, linux-64, noarch, osx-64, win-32, win-64
bibtex 0.4.2 Utility to parse a bibtex file. / GPL (>= 2) linux-64, osx-64, win-32, win-64
bindr 0.1.1 Provides a simple interface for creating active bindings where the bound function accepts additional arguments. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
bindrcpp 0.2.2 Provides an easy way to fill an environment with active bindings that call a C function. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
bit 1.1_14 True boolean datatype (no NAs), coercion from and to logicals, integers and integer subscripts; fast boolean operators and fast summary statistics. With ‘bit’ vectors you can store true binary booleans {FALSE,TRUE} at the expense of 1 bit only, on a 32 bit architecture this means factor 32 less RAM and ~ factor 32 more speed on boolean operations. Due to overhead of R calls, actual speed gain depends on the size of the vector: expect gains for vectors of size > 10000 elements. Even for one-time boolean operations it can pay-off to convert to bit, the pay-off is obvious, when such components are used more than once. Reading from and writing to bit is approximately as fast as accessing standard logicals - mostly due to R’s time for memory allocation. The package allows to work with pre-allocated memory for return values by calling .Call() directly: when evaluating the speed of C-access with pre-allocated vector memory, coping from bit to logical requires only 70% of the time for copying from logical to logical; and copying from logical to bit comes at a performance penalty of 150%. the package now contains further classes for representing logical selections: ‘bitwhich’ for very skewed selections and ‘ri’ for selecting ranges of values for chunked processing. All three index classes can be used for subsetting ‘ff’ objects (ff-2.1-0 and higher). / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
bit64 0.9_7 Package ‘bit64’ provides serializable S3 atomic 64bit (signed) integers. These are useful for handling database keys and exact counting in -2^63. WARNING: do not use them as replacement for 32bit integers, integer64 are not supported for subscripting by R-core and they have different semantics when combined with double, e.g. integer64 double => integer64. Class integer64 can be used in vectors, matrices, arrays and data.frames. Methods are available for coercion from and to logicals, integers, doubles, characters and factors as well as many elementwise and summary functions. Many fast algorithmic operations such as ‘match’ and ‘order’ support inter- active data exploration and manipulation and optionally leverage caching. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
bitops 1.0_6 Functions for bitwise operations on integer vectors. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
blob 1.1.1 R’s raw vector is useful for storing a single binary object. What if you want to put a vector of them in a data frame? The ‘blob’ package provides the blob object, a list of raw vectors, suitable for use as a column in data frame. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
boot 1.3_20 Functions and datasets for bootstrapping from the book Bootstrap Methods and Their Application by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S. / Unlimited linux-32, linux-64, noarch, osx-64, win-32, win-64
boruta 6.0.0 An all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes’ importance with importance achievable at random, estimated using their permuted copies (shadows). / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
bradleyterry2 1.0_9 Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
brew 1.0_6 brew implements a templating framework for mixing text and R code for report generation. brew template syntax is similar to PHP, Ruby’s erb module, Java Server Pages, and Python’s psp module. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
brglm 0.6.2 Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as ‘glm’. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
broom 0.5.2 Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
C
Name Version Summary/License Platforms
cairo 1.5_10 R graphics device using cairographics library that can be used to create high-quality vector (PDF, PostScript and SVG) and bitmap output (PNG,JPEG,TIFF), and high-quality rendering in displays (X11 and Win32). Since it uses the same back-end for all output, copying across formats is WYSIWYG. Files are created without the dependence on X11 or other external programs. This device supports alpha channel (semi-transparent drawing) and resulting images can contain transparent and semi-transparent regions. It is ideal for use in server environments (file output) and as a replacement for other devices that don’t have Cairo’s capabilities such as alpha support or anti-aliasing. Backends are modular such that any subset of backends is supported. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
callr 3.2.0 It is sometimes useful to perform a computation in a separate R process, without affecting the current R process at all. This packages does exactly that. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
car 3.0_2 Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, in press. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
cardata 3.0_2 Datasets to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage (forthcoming). / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
caret 6.0_83 Misc functions for training and plotting classification and regression models. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
catools 1.17.1.2 Contains several basic utility functions including: moving (rolling, running) window statistic functions, read/write for GIF and ENVI binary files, fast calculation of AUC, LogitBoost classifier, base64 encoder/decoder, round-off-error-free sum and cumsum, etc. / GPL-3 linux-32, linux-64, osx-64, win-32, win-64
cellranger 1.1.0 Helper functions to work with spreadsheets and the A1:D10 style of cell range specification. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
checkmate 1.9.1 Tests and assertions to perform frequent argument checks. A substantial part of the package was written in C to minimize any worries about execution time overhead. / BSD_3_clause file LICENSE linux-32, linux-64, osx-64, win-32, win-64
checkpoint 0.4.4 The goal of checkpoint is to solve the problem of package reproducibility in R. Specifically, checkpoint allows you to install packages as they existed on CRAN on a specific snapshot date as if you had a CRAN time machine. To achieve reproducibility, the checkpoint() function installs the packages required or called by your project and scripts to a local library exactly as they existed at the specified point in time. Only those packages are available to your project, thereby avoiding any package updates that came later and may have altered your results. In this way, anyone using checkpoint’s checkpoint() can ensure the reproducibility of your scripts or projects at any time. To create the snapshot archives, once a day (at midnight UTC) Microsoft refreshes the Austria CRAN mirror on the Microsoft R Archived Network server (<https://mran.microsoft.com/>). Immediately after completion of the rsync mirror process, the process takes a snapshot, thus creating the archive. Snapshot archives exist starting from 2014-09-17. / GPL-2 linux-64, osx-64, win-64
chron 2.3_53 Provides chronological objects which can handle dates and times. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
class 7.3_15 Various functions for classification, including k-nearest neighbour, Learning Vector Quantization and Self-Organizing Maps. / GPL-2 | GPL-3 linux-32, linux-64, osx-64, win-32, win-64
classint 0.3_1 Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
cli 1.1.0 A suite of tools designed to build attractive command line interfaces (‘CLIs’). Includes tools for drawing rules, boxes, trees, and ‘Unicode’ symbols with ‘ASCII’ alternatives. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
clipr 0.6.0 Simple utility functions to read from and write to the Windows, OS X, and X11 clipboards. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
clisymbols 1.2.0 A small subset of Unicode symbols, that are useful when building command line applications. They fall back to alternatives on terminals that do not support Unicode. Many symbols were taken from the ‘figures’ ‘npm’ package (see <https://github.com/sindresorhus/figures>). / MIT file LICENSE noarch
cluster 2.0.8 Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) Finding Groups in Data. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
coda 0.19_2 Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
codetools 0.2_16 Code analysis tools for R. / GPL linux-32, linux-64, noarch, osx-64, win-32, win-64
coin 1.3_0 Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
colorspace 1.4_1 Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny GUI) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). / BSD_3_clause file LICENSE linux-32, linux-64, osx-64, win-32, win-64
commonmark 1.7 The CommonMark specification defines a rationalized version of markdown syntax. This package uses the ‘cmark’ reference implementation for converting markdown text into various formats including html, latex and groff man. In addition it exposes the markdown parse tree in xml format. Also includes opt-in support for GFM extensions including tables, autolinks, and strikethrough text. / BSD_2_clause file LICENSE linux-32, linux-64, osx-64, win-32, win-64
config 0.3 Manage configuration values across multiple environments (e.g. development, test, production). Read values using a function that determines the current environment and returns the appropriate value. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
copula 0.999_19.1 Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function. / GPL (>= 3) | file LICENCE linux-32, linux-64, osx-64, win-32, win-64
crayon 1.3.4 Colored terminal output on terminals that support ‘ANSI’ color and highlight codes. It also works in ‘Emacs’ ‘ESS’. ‘ANSI’ color support is automatically detected. Colors and highlighting can be combined and nested. New styles can also be created easily. This package was inspired by the ‘chalk’ ‘JavaScript’ project. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
crosstalk 1.0.0 Provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e. static .html files). Currently supports linked brushing and filtering. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
ctv 0.8_5 Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated packages (client-side tools), generating HTML pages and storing task view information in the repository (server-side tools). / GPL-2 | GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
curl 3.3 The curl() and curl_download() functions provide highly configurable drop-in replacements for base url() and download.file() with better performance, support for encryption (https, ftps), gzip compression, authentication, and other ‘libcurl’ goodies. The core of the package implements a framework for performing fully customized requests where data can be processed either in memory, on disk, or streaming via the callback or connection interfaces. Some knowledge of ‘libcurl’ is recommended; for a more-user-friendly web client see the ‘httr’ package which builds on this package with http specific tools and logic. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
cvst 0.2_2 The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran’s Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts. / GPL (>= 2.0) linux-32, linux-64, noarch, osx-64, win-32, win-64
cvtools 0.3.2 Tools that allow developers to write functions for cross-validation with minimal programming effort and assist users with model selection. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
D
Name Version Summary/License Platforms
data.table 1.12.2 Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development. / MPL-2.0 | file LICENSE linux-32, linux-64, osx-64, win-32, win-64
dbi 1.0.0 A database interface definition for communication between R and relational database management systems. All classes in this package are virtual and need to be extended by the various R/DBMS implementations. / LGPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
dbplyr 1.4.0 A ‘dplyr’ back end for databases that allows you to work with remote database tables as if they are in-memory data frames. Basic features works with any database that has a ‘DBI’ back end; more advanced features require ‘SQL’ translation to be provided by the package author. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
ddalpha 1.3.9 Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
debugme 1.1.0 Specify debug messages as special string constants, and control debugging of packages via environment variables. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
deoptimr 1.0_8 Differential Evolution (DE) stochastic algorithms for global optimization of problems with and without constraints. The aim is to curate a collection of its state-of-the-art variants that (1) do not sacrifice simplicity of design, (2) are essentially tuning-free, and (3) can be efficiently implemented directly in the R language. Currently, it only provides an implementation of the ‘jDE’ algorithm by Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
deployrrserve 9.0.0 Rserve acts as a socket server (TCP/IP or local sockets) which allows binary requests to be sent to R. Every connection has a separate workspace and working directory. Client-side implementations are available for popular languages such as C/C and Java, allowing any application to use facilities of R without the need of linking to R code. Rserve supports remote connection, user authentication and file transfer. A simple R client is included in this package as well. / GPL version 2 linux-64, win-64
desc 1.2.0 Tools to read, write, create, and manipulate DESCRIPTION files. It is intended for packages that create or manipulate other packages. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
devtools 2.0.2 Collection of package development tools. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
diagrammer 1.0.1 Build graph/network structures using functions for stepwise addition and deletion of nodes and edges. Work with data available in tables for bulk addition of nodes, edges, and associated metadata. Use graph selections and traversals to apply changes to specific nodes or edges. A wide selection of graph algorithms allow for the analysis of graphs. Visualize the graphs and take advantage of any aesthetic properties assigned to nodes and edges. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
dichromat 2.0_0 Collapse red-green or green-blue distinctions to simulate the effects of different types of color-blindness. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
digest 0.6.18 Implementation of a function ‘digest()’ for the creation of hash digests of arbitrary R objects (using the ‘md5’, ‘sha-1’, ‘sha-256’, ‘crc32’, ‘xxhash’ and ‘murmurhash’ algorithms) permitting easy comparison of R language objects, as well as a function ‘hmac()’ to create hash-based message authentication code. Please note that this package is not meant to be deployed for cryptographic purposes for which more comprehensive (and widely tested) libraries such as ‘OpenSSL’ should be used. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
dimred 0.2.2 A collection of dimensionality reduction techniques from R packages and a common interface for calling the methods. / GPL-3 | file LICENSE linux-32, linux-64, osx-64, win-32, win-64
domc 1.3.5 Provides a parallel backend for the %dopar% function using the multicore functionality of the parallel package. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
doparallel 1.0.14 Provides a parallel backend for the %dopar% function using the parallel package. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
downloader 0.4 Provides a wrapper for the download.file function, making it possible to download files over HTTPS on Windows, Mac OS X, and other Unix-like platforms. The ‘RCurl’ package provides this functionality (and much more) but can be difficult to install because it must be compiled with external dependencies. This package has no external dependencies, so it is much easier to install. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
dplyr 0.8.0.1 A fast, consistent tool for working with data frame like objects, both in memory and out of memory. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
drr 0.0.3 An Implementation of Dimensionality Reduction via Regression using Kernel Ridge Regression. / GPL-3 | file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
dt 0.5 Data objects in R can be rendered as HTML tables using the JavaScript library ‘DataTables’ (typically via R Markdown or Shiny). The ‘DataTables’ library has been included in this R package. The package name ‘DT’ is an abbreviation of ‘DataTables’. / GPL-3 | file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
dygraphs 1.1.1.6 An R interface to the ‘dygraphs’ JavaScript charting library (a copy of which is included in the package). Provides rich facilities for charting time-series data in R, including highly configurable series- and axis-display and interactive features like zoom/pan and series/point highlighting. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
E
Name Version Summary/License Platforms
e1071 1.7_1 Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, … / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
ellipsis 0.1.0 In S3 generics, it’s useful to take … so that methods can have additional argument. But this flexibility comes at a cost: misspelled arguments will be silently ignored. The ellipsis packages is an experiment that allows a generic to warn if any arguments passed in … are not used. / GPL-3 linux-64, osx-64, win-32, win-64
emmeans 1.3.4 Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and compact letter displays. Least-squares means are discussed, and the term estimated marginal means is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>. / GPL-2 | GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
essentials 3.6.0 Some essential packages for working with R / Various linux-32, linux-64, osx-64, win-32, win-64
essentials-mrclient 3.4.3 Essential R packages including MS R Client and MS Machine Learning / Various linux-64, win-64
estimability 1.3 Provides tools for determining estimability of linear functions of regression coefficients, and ‘epredict’ methods that handle non-estimable cases correctly. Estimability theory is discussed in many linear-models textbooks including Chapter 3 of Monahan, JF (2008), A Primer on Linear Models, Chapman and Hall (ISBN 978-1-4200-6201-4). / GPL (>= 3) linux-32, linux-64, noarch, osx-64, win-32, win-64
evaluate 0.13 Parsing and evaluation tools that make it easy to recreate the command line behaviour of R. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
F
Name Version Summary/License Platforms
fansi 0.4.0 Counterparts to R string manipulation functions that account for the effects of ANSI text formatting control sequences. / GPL (>= 2) linux-64, osx-64, win-32, win-64
fastica 1.2_1 Implementation of FastICA algorithm to perform Independent Component Analysis (ICA) and Projection Pursuit. / GPL-2 | GPL-3 linux-32, linux-64, osx-64, win-32, win-64
fbasics 3042.89 Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Even more there are several utility functions for data handling and management. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
feather 0.3.3 Read and write feather files, a lightweight binary columnar data store designed for maximum speed. / Apache License 2.0 linux-32, linux-64, osx-64, win-32, win-64
fftw 1.0_5 Provides a simple and efficient wrapper around the fastest Fourier transform in the west (FFTW) library <http://www.fftw.org/>. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
fgarch 3042.83.1 Provides a collection of functions to analyze and model heteroskedastic behavior in financial time series models. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
findpython 1.0.5 Package designed to find an acceptable python binary. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
forcats 0.4.0 Helpers for reordering factor levels (including moving specified levels to front, ordering by first appearance, reversing, and randomly shuffling), and tools for modifying factor levels (including collapsing rare levels into other, ‘anonymising’, and manually ‘recoding’). / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
foreach 1.5.0 Support for the foreach looping construct. Foreach is an idiom that allows for iterating over elements in a collection, without the use of an explicit loop counter. This package in particular is intended to be used for its return value, rather than for its side effects. In that sense, it is similar to the standard lapply function, but doesn’t require the evaluation of a function. Using foreach without side effects also facilitates executing the loop in parallel. / Apache License (== 2.0) linux-32, linux-64, noarch, osx-64, win-32, win-64
forecast 8.6 Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling. / GPL-3 linux-32, linux-64, osx-64, win-32, win-64
foreign 0.8_71 Reading and writing data stored by some versions of ‘Epi Info’, ‘Minitab’, ‘S’, ‘SAS’, ‘SPSS’, ‘Stata’, ‘Systat’, ‘Weka’, and for reading and writing some ‘dBase’ files. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
forge 0.2.0 Helper functions with a consistent interface to coerce and verify the types and shapes of values for input checking. / Apache License (>= 2.0) noarch
formatr 1.6 Provides a function tidy_source() to format R source code. Spaces and indent will be added to the code automatically, and comments will be preserved under certain conditions, so that R code will be more human-readable and tidy. There is also a Shiny app as a user interface in this package (see tidy_app()). / GPL linux-32, linux-64, noarch, osx-64, win-32, win-64
formattable 0.2.0.1 Provides functions to create formattable vectors and data frames. ‘Formattable’ vectors are printed with text formatting, and formattable data frames are printed with multiple types of formatting in HTML to improve the readability of data presented in tabular form rendered in web pages. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
formula 1.2_3 Infrastructure for extended formulas with multiple parts on the right-hand side and/or multiple responses on the left-hand side (see <DOI:10.18637/jss.v034.i01>). / GPL-2 | GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
fracdiff 1.4_2 Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989). / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
fs 1.2.7 A cross-platform interface to file system operations, built on top of the ‘libuv’ C library. / GPL-3 linux-64, osx-64, win-32, win-64
functional 0.6 Curry, Compose, and other higher-order functions / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
fwdselect 2.1.0 A simple method to select the best model or best subset of variables using different types of data (binary, Gaussian or Poisson) and applying it in different contexts (parametric or non-parametric). / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
G
Name Version Summary/License Platforms
gdata 2.18.0 Various R programming tools for data manipulation, including: - medical unit conversions (‘ConvertMedUnits’, ‘MedUnits’), - combining objects (‘bindData’, ‘cbindX’, ‘combine’, ‘interleave’), - character vector operations (‘centerText’, ‘startsWith’, ‘trim’), - factor manipulation (‘levels’, ‘reorder.factor’, ‘mapLevels’), - obtaining information about R objects (‘object.size’, ‘elem’, ‘env’, ‘humanReadable’, ‘is.what’, ‘ll’, ‘keep’, ‘ls.funs’, ‘Args’,’nPairs’, ‘nobs’), - manipulating MS-Excel formatted files (‘read.xls’, ‘installXLSXsupport’, ‘sheetCount’, ‘xlsFormats’), - generating fixed-width format files (‘write.fwf’), - extricating components of date & time objects (‘getYear’, ‘getMonth’, ‘getDay’, ‘getHour’, ‘getMin’, ‘getSec’), - operations on columns of data frames (‘matchcols’, ‘rename.vars’), - matrix operations (‘unmatrix’, ‘upperTriangle’, ‘lowerTriangle’), - operations on vectors (‘case’, ‘unknownToNA’, ‘duplicated2’, ‘trimSum’), - operations on data frames (‘frameApply’, ‘wideByFactor’), - value of last evaluated expression (‘ans’), and - wrapper for ‘sample’ that ensures consistent behavior for both scalar and vector arguments (‘resample’). / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
generics 0.0.2 In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics. / GPL-2 linux-32, linux-64, noarch, osx-64, win-64
geometry 0.4.1 Makes the ‘Qhull’ library <http://www.qhull.org> available in R, in a similar manner as in Octave and MATLAB. Qhull computes convex hulls, Delaunay triangulations, halfspace intersections about a point, Voronoi diagrams, furthest-site Delaunay triangulations, and furthest-site Voronoi diagrams. It runs in 2D, 3D, 4D, and higher dimensions. It implements the Quickhull algorithm for computing the convex hull. Qhull does not support constrained Delaunay triangulations, or mesh generation of non-convex objects, but the package does include some R functions that allow for this. / GPL (>= 3) linux-32, linux-64, osx-64, win-32, win-64
getopt 1.20.3 Package designed to be used with Rscript to write ``#!’’ shebang scripts that accept short and long flags/options. Many users will prefer using instead the packages optparse or argparse which add extra features like automatically generated help option and usage, support for default values, positional argument support, etc. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
ggplot2 3.1.1 A system for ‘declaratively’ creating graphics, based on The Grammar of Graphics. You provide the data, tell ‘ggplot2’ how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. / GPL-2 | file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
ggvis 0.4.4 An implementation of an interactive grammar of graphics, taking the best parts of ‘ggplot2’, combining them with the reactive framework of ‘shiny’ and drawing web graphics using ‘vega’. / GPL-2 | file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
gh 1.0.1 Minimal client to access the ‘GitHub’ ‘API’. / MIT file LICENSE noarch
gistr 0.4.2 Work with ‘GitHub’ ‘gists’ from ‘R’ (e.g., <http://en.wikipedia.org/wiki/GitHub#Gist>, <https://help.github.com/articles/about-gists/>). A ‘gist’ is simply one or more files with code/text/images/etc. This package allows the user to create new ‘gists’, update ‘gists’ with new files, rename files, delete files, get and delete ‘gists’, star and ‘un-star’ ‘gists’, fork ‘gists’, open a ‘gist’ in your default browser, get embed code for a ‘gist’, list ‘gist’ ‘commits’, and get rate limit information when ‘authenticated’. Some requests require authentication and some do not. ‘Gists’ website: <https://gist.github.com/>. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
git2r 0.25.2 Interface to the ‘libgit2’ library, which is a pure C implementation of the ‘Git’ core methods. Provides access to ‘Git’ repositories to extract data and running some basic ‘Git’ commands. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
glmnet 2.0_16 Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
glue 1.3.1 An implementation of interpreted string literals, inspired by Python’s Literal String Interpolation <https://www.python.org/dev/peps/pep-0498/> and Docstrings <https://www.python.org/dev/peps/pep-0257/> and Julia’s Triple-Quoted String Literals <https://docs.julialang.org/en/stable/manual/strings/#triple-quoted-string-literals>. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
gmp 0.5_13.5 Multiple Precision Arithmetic (big integers and rationals, prime number tests, matrix computation), arithmetic without limitations using the C library GMP (GNU Multiple Precision Arithmetic). / GPL linux-32, linux-64, osx-64, win-32, win-64
gower 0.2.0 Compute Gower’s distance (or similarity) coefficient between records. Compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP. / GPL-3 linux-32, linux-64, osx-64, win-32, win-64
gplots 3.0.1.1 Various R programming tools for plotting data, including: - calculating and plotting locally smoothed summary function as (‘bandplot’, ‘wapply’), - enhanced versions of standard plots (‘barplot2’, ‘boxplot2’, ‘heatmap.2’, ‘smartlegend’), - manipulating colors (‘col2hex’, ‘colorpanel’, ‘redgreen’, ‘greenred’, ‘bluered’, ‘redblue’, ‘rich.colors’), - calculating and plotting two-dimensional data summaries (‘ci2d’, ‘hist2d’), - enhanced regression diagnostic plots (‘lmplot2’, ‘residplot’), - formula-enabled interface to ‘stats::lowess’ function (‘lowess’), - displaying textual data in plots (‘textplot’, ‘sinkplot’), - plotting a matrix where each cell contains a dot whose size reflects the relative magnitude of the elements (‘balloonplot’), - plotting Venn diagrams (‘venn’), - displaying Open-Office style plots (‘ooplot’), - plotting multiple data on same region, with separate axes (‘overplot’), - plotting means and confidence intervals (‘plotCI’, ‘plotmeans’), - spacing points in an x-y plot so they don’t overlap (‘space’). / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
gridbase 0.4_7 Integration of base and grid graphics / GPL linux-32, linux-64, noarch, osx-64, win-32, win-64
gridextra 2.3 Provides a number of user-level functions to work with grid graphics, notably to arrange multiple grid-based plots on a page, and draw tables. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
grpreg 3.2_1 Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge. / GPL-3 linux-32, linux-64, osx-64, win-32, win-64
gsl 2.1_6 An R wrapper for some of the functionality of the Gnu Scientific Library. / GPL-3 linux-32, linux-64, osx-64, win-32, win-64
gss 2.1_9 A comprehensive package for structural multivariate function estimation using smoothing splines. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
gsw 1.0_5 Provides an interface to the Gibbs ‘SeaWater’ (‘TEOS-10’) C library, version 3.05-4 (commit ‘5b4d959e54031f9e972f3e863f63e67fa4f5bfec’, dated 2017-08-07, available at <https://github.com/TEOS-10/GSW-C>, which stems from ‘Matlab’ and other code written by members of Working Group 127 of ‘SCOR’/’IAPSO’ (Scientific Committee on Oceanic Research / International Association for the Physical Sciences of the Oceans). / GPL (>= 2) | file LICENSE linux-32, linux-64, osx-64, win-32, win-64
gtable 0.3.0 Tools to make it easier to work with tables of ‘grobs’. The ‘gtable’ package defines a ‘gtable’ grob class that specifies a grid along with a list of grobs and their placement in the grid. Further the package makes it easy to manipulate and combine ‘gtable’ objects so that complex compositions can be build up sequentially. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
gtools 3.8.1 Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages (‘ask’, ‘checkRVersion’, ‘getDependencies’, ‘keywords’, ‘scat’), - calculate the logit and inverse logit transformations (‘logit’, ‘inv.logit’), - test if a value is missing, empty or contains only NA and NULL values (‘invalid’), - manipulate R’s .Last function (‘addLast’), - define macros (‘defmacro’), - detect odd and even integers (‘odd’, ‘even’), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII (‘ASCIIfy’), - perform a binary search (‘binsearch’), - sort strings containing both numeric and character components (‘mixedsort’), - create a factor variable from the quantiles of a continuous variable (‘quantcut’), - enumerate permutations and combinations (‘combinations’, ‘permutation’), - calculate and convert between fold-change and log-ratio (‘foldchange’, ‘logratio2foldchange’, ‘foldchange2logratio’), - calculate probabilities and generate random numbers from Dirichlet distributions (‘rdirichlet’, ‘ddirichlet’), - apply a function over adjacent subsets of a vector (‘running’), - modify the TCP_NODELAY (‘de-Nagle’) flag for socket objects, - efficient ‘rbind’ of data frames, even if the column names don’t match (‘smartbind’), - generate significance stars from p-values (‘stars.pval’), - convert characters to/from ASCII codes (‘asc’, ‘chr’), - convert character vector to ASCII representation (‘ASCIIfy’). / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
guide 1.2.7 A nice GUI for financial DErivatives in R. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
H
Name Version Summary/License Platforms
haven 2.1.0 Import foreign statistical formats into R via the embedded ‘ReadStat’ C library, <https://github.com/WizardMac/ReadStat>. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
hexbin 1.27.2 Binning and plotting functions for hexagonal bins. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
highcharter 0.7.0 A wrapper for the ‘Highcharts’ library including shortcut functions to plot R objects. ‘Highcharts’ <http://www.highcharts.com/> is a charting library offering numerous chart types with a simple configuration syntax. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
highr 0.8 Provides syntax highlighting for R source code. Currently it supports LaTeX and HTML output. Source code of other languages is supported via Andre Simon’s highlight package (<http://www.andre-simon.de>). / GPL linux-32, linux-64, noarch, osx-64, win-32, win-64
hmisc 4.2_0 Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, and recoding variables. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
hms 0.4.2 Implements an S3 class for storing and formatting time-of-day values, based on the ‘difftime’ class. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
htmltable 1.13.1 Tables with state-of-the-art layout elements such as row spanners, column spanners, table spanners, zebra striping, and more. While allowing advanced layout, the underlying css-structure is simple in order to maximize compatibility with word processors such as ‘MS Word’ or ‘LibreOffice’. The package also contains a few text formatting functions that help outputting text compatible with HTML/LaTeX. / GPL (>= 3) linux-32, linux-64, noarch, osx-64, win-32, win-64
htmltools 0.3.6 Tools for HTML generation and output. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
htmlwidgets 1.3 A framework for creating HTML widgets that render in various contexts including the R console, ‘R Markdown’ documents, and ‘Shiny’ web applications. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
httpuv 1.5.1 Provides low-level socket and protocol support for handling HTTP and WebSocket requests directly from within R. It is primarily intended as a building block for other packages, rather than making it particularly easy to create complete web applications using httpuv alone. httpuv is built on top of the libuv and http-parser C libraries, both of which were developed by Joyent, Inc. (See LICENSE file for libuv and http-parser license information.) / GPL (>= 2) | file LICENSE linux-32, linux-64, osx-64, win-32, win-64
httr 1.4.0 Useful tools for working with HTTP organised by HTTP verbs (GET(), POST(), etc). Configuration functions make it easy to control additional request components (authenticate(), add_headers() and so on). / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
hunspell 3.0 Low level spell checker and morphological analyzer based on the famous ‘hunspell’ library <https://hunspell.github.io>. The package can analyze or check individual words as well as parse text, latex, html or xml documents. For a more user-friendly interface use the ‘spelling’ package which builds on this package to automate checking of files, documentation and vignettes in all common formats. / GPL-2 | LGPL-2.1 | MPL-1.1 linux-32, linux-64, osx-64, win-32, win-64
I
Name Version Summary/License Platforms
igraph 1.2.4.1 Routines for simple graphs and network analysis. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
implyr 0.2.4 ‘SQL’ back-end to ‘dplyr’ for Apache Impala, the massively parallel processing query engine for Apache ‘Hadoop’. Impala enables low-latency ‘SQL’ queries on data stored in the ‘Hadoop’ Distributed File System ‘(HDFS)’, Apache ‘HBase’, Apache ‘Kudu’, Amazon Simple Storage Service ‘(S3)’, Microsoft Azure Data Lake Store ‘(ADLS)’, and Dell ‘EMC’ ‘Isilon’. See <https://impala.apache.org> for more information about Impala. / Apache License 2.0 | file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
influencer 0.1.0 Provides functionality to compute various node centrality measures on networks. Included are functions to compute betweenness centrality (by utilizing Madduri and Bader’s SNAP library), implementations of Burt’s constraint and effective network size (ENS) metrics, Borgatti’s algorithm to identify key players, and Valente’s bridging metric. On Unix systems, the betweenness, Key Players, and bridging implementations are parallelized with OpenMP, which may run faster on systems which have OpenMP configured. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
ini 0.3.1 Parse simple ‘.ini’ configuration files to an structured list. Users can manipulate this resulting list with lapply() functions. This same structured list can be used to write back to file after modifications. / GPL-3 noarch
inline 0.3.15 Functionality to dynamically define R functions and S4 methods with ‘inlined’ C, C or Fortran code supporting the .C and .Call calling conventions. / LGPL linux-32, linux-64, noarch, osx-64, win-32, win-64
ipred 0.9_8 Improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
irdisplay 0.7.0 An interface to the rich display capabilities of ‘Jupyter’ front-ends (e.g. ‘Jupyter Notebook’) <https://jupyter.org>. Designed to be used from a running ‘IRkernel’ session <https://irkernel.github.io>. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
irkernel 0.8.15 The R kernel for the ‘Jupyter’ environment executes R code which the front-end (‘Jupyter Notebook’ or other front-ends) submits to the kernel via the network. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
irlba 2.3.3 Fast and memory efficient methods for truncated singular value decomposition and principal components analysis of large sparse and dense matrices. / GPL-3 linux-32, linux-64, osx-64, win-32, win-64
isocodes 2019.04.22 ISO language, territory, currency, script and character codes. Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217 currency codes, ISO 15924 script codes, and the ISO 8859 character codes as well as the UN M.49 area codes. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
iterators 1.0.10 Support for iterators, which allow a programmer to traverse through all the elements of a vector, list, or other collection of data. / Apache License (== 2.0) linux-32, linux-64, noarch, osx-64, win-32, win-64
J
Name Version Summary/License Platforms
janeaustenr 0.1.5 Full texts for Jane Austen’s 6 completed novels, ready for text analysis. These novels are Sense and Sensibility, Pride and Prejudice, Mansfield Park, Emma, Northanger Abbey, and Persuasion. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
jpeg 0.1_8 This package provides an easy and simple way to read, write and display bitmap images stored in the JPEG format. It can read and write both files and in-memory raw vectors. / GPL-2 | GPL-3 linux-32, linux-64, osx-64, win-32, win-64
jsonlite 1.6 A fast JSON parser and generator optimized for statistical data and the web. Started out as a fork of ‘RJSONIO’, but has been completely rewritten in recent versions. The package offers flexible, robust, high performance tools for working with JSON in R and is particularly powerful for building pipelines and interacting with a web API. The implementation is based on the mapping described in the vignette (Ooms, 2014). In addition to converting JSON data from/to R objects, ‘jsonlite’ contains functions to stream, validate, and prettify JSON data. The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
K
Name Version Summary/License Platforms
keras 2.2.4.1 Interface to ‘Keras’ <https://keras.io>, a high-level neural networks ‘API’. ‘Keras’ was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both ‘CPU’ and ‘GPU’ devices. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-64
kernlab 0.9_27 Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods ‘kernlab’ includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
kernsmooth 2.23_15 Functions for kernel smoothing (and density estimation) corresponding to the book: Wand, M.P. and Jones, M.C. (1995) Kernel Smoothing. / Unlimited linux-32, linux-64, osx-64, win-32, win-64
knitr 1.22 Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques. / GPL linux-32, linux-64, noarch, osx-64, win-32, win-64
kohonen 3.0.8 Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
L
Name Version Summary/License Platforms
labeling 0.3 Provides a range of axis labeling algorithms / MIT file LICENSE | Unlimited linux-32, linux-64, noarch, osx-64, win-32, win-64
lahman 6.0_0 Provides the tables from the ‘Sean Lahman Baseball Database’ as a set of R data.frames. It uses the data on pitching, hitting and fielding performance and other tables from 1871 through 2015, as recorded in the 2016 version of the database. / GPL linux-32, linux-64, noarch, osx-64, win-32, win-64
lars 1.2 Efficient procedures for fitting an entire lasso sequence with the cost of a single least squares fit. Least angle regression and infinitesimal forward stagewise regression are related to the lasso, as described in the paper below. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
later 0.8.0 Executes arbitrary R or C functions some time after the current time, after the R execution stack has emptied. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
lattice 0.20_38 A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
latticeextra 0.6_28 Building on the infrastructure provided by the lattice package, this package provides several new high-level functions and methods, as well as additional utilities such as panel and axis annotation functions. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
lava 1.6.5 A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2019) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
lavaan 0.6_3 Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models. / GPL (>= 2) linux-64, noarch, osx-64, win-32, win-64
lazyeval 0.2.2 An alternative approach to non-standard evaluation using formulas. Provides a full implementation of LISP style ‘quasiquotation’, making it easier to generate code with other code. / GPL-3 linux-32, linux-64, osx-64, win-32, win-64
leaflet 2.0.2 Create and customize interactive maps using the ‘Leaflet’ JavaScript library and the ‘htmlwidgets’ package. These maps can be used directly from the R console, from ‘RStudio’, in Shiny applications and R Markdown documents. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
leaps 3.0 Regression subset selection, including exhaustive search. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
libcoin 1.0_4 Basic infrastructure for linear test statistics and permutation inference in the framework of Strasser and Weber (1999) <http://epub.wu.ac.at/102/>. This package must not be used by end-users. CRAN package ‘coin’ implements all user interfaces and is ready to be used by anyone. / GPL-2 linux-64, osx-64, win-32, win-64
lintr 1.0.3 Checks adherence to a given style, syntax errors and possible semantic issues. Supports on the fly checking of R code edited with ‘RStudio IDE’, ‘Emacs’, ‘Vim’, ‘Sublime Text’ and ‘Atom’. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
lme4 1.1_21 Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the ‘Eigen’ C library for numerical linear algebra and ‘RcppEigen’ glue. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
lmertest 3.1_0 Provides p-values in type I, II or III anova and summary tables for lmer model fits (cf. lme4) via Satterthwaite’s degrees of freedom method. A Kenward-Roger method is also available via the pbkrtest package. Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
lmtest 0.9_36 A collection of tests, data sets, and examples for diagnostic checking in linear regression models. Furthermore, some generic tools for inference in parametric models are provided. / GPL-2 | GPL-3 linux-32, linux-64, osx-64, win-32, win-64
logging 0.9_107 Pure R implementation of the ubiquitous log4j package. It offers hierarchic loggers, multiple handlers per logger, level based filtering, space handling in messages and custom formatting. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
loo 2.1.0 Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions. / GPL (>= 3) noarch
lpsolve 5.6.13 Lp_solve is freely available (under LGPL 2) software for solving linear, integer and mixed integer programs. In this implementation we supply a wrapper function in C and some R functions that solve general linear/integer problems, assignment problems, and transportation problems. This version calls lp_solve version 5.5. / LGPL-2 linux-64, osx-64, win-32, win-64
lsmeans 2.30_0 Obtain least-squares means for linear, generalized linear, and mixed models. Compute contrasts or linear functions of least-squares means, and comparisons of slopes. Plots and compact letter displays. Least-squares means were proposed in Harvey, W (1960) Least-squares analysis of data with unequal subclass numbers, Tech Report ARS-20-8, USDA National Agricultural Library, and discussed further in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>. NOTE: lsmeans now relies primarily on code in the ‘emmeans’ package. ‘lsmeans’ will be archived in the near future. / GPL-2 | GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
lubridate 1.7.4 Functions to work with date-times and time-spans: fast and user friendly parsing of date-time data, extraction and updating of components of a date-time (years, months, days, hours, minutes, and seconds), algebraic manipulation on date-time and time-span objects. The ‘lubridate’ package has a consistent and memorable syntax that makes working with dates easy and fun. Parts of the ‘CCTZ’ source code, released under the Apache 2.0 License, are included in this package. See <https://github.com/google/cctz> for more details. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
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Name Version Summary/License Platforms
magic 1.5_9 A collection of efficient, vectorized algorithms for the creation and investigation of magic squares and hypercubes, including a variety of functions for the manipulation and analysis of arbitrarily dimensioned arrays. The package includes methods for creating normal magic squares of any order greater than 2. The ultimate intention is for the package to be a computerized embodiment all magic square knowledge, including direct numerical verification of properties of magic squares (such as recent results on the determinant of odd-ordered semimagic squares). Some antimagic functionality is included. The package also serves as a rebuttal to the often-heard comment I thought R was just for statistics. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
magrittr 1.5 Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions. For more information, see package vignette. To quote Rene Magritte, Ceci n’est pas un pipe. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
manipulate 1.0.1 Interactive plotting functions for use within RStudio. The manipulate function accepts a plotting expression and a set of controls (e.g. slider, picker, checkbox, or button) which are used to dynamically change values within the expression. When a value is changed using its corresponding control the expression is automatically re-executed and the plot is redrawn. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
manipulatewidget 0.10.0 Like package ‘manipulate’ does for static graphics, this package helps to easily add controls like sliders, pickers, checkboxes, etc. that can be used to modify the input data or the parameters of an interactive chart created with package ‘htmlwidgets’. / GPL (>= 2) | file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
mapproj 1.2.6 Converts latitude/longitude into projected coordinates. / Lucent Public License linux-32, linux-64, osx-64, win-32, win-64
maps 3.3.0 Display of maps. Projection code and larger maps are in separate packages (‘mapproj’ and ‘mapdata’). / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
maptools 0.9_5 Set of tools for manipulating geographic data. It includes binary access to ‘GSHHG’ shoreline files. The package also provides interface wrappers for exchanging spatial objects with packages such as ‘PBSmapping’, ‘spatstat’, ‘maps’, ‘RArcInfo’, and others. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
markdown 0.9 Provides R bindings to the ‘Sundown’ ‘Markdown’ rendering library (<https://github.com/vmg/sundown>). ‘Markdown’ is a plain-text formatting syntax that can be converted to ‘XHTML’ or other formats. See <http://en.wikipedia.org/wiki/Markdown> for more information about ‘Markdown’. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
mass 7.3_51.3 Functions and datasets to support Venables and Ripley, Modern Applied Statistics with S (4th edition, 2002). / GPL-2 | GPL-3 linux-32, linux-64, osx-64, win-32, win-64
matrix 1.2_17 A rich hierarchy of matrix classes, including triangular, symmetric, and diagonal matrices, both dense and sparse and with pattern, logical and numeric entries. Numerous methods for and operations on these matrices, using ‘LAPACK’ and ‘SuiteSparse’ libraries. / GPL (>= 2) | file LICENCE linux-32, linux-64, osx-64, win-32, win-64
matrixcalc 1.0_3 A collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics. This package is used for teaching and research purposes at the Department of Finance and Risk Engineering, New York University, Polytechnic Institute, Brooklyn, NY 11201. / GPL (>= 2) linux-64, win-64
matrixmodels 0.4_1 Modelling with sparse and dense ‘Matrix’ matrices, using modular prediction and response module classes. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
matrixstats 0.54.0 High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian(). / Artistic-2.0 linux-64, osx-64, win-32, win-64
maxlik 1.3_4 Functions for Maximum Likelihood (ML) estimation and non-linear optimization, and related tools. It includes a unified way to call different optimizers, and classes and methods to handle the results from the ML viewpoint. It also includes a number of convenience tools for testing and developing your own models. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
memoise 1.1.0 Cache the results of a function so that when you call it again with the same arguments it returns the pre-computed value. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
mgcv 1.8_28 Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, ‘JAGS’ support and distributions beyond the exponential family. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
microbenchmark 1.4_6 Provides infrastructure to accurately measure and compare the execution time of R expressions. / BSD_2_clause file LICENSE linux-32, linux-64, osx-64, win-32, win-64
microsoftr 3.5.0.108 Umbrella package with licenses and notices for all Microsoft R packages / file LICENSE linux-64, osx-64, win-64
mime 0.6 Guesses the MIME type from a filename extension using the data derived from /etc/mime.types in UNIX-type systems. / GPL linux-32, linux-64, osx-64, win-32, win-64
miniui 0.1.1.1 Provides UI widget and layout functions for writing Shiny apps that work well on small screens. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
minqa 1.2.4 Derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
misctools 0.6_22 Miscellaneous small tools and utilities. Many of them facilitate the work with matrices, e.g. inserting rows or columns, creating symmetric matrices, or checking for semidefiniteness. Other tools facilitate the work with regression models, e.g. extracting the standard errors, obtaining the number of (estimated) parameters, or calculating R-squared values. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
mlmrev 1.0_7 Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
mnormt 1.5_5 Functions are provided for computing the density and the distribution function of multivariate normal and t random variables, and for generating random vectors sampled from these distributions. Probabilities are computed via non-Monte Carlo methods; different routines are used in the case d=1, d=2, d>2, if d denotes the number of dimensions. / GPL-2 | GPL-3 linux-32, linux-64, osx-64, win-32, win-64
modelmetrics 1.2.2 Collection of metrics for evaluating models written in C using ‘Rcpp’. Popular metrics include area under the curve, log loss, root mean square error, etc. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
modelr 0.1.4 Functions for modelling that help you seamlessly integrate modelling into a pipeline of data manipulation and visualisation. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
modeltools 0.2_22 A collection of tools to deal with statistical models. The functionality is experimental and the user interface is likely to change in the future. The documentation is rather terse, but packages `coin’ and `party’ have some working examples. However, if you find the implemented ideas interesting we would be very interested in a discussion of this proposal. Contributions are more than welcome! / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
mongolite 2.0.1 High-performance MongoDB client based on ‘mongo-c-driver’ and ‘jsonlite’. Includes support for aggregation, indexing, map-reduce, streaming, encryption, enterprise authentication, and GridFS. The online user manual provides an overview of the available methods in the package: <https://jeroen.github.io/mongolite/>. / Apache License 2.0 linux-32, linux-64, osx-64, win-32, win-64
mrclient 3.4.3 A free, community-supported, data science tool for high performance analytics / Proprietary linux-64, win-64
mrclient-mlm 3.4.3 Pre-trained machine learning models for sentiment analysis and image detection / Proprietary noarch
mrclient-mml 3.4.3 Provides state-of-the-art fast, scalable machine learning algorithms and transforms for R / Proprietary linux-64, win-64
mro 0.1.1 Computes multiple correlation coefficient when the data matrix is given and tests its significance. / GPL-2 linux-64, win-64
mro-base 3.5.1 R is a free software environment for statistical computing and graphics. / GPL-2 | GPL-3 linux-64, osx-64, win-64
mro-base_impl 3.5.1 R is a free software environment for statistical computing and graphics. / GPL-2 | GPL-3 linux-64, win-64
mro-basics 3.5.1 None / None linux-64, osx-64, win-64
multcomp 1.4_10 Simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models. The package includes demos reproducing analyzes presented in the book Multiple Comparisons Using R (Bretz, Hothorn, Westfall, 2010, CRC Press). / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
munsell 0.5.0 Provides easy access to, and manipulation of, the Munsell colours. Provides a mapping between Munsell’s original notation (e.g. 5R 5/10) and hexadecimal strings suitable for use directly in R graphics. Also provides utilities to explore slices through the Munsell colour tree, to transform Munsell colours and display colour palettes. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
mvtnorm 1.0_10 Computes multivariate normal and t probabilities, quantiles, random deviates and densities. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
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Name Version Summary/License Platforms
networkd3 0.4 Creates ‘D3’ ‘JavaScript’ network, tree, dendrogram, and Sankey graphs from ‘R’. / GPL (>= 3) linux-32, linux-64, noarch, osx-64, win-32, win-64
nlme 3.1_139 Fit and compare Gaussian linear and nonlinear mixed-effects models. / GPL (>= 2) | file LICENCE linux-32, linux-64, osx-64, win-32, win-64
nlopt-static 2.4.2 nonlinear optimization library / GNU Lesser General Public License (LGPL) linux-32, linux-64, osx-64
nloptr 1.2.1 Solve optimization problems using an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. See <http://ab-initio.mit.edu/wiki/index.php/NLopt_Introduction> for more information on the available algorithms. During installation of nloptr on Unix-based systems, the installer checks whether the NLopt library is installed on the system. If the NLopt library cannot be found, the code is compiled using the NLopt source included in the nloptr package. / LGPL-3 linux-32, linux-64, osx-64, win-32, win-64
nlp 0.2_0 Basic classes and methods for Natural Language Processing. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
nmf 0.21.0 Provides a framework to perform Non-negative Matrix Factorization (NMF). The package implements a set of already published algorithms and seeding methods, and provides a framework to test, develop and plug new/custom algorithms. Most of the built-in algorithms have been optimized in C, and the main interface function provides an easy way of performing parallel computations on multicore machines. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
nnet 7.3_12 Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models. / GPL-2 | GPL-3 linux-32, linux-64, osx-64, win-32, win-64
numderiv 2016.8_1 Methods for calculating (usually) accurate numerical first and second order derivatives. Accurate calculations are done using ‘Richardson’’s‘ extrapolation or, when applicable, a complex step derivative is available. A simple difference method is also provided. Simple difference is (usually) less accurate but is much quicker than ‘Richardson’’s‘ extrapolation and provides a useful cross-check. Methods are provided for real scalar and vector valued functions. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
nycflights13 1.0.0 Airline on-time data for all flights departing NYC in 2013. Also includes useful ‘metadata’ on airlines, airports, weather, and planes. / CC0 linux-32, linux-64, noarch, osx-64, win-32, win-64
O
Name Version Summary/License Platforms
oce 1.0_1 Supports the analysis of Oceanographic data, including ‘ADCP’ measurements, measurements made with ‘argo’ floats, ‘CTD’ measurements, sectional data, sea-level time series, coastline and topographic data, etc. Provides specialized functions for calculating seawater properties such as potential temperature in either the ‘UNESCO’ or ‘TEOS-10’ equation of state. Produces graphical displays that conform to the conventions of the Oceanographic literature. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
odbc 1.1.6 A DBI-compatible interface to ODBC databases. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
openssl 1.3 Bindings to OpenSSL libssl and libcrypto, plus custom SSH key parsers. Supports RSA, DSA and EC curves P-256, P-384 and P-521. Cryptographic signatures can either be created and verified manually or via x509 certificates. AES can be used in cbc, ctr or gcm mode for symmetric encryption; RSA for asymmetric (public key) encryption or EC for Diffie Hellman. High-level envelope functions combine RSA and AES for encrypting arbitrary sized data. Other utilities include key generators, hash functions (md5, sha1, sha256, etc), base64 encoder, a secure random number generator, and ‘bignum’ math methods for manually performing crypto calculations on large multibyte integers. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
openxlsx 4.1.0 Simplifies the creation of Excel .xlsx files by providing a high level interface to writing, styling and editing worksheets. Through the use of ‘Rcpp’, read/write times are comparable to the ‘xlsx’ and ‘XLConnect’ packages with the added benefit of removing the dependency on Java. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
P
Name Version Summary/License Platforms
packrat 0.5.0 Manage the R packages your project depends on in an isolated, portable, and reproducible way. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
pbdzmq 0.3_3 ‘ZeroMQ’ is a well-known library for high-performance asynchronous messaging in scalable, distributed applications. This package provides high level R wrapper functions to easily utilize ‘ZeroMQ’. We mainly focus on interactive client/server programming frameworks. For convenience, a minimal ‘ZeroMQ’ library (4.2.2) is shipped with ‘pbdZMQ’, which can be used if no system installation of ‘ZeroMQ’ is available. A few wrapper functions compatible with ‘rzmq’ are also provided. / GPL-3 linux-32, linux-64, osx-64, win-32, win-64
pbivnorm 0.6.0 Provides a vectorized R function for calculating probabilities from a standard bivariate normal CDF. / GPL (>= 2) linux-64, osx-64, win-32, win-64
pbkrtest 0.4_7 Test in mixed effects models. Attention is on mixed effects models as implemented in the ‘lme4’ package. This package implements a parametric bootstrap test and a Kenward Roger modification of F-tests for linear mixed effects models and a parametric bootstrap test for generalized linear mixed models. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
pcapp 1.9_73 Provides functions for robust PCA by projection pursuit. The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>, Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>. / GPL (>= 3) linux-32, linux-64, osx-64, win-32, win-64
pdftools 2.2 Utilities based on ‘libpoppler’ for extracting text, fonts, attachments and metadata from a PDF file. Also supports high quality rendering of PDF documents into PNG, JPEG, TIFF format, or into raw bitmap vectors for further processing in R. / MIT file LICENSE linux-64, osx-64, win-32, win-64
perm 1.0_0.0 Perform Exact or Asymptotic permutation tests / GPL linux-32, linux-64, noarch, osx-64, win-32, win-64
pillar 1.3.1 Provides a ‘pillar’ generic designed for formatting columns of data using the full range of colours provided by modern terminals. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
pkgbuild 1.0.3 Provides functions used to build R packages. Locates compilers needed to build R packages on various platforms and ensures the PATH is configured appropriately so R can use them. / GPL-3 linux-64, noarch, osx-64, win-32, win-64
pkgconfig 2.0.2 Set configuration options on a per-package basis. Options set by a given package only apply to that package, other packages are unaffected. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
pkgload 1.0.2 Simulates the process of installing a package and then attaching it. This is a key part of the ‘devtools’ package as it allows you to rapidly iterate while developing a package. / GPL-3 linux-64, osx-64, win-32, win-64
pkgmaker 0.27 Provides some low-level utilities to use for package development. It currently provides managers for multiple package specific options and registries, vignette, unit test and bibtex related utilities. It serves as a base package for packages like NMF, RcppOctave, doRNG, and as an incubator package for other general purposes utilities, that will eventually be packaged separately. It is still under heavy development and changes in the interface(s) are more than likely to happen. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
pki 0.1_5.1 PKI functions such as verifying certificates, RSA encription and signing which can be used to build PKI infrastructure and perform cryptographic tasks. / GPL-2 | GPL-3 | file LICENSE linux-32, linux-64, osx-64, win-32, win-64
plm 1.7_0 A set of estimators and tests for panel data econometrics. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
plogr 0.2.0 A simple header-only logging library for C. Add ‘LinkingTo: plogr’ to ‘DESCRIPTION’, and ‘#include <plogr.h>’ in your C modules to use it. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
plotly 4.9.0 Create interactive web graphics from ‘ggplot2’ graphs and/or a custom interface to the (MIT-licensed) JavaScript library ‘plotly.js’ inspired by the grammar of graphics. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
pls 2.7_1 Multivariate regression methods Partial Least Squares Regression (PLSR), Principal Component Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS). / GPL-2 linux-64, noarch, osx-64, win-32, win-64
plumber 0.4.6 Gives the ability to automatically generate and serve an HTTP API from R functions using the annotations in the R documentation around your functions. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
plyr 1.8.4 A set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to fit a model to each spatial location or time point in your study, summarise data by panels or collapse high-dimensional arrays to simpler summary statistics. The development of ‘plyr’ has been generously supported by ‘Becton Dickinson’. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
png 0.1_7 This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors. / GPL-2 | GPL-3 linux-32, linux-64, osx-64, win-32, win-64
polspline 1.1.14 Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
pool 0.1.4.2 Enables the creation of object pools, which make it less computationally expensive to fetch a new object. Currently the only supported pooled objects are ‘DBI’ connections. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
praise 1.0.0 Build friendly R packages that praise their users if they have done something good, or they just need it to feel better. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
prettyunits 1.0.2 Pretty, human readable formatting of quantities. Time intervals: 1337000 -> 15d 11h 23m 20s. Vague time intervals: 2674000 -> about a month ago. Bytes: 1337 -> 1.34 kB. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
proc 1.14.0 Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves. / GPL (>= 3) linux-32, linux-64, osx-64, win-32, win-64
processx 3.3.0 Tools to run system processes in the background. It can check if a background process is running; wait on a background process to finish; get the exit status of finished processes; kill background processes. It can read the standard output and error of the processes, using non-blocking connections. ‘processx’ can poll a process for standard output or error, with a timeout. It can also poll several processes at once. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
prodlim 2018.04.18 Fast and user friendly implementation of nonparametric estimators for censored event history (survival) analysis. Kaplan-Meier and Aalen-Johansen method. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
profilemodel 0.6.0 Provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of arbitrary inference functions for arbitrary ‘glm’-like fitted models with linear predictors. More information on the methods that are implemented can be found in Kosmidis (2008) <https://www.r-project.org/doc/Rnews/Rnews_2008-2.pdf>. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
profvis 0.3.5 Interactive visualizations for profiling R code. / GPL-3 | file LICENSE linux-32, linux-64, osx-64, win-32, win-64
progress 1.2.0 Configurable Progress bars, they may include percentage, elapsed time, and/or the estimated completion time. They work in terminals, in ‘Emacs’ ‘ESS’, ‘RStudio’, ‘Windows’ ‘Rgui’ and the ‘macOS’ ‘R.app’. The package also provides a ‘C’ ‘API’, that works with or without ‘Rcpp’. / MIT file LICENSE noarch
promises 1.0.1 Provides fundamental abstractions for doing asynchronous programming in R using promises. Asynchronous programming is useful for allowing a single R process to orchestrate multiple tasks in the background while also attending to something else. Semantics are similar to ‘JavaScript’ promises, but with a syntax that is idiomatic R. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
proto 1.0.0 An object oriented system using object-based, also called prototype-based, rather than class-based object oriented ideas. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
pryr 0.1.4 Useful tools to pry back the covers of R and understand the language at a deeper level. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
ps 1.3.0 List, query and manipulate all system processes, on ‘Windows’, ‘Linux’ and ‘macOS’. / BSD_3_clause file LICENSE linux-64, osx-64, win-32, win-64
pspline 1.0_18 Smoothing splines with penalties on order m derivatives. / Unlimited linux-32, linux-64, osx-64, win-32, win-64
psych 1.8.12 A general purpose toolbox for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Functions for simulating and testing particular item and test structures are included. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r> web page. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
purrr 0.3.2 A complete and consistent functional programming toolkit for R. / GPL-3 | file LICENSE linux-32, linux-64, osx-64, win-32, win-64
Q
Name Version Summary/License Platforms
qpdf 1.1 Content-preserving transformations transformations of PDF files such as split, combine, and compress. This package interfaces directly to the ‘qpdf’ C API and does not require any command line utilities. Note that ‘qpdf’ does not read actual content from PDF files: to extract text and data you need the ‘pdftools’ package. / Apache License 2.0 linux-64, osx-64, win-32, win-64
qrm 0.4_13 Accompanying package to the book Quantitative Risk Management: Concepts, Techniques and Tools by Alexander J. McNeil, Rüdiger Frey, and Paul Embrechts. / GPL (>= 2) linux-64, osx-64, win-32, win-64
quadprog 1.5_5 This package contains routines and documentation for solving quadratic programming problems. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
quantmod 0.4_14 Specify, build, trade, and analyse quantitative financial trading strategies. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
quantreg 5.38 Estimation and inference methods for models of conditional quantiles: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also included. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
qvcalc 0.9_1 Functions to compute quasi variances and associated measures of approximation error. / GPL-2 | GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
R
Name Version Summary/License Platforms
r 3.6.0 R is a free software environment for statistical computing and graphics. / GPL-3.0 linux-32, linux-64, osx-64, win-32, win-64
r-abc.data 1.0 Contains data which are used by functions of the ‘abc’ package. / GPL (>= 3) noarch
r-abc.rap 0.9.0 It aims to identify candidate genes that are “differentially methylated” between cases and controls. It applies Student’s t-test and delta beta analysis to identify candidate genes containing multiple “CpG sites”. / GPL-3 noarch
r-abcoptim 0.15.0 An implementation of Karaboga (2005) Artificial Bee Colony Optimization algorithm <http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf>. This (working) version is a Work-in-progress, which is why it has been implemented using pure R code. This was developed upon the basic version programmed in C and distributed at the algorithm’s official website. / MIT file LICENSE linux-64, osx-64, win-64
r-abcp2 1.2 Tests the goodness of fit of a distribution of offspring to the Normal, Poisson, and Gamma distribution and estimates the proportional paternity of the second male (P2) based on the best fit distribution. / GPL-2 noarch
r-abe 3.0.1 Performs augmented backward elimination and checks the stability of the obtained model. Augmented backward elimination combines significance or information based criteria with the change in estimate to either select the optimal model for prediction purposes or to serve as a tool to obtain a practically sound, highly interpretable model. More details can be found in Dunkler et al. (2014) <doi:10.1371/journal.pone.0113677>. / GPL (>= 2) noarch
r-abf2 0.7_1 Loads ABF2 files containing gap-free data from electrophysiological recordings, as created by Axon Instruments/Molecular Devices software such as pClamp 10. / Artistic-2.0 noarch
r-abind 1.4_5 Combine multidimensional arrays into a single array. This is a generalization of ‘cbind’ and ‘rbind’. Works with vectors, matrices, and higher-dimensional arrays. Also provides functions ‘adrop’, ‘asub’, and ‘afill’ for manipulating, extracting and replacing data in arrays. / LGPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-abnormality 0.1.0 Contains the functions to implement the methodology and considerations laid out by Marks et al. in the manuscript Measuring Abnormality in High Dimensional Spaces: Applications in Biomechanical Gait Analysis. As of 2/27/2018 this paper has been submitted and is under scientific review. Using high-dimensional datasets to measure a subject’s overall level of abnormality as compared to a reference population is often needed in outcomes research. Utilizing applications in instrumented gait analysis, that article demonstrates how using data that is inherently non-independent to measure overall abnormality may bias results. A methodology is introduced to address this bias to accurately measure overall abnormality in high dimensional spaces. While this methodology is in line with previous literature, it differs in two major ways. Advantageously, it can be applied to datasets in which the number of observations is less than the number of features/variables, and it can be abstracted to practically any number of domains or dimensions. After applying the proposed methodology to the original data, the researcher is left with a set of uncorrelated variables (i.e. principal components) with which overall abnormality can be measured without bias. Different considerations are discussed in that article in deciding the appropriate number of principal components to keep and the aggregate distance measure to utilize. / MIT file LICENSE noarch
r-abodoutlier 0.1 Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection. / MIT file LICENSE noarch
r-abps 0.3 An implementation of the Abnormal Blood Profile Score (ABPS, part of the Athlete Biological Passport program of the World Anti-Doping Agency), which combines several blood parameters into a single score in order to detect blood doping (Sottas et al. (2006) <doi:10.2202/1557-4679.1011>). The package also contains functions to calculate other scores used in anti-doping programs, such as the OFF-score (Gore et al. (2003) <http://www.haematologica.org/content/88/3/333>), as well as example data. / GPL (>= 2) noarch
r-ac3net 1.2.2 Infers directional conservative causal core (gene) networks. It is an advanced version of the algorithm C3NET by providing directional network. Gokmen Altay (2018) <doi:10.1101/271031>, bioRxiv. / GPL (>= 3) noarch
r-aca 1.1 Offers an interactive function for the detection of breakpoints in series. / GPL noarch
r-acceptancesampling 1.0_6 Provides functionality for creating and evaluating acceptance sampling plans. Sampling plans can be single, double or multiple. / GPL (>= 3) noarch
r-acclma 1.0 The main function is plotLMA(sourcefile,header) that takes a data set and plots the appropriate LMA and ACC graphs. If no sourcefile (a string) was passed, a manual data entry window is opened. The header parameter indicates by TRUE/FALSE (false by default) if the source CSV file has a head row or not. The data set should contain only one independent variable (X) and one dependent varialbe (Y) and can contain a weight for each observation / GPL-2 noarch
r-accrued 1.4.1 Package for visualizing data quality of partially accruing data. / GPL-3 noarch
r-acd 1.5.3 Categorical data analysis with complete or missing responses / GPL (>= 2) noarch
r-ace2fastq 0.6.0 The ACE file format is used in genomics to store contigs from sequencing machines. This tools converts it into FASTQ format. Both formats contain the sequence characters and their corresponding quality information. Unlike the FASTQ file, the ace file stores the quality values numerically. The conversion algorithm uses the standard Sanger formula. The package facilitates insertion into pipelines, and content inspection. / GPL-3 noarch
r-acepack 1.4.1 Two nonparametric methods for multiple regression transform selection are provided. The first, Alternative Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. Estimating Optimal Transformations for Multiple Regression and Correlation. Journal of the American Statistical Association. 80:580-598. <doi:10.1080/01621459.1985.10478157>]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low [see Tibshirani, R.. 1986. Estimating Transformations for Regression via Additivity and Variance Stabilization. Journal of the American Statistical Association. 83:394-405. <doi:10.1080/01621459.1988.10478610>]. A good introduction to these two methods is in chapter 16 of Frank Harrel’s Regression Modeling Strategies in the Springer Series in Statistics. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
r-acet 1.8.0 Twin models that are able to estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines. / GPL (>= 2) linux-64, osx-64, win-64
r-acfmperiod 1.0.0 Non-robust and robust computations of the sample autocovariance (ACOVF) and sample autocorrelation functions (ACF) of univariate and multivariate processes. The methodology consists in reversing the diagonalization procedure involving the periodogram or the cross-periodogram and the Fourier transform vectors, and, thus, obtaining the ACOVF or the ACF as discussed in Fuller (1995) <doi:10.1002/9780470316917>. The robust version is obtained by fitting robust M-regressors to obtain the M-periodogram or M-cross-periodogram as discussed in Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>. / GPL (>= 2) noarch
r-acm4r 1.0 Fragment lengths or molecular weights from pairs of lanes are compared, and a number of matching bands are calculated using the Align-and-Count Method. / GPL noarch
r-acmer 1.1.0 Implementation of estimator ACME, described in Wolpert (2015), ACME: A Partially Periodic Estimator of Avian & Chiropteran Mortality at Wind Turbines (submitted). Unlike most other models, this estimator supports decreasing-hazard Weibull model for persistence; decreasing search proficiency as carcasses age; variable bleed-through at successive searches; and interval mortality estimates. The package provides, based on search data, functions for estimating the mortality inflation factor in Frequentist and Bayesian settings. / MIT file LICENSE noarch
r-acnr 1.0.0 Provides SNP array data from different types of copy-number regions. These regions were identified manually by the authors of the package and may be used to generate realistic data sets with known truth. / LGPL (>= 2.1) noarch
r-acopula 0.9.3 Archimax copulas are mixture of Archimedean and EV copulas. The package provides definitions of several parametric families of generator and dependence function, computes CDF and PDF, estimates parameters, tests for goodness of fit, generates random sample and checks copula properties for custom constructs. In 2-dimensional case explicit formulas for density are used, in the contrary to higher dimensions when all derivatives are linearly approximated. Several non-archimax families (normal, FGM, Plackett) are provided as well. / GPL-2 noarch
r-acp 2.1 Analysis of count data exhibiting autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen (2003). / GPL-2 noarch
r-acrt 1.0.1 Functions for testing affine hypotheses on the regression coefficient vector in regression models with autocorrelated errors. / GPL-2 linux-64, osx-64, win-64
r-acs 2.1.4 Provides a general toolkit for downloading, managing, analyzing, and presenting data from the U.S. Census (<https://www.census.gov/data/developers/data-sets.html>), including SF1 (Decennial short-form), SF3 (Decennial long-form), and the American Community Survey (ACS). Confidence intervals provided with ACS data are converted to standard errors to be bundled with estimates in complex acs objects. Package provides new methods to conduct standard operations on acs objects and present/plot data in statistically appropriate ways. / GPL-3 noarch
r-acss 0.2_5 Main functionality is to provide the algorithmic complexity for short strings, an approximation of the Kolmogorov Complexity of a short string using the coding theorem method (see ?acss). The database containing the complexity is provided in the data only package acss.data, this package provides functions accessing the data such as prob_random returning the posterior probability that a given string was produced by a random process. In addition, two traditional (but problematic) measures of complexity are also provided: entropy and change complexity. / GPL (>= 2) noarch
r-acss.data 1.0 Data only package providing the algorithmic complexity of short strings, computed using the coding theorem method. For a given set of symbols in a string, all possible or a large number of random samples of Turing machines (TM) with a given number of states (e.g., 5) and number of symbols corresponding to the number of symbols in the strings were simulated until they reached a halting state or failed to end. This package contains data on 4.5 million strings from length 1 to 12 simulated on TMs with 2, 4, 5, 6, and 9 symbols. The complexity of the string corresponds to the distribution of the halting states of the TMs. / GPL (>= 2) noarch
r-acswr 1.0 A book designed to meet the requirements of masters students. Tattar, P.N., Suresh, R., and Manjunath, B.G. A Course in Statistics with R, J. Wiley, ISBN 978-1-119-15272-9. / GPL-2 noarch
r-activedriver 1.0.0 A mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalised linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers. Reference: Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Juri Reimand and Gary D Bader. Molecular Systems Biology (2013) 9:637 <doi:10.1038/msb.2012.68>. / GPL (>= 2) noarch
r-activityindex 0.3.6 Read raw accelerometry from ‘GT3X’ data and plain table data to calculate Activity Index from Bai et al. (2016) <doi:10.1371/journal.pone.0160644>. / GPL-3 noarch
r-activpalprocessing 1.0.2 Performs estimation of physical activity and sedentary behavior variables from activPAL (PAL Technologies, Glasgow, Scotland) events files. See <http://paltechnologies.com> for more information on the activPAL. / GPL-2 | GPL-3 noarch
r-ada 2.0_5 Performs discrete, real, and gentle boost under both exponential and logistic loss on a given data set. The package ada provides a straightforward, well-documented, and broad boosting routine for classification, ideally suited for small to moderate-sized data sets. / GPL noarch
r-adagio 0.7.1 The R package ‘adagio’ will provide methods and algorithms for discrete optimization, e.g. knapsack and subset sum procedures, derivative-free Nelder-Mead and Hooke-Jeeves minimization, and some (evolutionary) global optimization functions. / GPL (>= 3) linux-64, osx-64, win-64
r-adaptivesparsity 1.6 Implements Figueiredo EM algorithm for adaptive sparsity (Jeffreys prior) (see Figueiredo, M.A.T.; , Adaptive sparseness for supervised learning, Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.25, no.9, pp. 1150- 1159, Sept. 2003) and Wong algorithm for adaptively sparse gaussian geometric models (see Wong, Eleanor, Suyash Awate, and P. Thomas Fletcher. Adaptive Sparsity in Gaussian Graphical Models. In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 311-319. 2013.) / LGPL (>= 3.0) linux-64, osx-64, win-64
r-adaptivetau 2.2_3 Implements adaptive tau leaping to approximate the trajectory of a continuous-time stochastic process as described by Cao et al. (2007) The Journal of Chemical Physics <doi:10.1063/1.2745299> (aka. the Gillespie stochastic simulation algorithm). This package is based upon work supported by NSF DBI-0906041 and NIH K99-GM104158 to Philip Johnson and NIH R01-AI049334 to Rustom Antia. / GPL (>= 3) linux-64, osx-64, win-64
r-adaptmcmc 1.3 Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) <DOI:10.1007/s11222-011-9269-5> and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate. / GPL (>= 2) noarch
r-adaptmt 1.0.0 Implementation of adaptive p-value thresholding (AdaPT), including both a framework that allows the user to specify any algorithm to learn local false discovery rate and a pool of convenient functions that implement specific algorithms. See Lei, Lihua and Fithian, William (2016) <arXiv:1609.06035>. / MIT file LICENSE noarch
r-adapttest 1.0 The functions defined in this program serve for implementing adaptive two-stage tests. Currently, four tests are included: Bauer and Koehne (1994), Lehmacher and Wassmer (1999), Vandemeulebroecke (2006), and the horizontal conditional error function. User-defined tests can also be implemented. Reference: Vandemeulebroecke, An investigation of two-stage tests, Statistica Sinica 2006. / GPL (>= 2) noarch
r-adct 0.1.0 Existing adaptive design methods in clinical trials. The package includes power, stopping boundaries (sample size) calculation functions for two-group group sequential designs, adaptive design with coprimary endpoints, biomarker-informed adaptive design, etc. / GPL (>= 2) noarch
r-addhaz 0.5 Functions to fit the binomial and multinomial additive hazard models and to estimate the contribution of diseases/conditions to the disability prevalence, as proposed by Nusselder and Looman (2004) and extended by Yokota et al (2017). / GPL-3 noarch
r-additivitytests 1.1_4 Implementation of the Tukey, Mandel, Johnson-Graybill, LBI, Tusell and modified Tukey non-additivity tests. / GPL-3 noarch
r-addt 2.0 Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the collected data, a thermal index (TI) is estimated. The TI can be useful for material rating and comparison. This package implements the traditional method based on the least-squares method, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline methods, and the corresponding methods for estimating TI for polymeric materials. The traditional approach is a two-step approach that is currently used in industrial standards, while the parametric method is widely used in the statistical literature. The semiparametric method is newly developed. Both the parametric and semiparametric approaches allow one to do statistical inference such as quantifying uncertainties in estimation, hypothesis testing, and predictions. Publicly available datasets are provided illustrations. More details can be found in Jin et al. (2017). / GPL-2 noarch
r-ade4 1.7_13 Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>. / GPL (>= 2) linux-64, osx-64, win-64
r-ade4tkgui 0.2_9 A Tcl/Tk GUI for some basic functions in the ‘ade4’ package. / GPL (>= 2) noarch
r-adegraphics 1.0_15 Graphical functionalities for the representation of multivariate data. It is a complete re-implementation of the functions available in the ‘ade4’ package. / GPL (>= 2) noarch
r-adeptdata 1.0.1 Created to host raw accelerometry data sets and their derivatives which are used in the corresponding ‘adept’ package. / GPL-3 noarch
r-adequacymodel 2.0.0 The main application concerns to a new robust optimization package with two major contributions. The first contribution refers to the assessment of the adequacy of probabilistic models through a combination of several statistics, which measure the relative quality of statistical models for a given data set. The second one provides a general purpose optimization method based on meta-heuristics functions for maximizing or minimizing an arbitrary objective function. / GPL (>= 2) noarch
r-adfexplorer 0.1.4 Amiga Disk Files (ADF) are virtual representations of 3.5 inch floppy disks for the Commodore Amiga. Most disk drives from other systems (including modern drives) are not able to read these disks. To be able to emulate this system, the ADF format was created. This package enables you to read ADF files and import and export files from and to such virtual DOS-formatted disks. / GPL-3 noarch
r-adgoftest 0.3 Anderson-Darling GoF test with p-value calculation based on Marsaglia’s 2004 paper Evaluating the Anderson-Darling Distribution / GPL linux-32, linux-64, noarch, osx-64, win-32, win-64
r-adimpro 0.9.0 Implements tools for manipulation of digital images and the Propagation Separation approach by Polzehl and Spokoiny (2006) <DOI:10.1007/s00440-005-0464-1> for smoothing digital images, see Polzehl and Tabelow (2007) <DOI:10.18637/jss.v019.i01>. / GPL (>= 2) linux-64, osx-64, win-64
r-adjclust 0.5.7 Implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated to a position, and only adjacent clusters can be merged. Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient. This algorithm is described in Chapter 4 of Alia Dehman (2015) <https://hal.archives-ouvertes.fr/tel-01288568v1>. / GPL-3 linux-64, osx-64, win-64
r-adklakedata 0.6.1 Package for the access and distribution of Long-term lake datasets from lakes in the Adirondack Park, northern New York state. Includes a wide variety of physical, chemical, and biological parameters from 28 lakes. Data are from multiple collection organizations and have been harmonized in both time and space for ease of reuse. / MIT file LICENSE noarch
r-admisc 0.3 Contains functions used across packages ‘QCA’, ‘DDIwR’, and ‘venn’. Interprets and translates DNF - Disjunctive Normal Form expressions, for both binary and multi-value crisp sets, and extracts information (set names, set values) from those expressions. Other functions perform various other checks if possibly numeric (even if all numbers reside in a character vector) and coerce to numeric, or check if the numbers are whole. It also offers, among many others, a highly flexible recoding function. / GPL (>= 2) noarch
r-admit 2.1.3 Provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function as described in Ardia et al. (2009) <doi:10.18637/jss.v029.i03>. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the target density itself. / GPL (>= 2) linux-64, osx-64, win-64
r-admmnet 0.1 Fit linear and cox models regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty, and their adaptive forms, such as adaptive lasso and net adjusting for signs of linked coefficients. In addition, it treats the number of non-zero coefficients as another tuning parameter and simultaneously selects with the regularization parameter. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients. / GPL (>= 2) linux-64, osx-64, win-64
r-adoptr 0.2.2 Optimize one or two-arm, two-stage designs for clinical trials with respect to several pre-implemented objective criteria or implement custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz M, Kunzmann K, Herrmann C, Rauch G, Kieser M. A variational approach to optimal two-stage designs. Statistics in Medicine. 2019;1–13. <doi:10.1002/sim.8291> for details. / MIT file LICENSE noarch
r-adpf 0.0.1 This function takes a vector or matrix of data and smooths the data with an improved Savitzky Golay transform. The Savitzky-Golay method for data smoothing and differentiation calculates convolution weights using Gram polynomials that exactly reproduce the results of least-squares polynomial regression. Use of the Savitzky-Golay method requires specification of both filter length and polynomial degree to calculate convolution weights. For maximum smoothing of statistical noise in data, polynomials with low degrees are desirable, while a high polynomial degree is necessary for accurate reproduction of peaks in the data. Extension of the least-squares regression formalism with statistical testing of additional terms of polynomial degree to a heuristically chosen minimum for each data window leads to an adaptive-degree polynomial filter (ADPF). Based on noise reduction for data that consist of pure noise and on signal reproduction for data that is purely signal, ADPF performed nearly as well as the optimally chosen fixed-degree Savitzky-Golay filter and outperformed sub-optimally chosen Savitzky-Golay filters. For synthetic data consisting of noise and signal, ADPF outperformed both optimally chosen and sub-optimally chosen fixed-degree Savitzky-Golay filters. See Barak, P. (1995) <doi:10.1021/ac00113a006> for more information. / GPL-3 noarch
r-adpss 0.1.1 Provides the functions for planning and conducting a clinical trial with adaptive sample size determination. Maximal statistical efficiency will be exploited even when dramatic or multiple adaptations are made. Such a trial consists of adaptive determination of sample size at an interim analysis and implementation of frequentist statistical test at the interim and final analysis with a prefixed significance level. The required assumptions for the stage-wise test statistics are independent and stationary increments and normality. Predetermination of adaptation rule is not required. / GPL (>= 2) linux-64, osx-64, win-64
r-advdif4 0.7.18 This software solves an Advection Bi-Flux Diffusive Problem using the Finite Difference Method FDM. Vasconcellos, J.F.V., Marinho, G.M., Zanni, J.H., 2016, Numerical analysis of an anomalous diffusion with a bimodal flux distribution. <doi:10.1016/j.rimni.2016.05.001>. Silva, L.G., Knupp, D.C., Bevilacqua, L., Galeao, A.C.N.R., Silva Neto, A.J., 2014, Formulation and solution of an Inverse Anomalous Diffusion Problem with Stochastic Techniques. <doi:10.5902/2179460X13184>. In this version, it is possible to include a source as a function depending on space and time, that is, s(x,t). / GPL-3 noarch
r-adwordsr 0.3.1 Allows access to selected services that are part of the ‘Google Adwords’ API <https://developers.google.com/adwords/api/docs/guides/start>. ‘Google Adwords’ is an online advertising service by ‘Google’, that delivers Ads to users. This package offers a authentication process using ‘OAUTH2’. Currently, there are two methods of data of accessing the API, depending on the type of request. One method uses ‘SOAP’ requests which require building an ‘XML’ structure and then sent to the API. These are used for the ‘ManagedCustomerService’ and the ‘TargetingIdeaService’. The second method is by building ‘AWQL’ queries for the reporting side of the ‘Google Adwords’ API. / MIT file LICENSE noarch
r-aer 1.2_6 Functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette AER for a package overview.) / GPL-2 | GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-afc 1.4.0 This is an implementation of the Generalized Discrimination Score (also known as Two Alternatives Forced Choice Score, 2AFC) for various representations of forecasts and verifying observations. The Generalized Discrimination Score is a generic forecast verification framework which can be applied to any of the following verification contexts: dichotomous, polychotomous (ordinal and nominal), continuous, probabilistic, and ensemble. A comprehensive description of the Generalized Discrimination Score, including all equations used in this package, is provided by Mason and Weigel (2009) <doi:10.1175/MWR-D-10-05069.1>. / GPL-3 noarch
r-afex 0.23_0 Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software). / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-affluenceindex 1.0 Computes the statistical indices of affluence (richness) and constructs bootstrap confidence intervals for these indices. Also computes the Wolfson polarization index. / GPL-2 noarch
r-afpt 1.0.0 Allows estimation and modelling of flight costs in animal (vertebrate) flight, implementing the aerodynamic power model described in Klein Heerenbrink et al. (2015) <doi:10.1098/rspa.2014.0952>. Taking inspiration from the program ‘Flight’, developed by Colin Pennycuick (Pennycuick (2008) Modelling the flying bird. Amsterdam: Elsevier. ISBN 0-19-857721-4), flight performance is estimated based on basic morphological measurements such as body mass, wingspan and wing area. ‘afpt’ can be used to make predictions on how animals should adjust their flight behaviour and wingbeat kinematics to varying flight conditions. / GPL (>= 3) noarch
r-aggregater 0.0.2 Convenience functions for aggregating data frame. Currently mean, sum and variance are supported. For Date variables, recency and duration are supported. There is also support for dummy variables in predictive contexts. / GPL-2 noarch
r-aggregation 1.0.1 Contains functionality for performing the following methods of p-value aggregation: Fisher’s method [Fisher, RA (1932, ISBN: 9780028447308)], the Lancaster method (weighted Fisher’s method) [Lancaster, HO (1961, <doi:10.1111/j.1467-842X.1961.tb00058.x>)], and Sidak correction [Sidak, Z (1967, <doi:10.1080/01621459.1967.10482935>)]. Please cite Yi et al., the manuscript corresponding to this package [Yi, L et al., (2017), <doi:10.1101/190199>]. / GPL-3 noarch
r-aghmatrix 1.0.2 Computation of A (pedigree), G (genomic-base), and H (A corrected by G) relationship matrices for diploid and autopolyploid species. Several methods are implemented considering additive and non-additive models. / GPL-3 linux-64, osx-64, win-64
r-agop 0.2_2 Tools supporting multi-criteria and group decision making, including variable number of criteria, by means of aggregation operators, spread measures, fuzzy logic connectives, fusion functions, and preordered sets. Possible applications include, but are not limited to, quality management, scientometrics, software engineering, etc. / LGPL (>= 3) linux-64, osx-64, win-64
r-agreementinterval 0.1.1 A tool for calculating agreement interval of two measurement methods (Jason Liao (2015) <DOI:10.1515/ijb-2014-0030>) and present results in plots with discordance rate and/or clinically meaningful limit to quantify agreement quality. / MIT file LICENSE noarch
r-agridat 1.16 Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more. / CC BY-SA 4.0 noarch
r-agrmt 1.40.4 Calculate agreement or consensus in ordered rating scales. The package implements van der Eijk’s (2001) <DOI: 10.1023/A:1010374114305> measure of agreement A, which can be used to describe agreement, consensus, or polarization among respondents. It also implements measures of consensus (dispersion) by Leik, Tatsle and Wierman, Blair and Lacy, Kvalseth, Berry and Mielke, and Garcia-Montalvo and Reynal-Querol. Furthermore, an implementation of Galtungs AJUS-system is provided to classify distributions, as well as a function to identify the position of multiple modes. / GPL-3 noarch
r-agsemisc 1.3_1 High-featured panel functions for bwplot and xyplot, some plot management helpers, various convenience functions / GPL-2 noarch
r-ahaz 1.14 Computationally efficient procedures for regularized estimation with the semiparametric additive hazards regression model. / GPL-2 linux-64, osx-64, win-64
r-ahocorasicktrie 0.1.0 Aho-Corasick is an optimal algorithm for finding many keywords in a text. It can locate all matches in a text in O(NM) time; i.e., the time needed scales linearly with the number of keywords (N) and the size of the text (M). Compare this to the naive approach which takes O(N*M) time to loop through each pattern and scan for it in the text. This implementation builds the trie (the generic name of the data structure) and runs the search in a single function call. If you want to search multiple texts with the same trie, the function will take a list or vector of texts and return a list of matches to each text. By default, all 128 ASCII characters are allowed in both the keywords and the text. A more efficient trie is possible if the alphabet size can be reduced. For example, DNA sequences use at most 19 distinct characters and usually only 4; protein sequences use at most 26 distinct characters and usually only 20. UTF-8 (Unicode) matching is not currently supported. / Apache License 2.0 linux-64, osx-64, win-64
r-aidar 1.0.5 Read objects from the AIDA (<http://aida.freehep.org/>) file and make them available as dataframes in R. / LGPL (>= 2) noarch
r-aim 1.01 R functions for adaptively constructing index models for continuous, binary and survival outcomes. Implementation requires loading R-pacakge survival / LGPL-2 noarch
r-airgr 1.3.2.23 Hydrological modelling tools developed at Irstea-Antony (HYCAR Research Unit, France). The package includes several conceptual rainfall-runoff models (GR4H, GR4J, GR5J, GR6J, GR2M, GR1A), a snow accumulation and melt model (CemaNeige) and the associated functions for their calibration and evaluation. Use help(airGR) for package description and references. / GPL-2 linux-64, osx-64, win-64
r-airthermo 1.2.1 Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics. / GPL-3 linux-64, osx-64, win-64
r-ake 1.0 Continuous and discrete (count or categorical) estimation of density, probability mass function (p.m.f.) and regression functions are performed using associated kernels. The cross-validation technique and the local Bayesian procedure are also implemented for bandwidth selection. / GPL (>= 2) noarch
r-akima 0.6_2 Several cubic spline interpolation methods of H. Akima for irregular and regular gridded data are available through this package, both for the bivariate case (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697). Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas triangulation code which is part of Akimas Fortran code. A bilinear interpolator for regular grids was also added for comparison with the bicubic interpolator on regular grids. / ACM | file LICENSE (Restricts use) linux-64, osx-64, win-64
r-akmeans 1.1 Adaptive K-means algorithm with various threshold settings. It support two distance metric: Euclidean distance, Cosine distance (1 - cosine similarity) In version 1.1, it contains one more threshold condition. / GPL-2 noarch
r-alabama 2015.3_1 Augmented Lagrangian Adaptive Barrier Minimization Algorithm for optimizing smooth nonlinear objective functions with constraints. Linear or nonlinear equality and inequality constraints are allowed. / GPL (>= 2) noarch
r-albopictus 0.5 Implements discrete time deterministic and stochastic age-structured population dynamics models described in Erguler and others (2016) <doi:10.1371/journal.pone.0149282> and Erguler and others (2017) <doi:10.1371/journal.pone.0174293>. / GPL (>= 3) noarch
r-ald 1.2 It provides the density, distribution function, quantile function, random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for the three parameter Asymmetric Laplace Distribution defined in Koenker and Machado (1999). This is a special case of the skewed family of distributions available in Galarza et.al. (2017) <doi:10.1002/sta4.140> useful for quantile regression. / GPL (>= 2) noarch
r-alfr 1.2.1 Allows you to connect to an ‘Alfresco’ content management repository and interact with its contents using simple and intuitive functions. You will be able to establish a connection session to the ‘Alfresco’ repository, read and upload content and manage folder hierarchies. For more details on the ‘Alfresco’ content management repository see <https://www.alfresco.com/ecm-software/document-management>. / GPL-3 | file LICENSE noarch
r-algaeclassify 0.1.0 Functions designed to facilitate the assignment of morpho-functional group (MFG) classifications to phytoplankton species based on a combination of taxonomy (Class,Order) and a suite of 7 binomial functional traits. Classifications can also be made using only a species list and a database of trait-derived classifications included in the package. MFG classifications are derived from Salmaso et al. (2015) <doi:10.1111/fwb.12520> and this reference should be cited when using the package. The ‘algaeClassify’ package is a product of the GEISHA (Global Evaluation of the Impacts of Storms on freshwater Habitat and Structure of phytoplankton Assemblages), funded by CESAB (Centre for Synthesis and Analysis of Biodiversity) and the USGS John Wesley Powell Center, with data and other support provided by members of GLEON (Global Lake Ecology Observation Network). This software is preliminary or provisional and is subject to revision. It is being provided to meet the need for timely best science. The software has not received final approval by the U.S. Geological Survey (USGS). No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. The software is provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized on unauthorized use of the software. / GPL (>= 3) noarch
r-algdesign 1.1_7.3 Algorithmic experimental designs. Calculates exact and approximate theory experimental designs for D,A, and I criteria. Very large designs may be created. Experimental designs may be blocked or blocked designs created from a candidate list, using several criteria. The blocking can be done when whole and within plot factors interact. / GPL (>= 2) linux-64, osx-64, win-64
r-algebraichaplopackage 1.2 Two unordered pairs of data of two different snips positions is haplotyped by resolving a small number ob closed equations. / GPL-2 noarch
r-algorithmia 0.2.0 The company, Algorithmia, houses the largest marketplace of online algorithms. This package essentially holds a bunch of REST wrappers that make it very easy to call algorithms in the Algorithmia platform and access files and directories in the Algorithmia data API. To learn more about the services they offer and the algorithms in the platform visit <http://algorithmia.com>. More information for developers can be found at <http://developers.algorithmia.com>. / MIT file LICENSE noarch
r-aliner 1.1.4 Functions are provided to calculate the ‘ALINE’ Distance between words as per (Kondrak 2000) and (Downey, Hallmark, Cox, Norquest, & Lansing, 2008, <doi:10.1080/09296170802326681>). The score is based on phonetic features represented using the Unicode-compliant International Phonetic Alphabet (IPA). Parameterized features weights are used to determine the optimal alignment and functions are provided to estimate optimum values using a genetic algorithm and supervised learning. See (Downey, Sun, and Norquest 2017, <https://journal.r-project.org/archive/2017/RJ-2017-005/index.html>. / GPL-3 linux-64, osx-64, win-64
r-allan 1.01 Automated fitting of linear regression models and a stepwise routine / GPL-3 noarch
r-allanvar 1.1 A collection of tools for stochastic sensor error characterization using the Allan Variance technique originally developed by D. Allan. / GPL-2 noarch
r-allehap 0.9.9 Tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker, probability and proportion of missing genotypes, recombination rate, etc). Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited from each parent) and thus imputation and reconstruction results can be deterministically calculated. / GPL (>= 2) noarch
r-allelematch 2.5.1 Tools for the identification of unique of multilocus genotypes when both genotyping error and missing data may be present. The package is targeted at those working with large datasets and databases containing multiple samples of each individual, a situation that is common in conservation genetics, and particularly in non-invasive wildlife sampling applications. Functions explicitly incorporate missing data, and can tolerate allele mismatches created by genotyping error. If you use this tool, please cite the package using the journal article in Molecular Ecology Resources (Galpern et al., 2012). Please use citation(‘allelematch’) to call the full citation. For users with access to the associated journal article, tutorial material is also available as supplementary material to the article describing this software, the citation for which can be called using citation(‘allelematch’). / GPL-3 noarch
r-alleleretain 2.0.2 Simulate the effect of management or demography on allele retention and inbreeding accumulation in bottlenecked populations of animals with overlapping generations. / GPL (>= 2) noarch
r-allelic 0.1 This is the implementation in RC of a new association test described in A fast, unbiased and exact allelic exact test for case-control association studies (Submitted). It appears that in most cases the classical chi-square test used for testing for allelic association on genotype data is biased. Our test is unbiased, exact but fast throught careful optimization. / GPL (>= 2) linux-64, osx-64, win-64
r-allpossiblespellings 1.1 Contains functions possSpells.fnc and batch.possSpells.fnc. / GPL-2 noarch
r-alluvial 0.1_2 Creating alluvial diagrams (also known as parallel sets plots) for multivariate and time series-like data. / MIT file LICENSE noarch
r-alpaca 0.3.1 Provides a routine to concentrate out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm proposed by Stammann (2018) <arXiv:1707.01815> and is restricted to glm’s that are based on maximum likelihood estimation and non-linear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides an analytical bias-correction for binary choice models (logit and probit) derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014>. / GPL-3 linux-64, osx-64, win-64
r-alphasimr 0.10.0 The successor to the ‘AlphaSim’ software for breeding program simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the ‘Markovian Coalescent Simulator’ (‘MaCS’) for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>]. / MIT file LICENSE linux-64, osx-64, win-64
r-alphavantageclient 0.0.1 Download data from the Alpha Vantage API (<https://www.alphavantage.co/>). Alpha Vantage is a RESTful API which provides various financial data, including stock prices and technical indicators. There is documentation for the underlying API available here: <https://www.alphavantage.co/documentation/>. To get access to this API, the user needs to first claim an API key: <https://www.alphavantage.co/support/>. / MIT file LICENSE noarch
r-alscpc 1.0 Using of the accelerated line search algorithm for simultaneously diagonalize a set of symmetric positive definite matrices. / GPL (>= 2) noarch
r-alterryx 0.5.0 A tool to access each of the ‘Alteryx’ Gallery ‘API’ endpoints. Users can queue jobs, poll job status, and retrieve application output as a data frame. You will need an ‘Alteryx’ Server license and have ‘Alteryx’ Gallery running to utilize this package. The ‘API’ is accessed through the ‘URL’ that you setup for the server running ‘Alteryx’ Gallery and more information on the endpoints can be found at <https://gallery.alteryx.com/api-docs/>. / GPL-2 noarch
r-altmeta 2.2 Provides alternative statistical methods for meta-analysis, including new heterogeneity tests and measures that are robust to outliers. / GPL (>= 2) noarch
r-altopt 0.1.1 Creates the optimal (D, U and I) designs for the accelerated life testing with right censoring or interval censoring. It uses generalized linear model (GLM) approach to derive the asymptotic variance-covariance matrix of regression coefficients. The failure time distribution is assumed to follow Weibull distribution with a known shape parameter and log-linear link functions are used to model the relationship between failure time parameters and stress variables. The acceleration model may have multiple stress factors, although most ALTs involve only two or less stress factors. ALTopt package also provides several plotting functions including contour plot, Fraction of Use Space (FUS) plot and Variance Dispersion graphs of Use Space (VDUS) plot. / GPL-3 noarch
r-amap 0.8_17 Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions). / GPL linux-64, osx-64, win-64
r-amap.seq 1.0 An Approximated Most Average Powerful Test with Optimal FDR Control with Application to RNA-seq Data / GPL (>= 2) noarch
r-ambient 0.1.0 Generation of natural looking noise has many application within simulation, procedural generation, and art, to name a few. The ‘ambient’ package provides an interface to the ‘FastNoise’ C library and allows for efficient generation of perlin, simplex, worley, cubic, value, and white noise with optional pertubation in either 2, 3, or 4 (in case of simplex and white noise) dimensions. / MIT file LICENSE linux-64, osx-64, win-64
r-amcp 0.0.4 Accompanies Designing experiments and analyzing data: A model comparison perspective (3rd ed.) by Maxwell, Delaney, & Kelley (forthcoming from Routledge). Contains all of the data sets in the book’s chapters and end-of-chapter exercises. Information about the book is available at <http://www.DesigningExperiments.com>. / GPL (>= 3) noarch
r-amctestmaker 1.0.0 Generate code for use with the Optical Mark Recognition free software Auto Multiple Choice (AMC). More specifically, this package provides functions that use as input the question and answer texts, and output the LaTeX code for AMC. / GPL-3 noarch
r-ameco 0.2.9 Annual macro-economic database provided by the European Commission. / CC0 noarch
r-amelia 1.7.5 A tool that multiply imputes missing data in a single cross-section (such as a survey), from a time series (like variables collected for each year in a country), or from a time-series-cross-sectional data set (such as collected by years for each of several countries). Amelia II implements our bootstrapping-based algorithm that gives essentially the same answers as the standard IP or EMis approaches, is usually considerably faster than existing approaches and can handle many more variables. Unlike Amelia I and other statistically rigorous imputation software, it virtually never crashes (but please let us know if you find to the contrary!). The program also generalizes existing approaches by allowing for trends in time series across observations within a cross-sectional unit, as well as priors that allow experts to incorporate beliefs they have about the values of missing cells in their data. Amelia II also includes useful diagnostics of the fit of multiple imputation models. The program works from the R command line or via a graphical user interface that does not require users to know R. / GPL (>= 2) linux-64, osx-64, win-64
r-amelie 0.2.1 Implements anomaly detection as binary classification for cross-sectional data. Uses maximum likelihood estimates and normal probability functions to classify observations as anomalous. The method is presented in the following lecture from the Machine Learning course by Andrew Ng: <https://www.coursera.org/learn/machine-learning/lecture/C8IJp/algorithm/>, and is also described in: Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava (2003) <doi:10.1137/1.9781611972733.3>. / GPL (>= 3) noarch
r-amen 1.3 Analysis of dyadic network and relational data using additive and multiplicative effects (AME) models. The basic model includes regression terms, the covariance structure of the social relations model (Warner, Kenny and Stoto (1979) <DOI:10.1037/0022-3514.37.10.1742>, Wong (1982) <DOI:10.2307/2287296>), and multiplicative factor models (Hoff(2009) <DOI:10.1007/s10588-008-9040-4>). Four different link functions accommodate different relational data structures, including binary/network data (bin), normal relational data (nrm), ordinal relational data (ord) and data from fixed-rank nomination schemes (frn). Several of these link functions are discussed in Hoff, Fosdick, Volfovsky and Stovel (2013) <DOI:10.1017/nws.2013.17>. Development of this software was supported in part by NIH grant R01HD067509. / GPL-3 noarch
r-americancallopt 0.95 This package includes a set of pricing functions for American call options. The following cases are covered: Pricing of an American call using the standard binomial approximation; Hedge parameters for an American call with a standard binomial tree; Binomial pricing of an American call with continuous payout from the underlying asset; Binomial pricing of an American call with an underlying stock that pays proportional dividends in discrete time; Pricing of an American call on futures using a binomial approximation; Pricing of a currency futures American call using a binomial approximation; Pricing of a perpetual American call. The user should kindly notice that this material is for educational purposes only. The codes are not optimized for computational efficiency as they are meant to represent standard cases of analytical and numerical solution. / GPL-3 noarch
r-amerika 0.1.0 A color palette generator inspired by American politics, with colors ranging from blue on the left to gray in the middle and red on the right. A variety of palettes allow for a range of applications from brief discrete scales (e.g., three colors for Democrats, Independents, and Republicans) to continuous interpolated arrays including dozens of shades graded from blue (left) to red (right). This package greatly benefitted from building on the source code (with permission) from Ram and Wickham (2015). / MIT file LICENSE noarch
r-amget 1.0 AMGET allows to simply and rapidly creates highly informative diagnostic plots for ADAPT 5 models. Features include data analysis prior any modeling form either NONMEM or ADAPT shaped dataset, goodness-of-fit plots (GOF), posthoc-fits plots (PHF), parameters distribution plots (PRM) and visual predictive check plots (VPC) based on ADAPT output. / GPL (>= 2) noarch
r-aml 0.1_1 This package implements the adaptive mixed lasso (AML) method proposed by Wang et al.(2011). AML applies adaptive lasso penalty to a large number of predictors, thus producing a sparse model, while accounting for the population structure in the linear mixed model framework. The package here is primarily designed for application to genome wide association studies or genomic prediction in plant breeding populations, though it could be applied to other settings of linear mixed models. / GPL (>= 2) noarch
r-ammoniaconcentration 0.1 Provides a function to calculate the concentration of un-ionized ammonia in the total ammonia in aqueous solution using the pH and temperature values. / MIT file LICENSE noarch
r-amore 0.2_15 This package was born to release the TAO robust neural network algorithm to the R users. It has grown and I think it can be of interest for the users wanting to implement their own training algorithms as well as for those others whose needs lye only in the user space. / GPL (>= 2) linux-64, osx-64, win-64
r-ampd 0.2 A method for automatic detection of peaks in noisy periodic and quasi-periodic signals. This method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima. For further information see <doi:10.3390/a5040588>. / GPL noarch
r-anacoda 0.1.3.0 Is a collection of models to analyze genome scale codon data using a Bayesian framework. Provides visualization routines and checkpointing for model fittings. Currently published models to analyze gene data for selection on codon usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist et al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi (Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In addition ‘AnaCoDa’ contains three currently unpublished models. The FONSE (First order approximation On NonSense Error) model analyzes gene data for selection on codon usage against of nonsense error rates. The PA (PAusing time) and PANSE (PAusing time NonSense Error) models use ribosome footprinting data to analyze estimate ribosome pausing times with and without nonsense error rate from ribosome footprinting data. / GPL (>= 2) linux-64, osx-64, win-64
r-analogsea 0.7.2 Provides a set of functions for interacting with the ‘Digital Ocean’ API at <https://developers.digitalocean.com/documentation/v2>, including creating images, destroying them, rebooting, getting details on regions, and available images. / MIT noarch
r-analyz 1.4 Class with methods to read and execute R commands described as steps in a CSV file. / GPL (>= 2) noarch
r-anapuce 2.3 Functions for normalisation, differentially analysis of microarray data and local False Discovery Rate. / GPL-2 noarch
r-ande 1.0 AODE achieves highly accurate classification by averaging over all of a small space. / GPL-3 noarch
r-andrews 1.0 Andrews curves for visualization of multidimensional data / GPL (>= 2) noarch
r-anfis 0.99.1 The package implements ANFIS Type 3 Takagi and Sugeno’s fuzzy if-then rule network with the following features: (1) Independent number of membership functions(MF) for each input, and also different MF extensible types. (2) Type 3 Takagi and Sugeno’s fuzzy if-then rule (3) Full Rule combinations, e.g. 2 inputs 2 membership funtions -> 4 fuzzy rules (4) Hibrid learning, i.e. Descent Gradient for precedents and Least Squares Estimation for consequents (5) Multiple outputs. / GPL (>= 2) noarch
r-anmc 0.2.1 Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors. / GPL-3 linux-64, osx-64, win-64
r-annotlists 1.2 Annotate multiple lists from a specific annotation file. / GPL noarch
r-anocva 0.1.1 Provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test to compare clustering structures with applications in functional magnetic resonance imaging data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering. / GPL (>= 3) noarch
r-anoint 1.4 The tools in this package are intended to help researchers assess multiple treatment-covariate interactions with data from a parallel-group randomized controlled clinical trial. / GPL (>= 2) noarch
r-anomalydetection 1.0 A technique for detecting anomalies in seasonal univariate time series. / Apache License 2.0 linux-32, linux-64, osx-64, win-32, win-64
r-anovareplication 1.1.4 Allows for the computation of a prior predictive p-value to test replication of relevant features of original ANOVA studies. Relevant features are captured in informative hypotheses. The package also allows for the computation of sample sizes for new studies, post-hoc power calculations, and comes with a Shiny application in which all calculations can be conducted as well. The statistical underpinnings are described in Zondervan-Zwijnenburg (2019) <doi:10.31234/osf.io/6myqh>. / GPL (>= 3) noarch
r-antareseditobject 0.1.7 Edit an ‘Antares’ simulation before running it : create new areas, links, thermal clusters or binding constraints or edit existing ones. Update ‘Antares’ general & optimization settings. ‘Antares’ is an open source power system generator, more information available here : <https://antares-simulator.org/>. / GPL (>= 2) | file LICENSE noarch
r-antaresprocessing 0.17.0 Process results generated by ‘Antares’, a powerful open source software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about ‘Antares’ here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). You can see the results of several ANTARES studies here : <http://bpnumerique.rte-france.com/>. This package provides functions to create new columns like net load, load factors, upward and downward margins or to compute aggregated statistics like economic surpluses of consumers, producers and sectors. / GPL (>= 2) | file LICENSE noarch
r-antaresread 2.2.4 Import, manipulate and explore results generated by ‘Antares’, a powerful open source software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about ‘Antares’ here : <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). You can see the results of several ANTARES studies here : <http://bpnumerique.rte-france.com/>. / GPL (>= 2) | file LICENSE noarch
r-antiword 1.3 Wraps the ‘AntiWord’ utility to extract text from Microsoft Word documents. The utility only supports the old ‘doc’ format, not the new xml based ‘docx’ format. Use the ‘xml2’ package to read the latter. / GPL-2 linux-64, osx-64, win-64
r-anylib 1.0.5 Made to make your life simpler with packages, by installing and loading a list of packages, whether they are on CRAN, Bioconductor or github. For github, if you do not have the full path, with the maintainer name in it (e.g. achateigner/topReviGO), it will be able to load it but not to install it. / CC BY-SA 4.0 noarch
r-anytime 0.3.5 Convert input in any one of character, integer, numeric, factor, or ordered type into ‘POSIXct’ (or ‘Date’) objects, using one of a number of predefined formats, and relying on Boost facilities for date and time parsing. / GPL (>= 2) linux-64, osx-64, win-64
r-aod 1.3.1 Provides a set of functions to analyse overdispersed counts or proportions. Most of the methods are already available elsewhere but are scattered in different packages. The proposed functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM). / GPL (>= 2) noarch
r-aods3 0.4_1.1 Provides functions to analyse overdispersed counts or proportions. These functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM). aods3 is an S3 re-implementation of the deprecated S4 package aod. / GPL (>= 2) noarch
r-aoos 0.5.0 Another implementation of object-orientation in R. It provides syntactic sugar for the S4 class system and two alternative new implementations. One is an experimental version built around S4 and the other one makes it more convenient to work with lists as objects. / MIT file LICENSE noarch
r-aoptbdtvc 0.0.2 A collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737. / GPL (>= 2) noarch
r-apachelogprocessor 0.2.3 Provides capabilities to process Apache HTTPD Log files.The main functionalities are to extract data from access and error log files to data frames. / LGPL-3 | file LICENSE noarch
r-apc 1.3 Functions for age-period-cohort analysis. The data can be organised in matrices indexed by age-cohort, age-period or cohort-period. The data can include dose and response or just doses. The statistical model is a generalized linear model (GLM) allowing for 3,2,1 or 0 of the age-period-cohort factors. The canonical parametrisation of Kuang, Nielsen and Nielsen (2008) <DOI:10.1093/biomet/asn026> is used. Thus, the analysis does not rely on ad hoc identification. / GPL-3 noarch
r-apcanalysis 1.0 Analysis of data from unreplicated orthogonal experiments / GPL-3 noarch
r-apcf 0.1.3 The adapted pair correlation function transfers the concept of the pair correlation function from point patterns to patterns of objects of finite size and irregular shape (e.g. lakes within a country). This is a reimplementation of the method suggested by Nuske et al. (2009) <doi:10.1016/j.foreco.2009.09.050> using the libraries ‘GEOS’ and ‘GDAL’ directly instead of through ‘PostGIS’. / GPL (>= 3) win-64
r-apcluster 1.4.7 Implements Affinity Propagation clustering introduced by Frey and Dueck (2007) <DOI:10.1126/science.1136800>. The algorithms are largely analogous to the ‘Matlab’ code published by Frey and Dueck. The package further provides leveraged affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results. / GPL (>= 2) linux-64, osx-64, win-64
r-apdesign 1.0.0 An implementation of the additive polynomial (AP) design matrix. It constructs and appends an AP design matrix to a data frame for use with longitudinal data subject to seasonality. / GPL-3 noarch
r-ape 5.3 Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel’s test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
r-apercu 0.2.3 The goal is to print an aperçu, a short view of a vector, a matrix, a data.frame, a list or an array. By default, it prints the first 5 elements of each dimension. By default, the number of columns is equal to the number of lines. If you want to control the selection of the elements, you can pass a list, with each element being a vector giving the selection for each dimension. / CC BY-SA 4.0 noarch
r-apfr 1.0.2 Implements a multiple testing approach to the choice of a threshold gamma on the p-values using the Average Power Function (APF) and Bayes False Discovery Rate (FDR) robust estimation. Function apf_fdr() estimates both quantities from either raw data or p-values. Function apf_plot() produces smooth graphs and tables of the relevant results. Details of the methods can be found in Quatto P, Margaritella N, et al. (2019) <doi:10.1177/0962280219844288>. / GPL-3 noarch
r-aplore3 0.9 An unofficial companion to Applied Logistic Regression by D.W. Hosmer, S. Lemeshow and R.X. Sturdivant (3rd ed., 2013) containing the dataset used in the book. / GPL-3 noarch
r-aplpack 1.3.3 Some functions for drawing some special plots: The function ‘bagplot’ plots a bagplot, ‘faces’ plots chernoff faces, ‘iconplot’ plots a representation of a frequency table or a data matrix, ‘plothulls’ plots hulls of a bivariate data set, ‘plotsummary’ plots a graphical summary of a data set, ‘puticon’ adds icons to a plot, ‘skyline.hist’ combines several histograms of a one dimensional data set in one plot, ‘slider’ functions supports some interactive graphics, ‘spin3R’ helps an inspection of a 3-dim point cloud, ‘stem.leaf’ plots a stem and leaf plot, ‘stem.leaf.backback’ plots back-to-back versions of stem and leaf plot. / GPL (>= 2) noarch
r-apml0 0.9 Fit linear, logistic and Cox models regularized with L0, lasso (L1), elastic-net (L1 and L2), or net (L1 and Laplacian) penalty, and their adaptive forms, such as adaptive lasso / elastic-net and net adjusting for signs of linked coefficients. It solves L0 penalty problem by simultaneously selecting regularization parameters and the number of non-zero coefficients. This augmented and penalized minimization method provides an approximation solution to the L0 penalty problem, but runs as fast as L1 regularization problem. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients. It could deal with very high dimensional data and has superior selection performance. / GPL (>= 2) linux-64, osx-64, win-64
r-apng 1.0 Convert several png files into an animated png file. This package exports only a single function `apng’. Call the apng function with a vector of file names (which should be png files) to convert them to a single animated png file. / GPL-3 | file LICENSE noarch
r-appestimation 0.1.1 Calculating predictive model performance measures adjusted for predictor distributions using density ratio method (Sugiyama et al., (2012, ISBN:9781139035613)). L1 and L2 error for continuous outcome and C-statistics for binomial outcome are computed. / GPL-2 noarch
r-apple 0.3 Approximate Path for Penalized Likelihood Estimators for Generalized Linear Models penalized by LASSO or MCP / GPL-2 linux-64, osx-64, win-64
r-appliedpredictivemodeling 1.1_7 A few functions and several data set for the Springer book ‘Applied Predictive Modeling’. / GPL-2 noarch
r-appnn 1.0_0 Amyloid propensity prediction neural network (APPNN) is an amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation. / GPL-3 noarch
r-approximator 1.2_7 Performs Bayesian prediction of complex computer codes when fast approximations are available. It uses a hierarchical version of the Gaussian process, originally proposed by Kennedy and O’Hagan (2000), Biometrika 87(1):1. / GPL-2 noarch
r-approxmatch 1.0 Tools for constructing a matched design with multiple comparison groups. Further specifications of refined covariate balance restriction and exact match on covariate can be imposed. Matches are approximately optimal in the sense that the cost of the solution is at most twice the optimal cost, Crama and Spieksma (1992) <doi:10.1016/0377-2217(92)90078-N>. / MIT file LICENSE noarch
r-aprean3 1.0.1 An unofficial companion to the textbook Applied Regression Analysis by N.R. Draper and H. Smith (3rd Ed., 1998) including all the accompanying datasets. / GPL-3 noarch
r-aprof 0.4.1 Assists the evaluation of whether and where to focus code optimization, using Amdahl’s law and visual aids based on line profiling. Amdahl’s profiler organizes profiling output files (including memory profiling) in a visually appealing way. It is meant to help to balance development vs. execution time by helping to identify the most promising sections of code to optimize and projecting potential gains. The package is an addition to R’s standard profiling tools and is not a wrapper for them. / GPL (>= 2) noarch
r-apsimbatch 0.1.0.2374 Run APSIM in Batch mode / GPL-3 noarch
r-apsrtable 0.8_8 Formats latex tables from one or more model objects side-by-side with standard errors below, not unlike tables found in such journals as the American Political Science Review. / GPL (>= 2) noarch
r-aptools 6.8.8 We provide tools to estimate two prediction accuracy metrics, the average positive predictive values (AP) as well as the well-known AUC (the area under the receiver operator characteristic curve) for risk scores. The outcome of interest is either binary or censored event time. Note that for censored event time, our functions’ estimates, the AP and the AUC, are time-dependent for pre-specified time interval(s). A function that compares the APs of two risk scores/markers is also included. Optional outputs include positive predictive values and true positive fractions at the specified marker cut-off values, and a plot of the time-dependent AP versus time (available for event time data). / LGPL-3 noarch
r-aqr 0.4 This R extension provides methods to use a standalone ActiveQuant Master Server from within R. Currently available features include fetching and storing historical data, receiving and sending live data. Several utility methods for simple data transformations are included, too. For support requests, please join the mailing list at https://r-forge.r-project.org/mail/?group_id=1518 / GPL (>= 2) linux-64, osx-64, win-64
r-ar 1.1 In mathematics, ‘rejection sampling’ is a basic technique used to generate observations from a distribution. It is also commonly called ‘the Acceptance-Rejection method’ or ‘Accept-Reject algorithm’ and is a type of Monte Carlo method. ‘Acceptance-Rejection method’ is based on the observation that to sample a random variable one can perform a uniformly random sampling of the 2D cartesian graph, and keep the samples in the region under the graph of its density function. Package ‘AR’ is able to generate/simulate random data from a probability density function by Acceptance-Rejection method. Moreover, this package is a useful teaching resource for graphical presentation of Acceptance-Rejection method. From the practical point of view, the user needs to calculate a constant in Acceptance-Rejection method, which package ‘AR’ is able to compute this constant by optimization tools. Several numerical examples are provided to illustrate the graphical presentation for the Acceptance-Rejection Method. / LGPL-3 noarch
r-arabicstemr 1.2 Allows users to stem Arabic texts for text analysis. / GPL (>= 2) noarch
r-ararredux 1.0 Processes noble gas mass spectrometer data to determine the isotopic composition of argon (comprised of Ar36, Ar37, Ar38, Ar39 and Ar40) released from neutron-irradiated potassium-bearing minerals. Then uses these compositions to calculate precise and accurate geochronological ages for multiple samples as well as the covariances between them. Error propagation is done in matrix form, which jointly treats all samples and all isotopes simultaneously at every step of the data reduction process. Includes methods for regression of the time-resolved mass spectrometer signals to t=0 (‘time zero’) for both single- and multi-collector instruments, blank correction, mass fractionation correction, detector intercalibration, decay corrections, interference corrections, interpolation of the irradiation parameter between neutron fluence monitors, and (weighted mean) age calculation. All operations are performed on the logs of the ratios between the different argon isotopes so as to properly treat them as ‘compositional data’, sensu Aitchison [1986, The Statistics of Compositional Data, Chapman and Hall]. / GPL-2 noarch
r-arc 1.2 Implements the Classification-based on Association Rules (CBA) (Bing Liu, Wynne Hsu, Yiming Ma (1999) <http://dl.acm.org/citation.cfm?id=3000292.3000305>) algorithm for association rule classification (ARC). The package also contains several convenience methods that allow to automatically set CBA parameters (minimum confidence, minimum support) and it also natively handles numeric attributes by integrating a pre-discretization step. The rule generation phase is handled by the ‘arules’ package. To further decrease the size of the CBA models produced by the ‘arc’ package, postprocessing by the ‘qCBA’ package is suggested. / AGPL-3 noarch
r-archdata 1.2 The archdata package provides several types of data that are typically used in archaeological research. It provides all of the data sets used in Quantitative Methods in Archaeology Using R by David L Carlson, one of the Cambridge Manuals in Archaeology. / GPL noarch
r-arco 0.3_1 Set of functions to analyse and estimate Artificial Counterfactual models from Carvalho, Masini and Medeiros (2016) <DOI:10.2139/ssrn.2823687>. / MIT file LICENSE noarch
r-ardec 2.0 Package ArDec implements autoregressive-based decomposition of a time series based on the constructive approach in West (1997). Particular cases include the extraction of trend and seasonal components. / GPL (>= 2) noarch
r-areaplot 1.2_1 Plot stacked areas and confidence bands as filled polygons, or add polygons to existing plots. A variety of input formats are supported, including vectors, matrices, data frames, formulas, etc. / GPL (>= 2) noarch
r-arf3ds4 2.5_10 Activated Region Fitting (ARF) is an analysis method for fMRI data. / GPL-2 linux-64, osx-64, win-64
r-argo 2.0.0 Augmented Regression with General Online data (ARGO) for accurate estimation of influenza epidemics in United States on both national level and regional level. It replicates the method introduced in paper Yang, S., Santillana, M. and Kou, S.C. (2015) <doi:10.1073/pnas.1515373112> and Ning, S., Yang, S. and Kou, S.C. (2019) <doi:10.1038/s41598-019-41559-6>. / GPL-2 noarch
r-argon2 0.2_0 Utilities for secure password hashing via the argon2 algorithm. It is a relatively new hashing algorithm and is believed to be very secure. The ‘argon2’ implementation included in the package is the reference implementation. The package also includes some utilities that should be useful for digest authentication, including a wrapper of ‘blake2b’. For similar R packages, see sodium and ‘bcrypt’. See <https://en.wikipedia.org/wiki/Argon2> or <https://eprint.iacr.org/2015/430.pdf> for more information. / BSD 2-clause License file LICENSE linux-64, osx-64, win-64
r-argondash 0.1.0 Create awesome ‘Bootstrap 4’ dashboards powered by ‘Argon’. See more here <https://rinterface.github.io/argonDash/>. / GPL-2 noarch
r-argonr 0.1.0 R wrapper around the argon HTML library. More at <https://demos.creative-tim.com/argon-design-system/>. / GPL-2 noarch
r-argosfilter 0.63 Functions to filters animal satellite tracking data obtained from Argos. It is especially indicated for telemetry studies of marine animals, where Argos locations are predominantly of low-quality. / GPL (>= 2) noarch
r-argparse 2.0.1 A command line parser to be used with Rscript to write #! shebang scripts that gracefully accept positional and optional arguments and automatically generate usage. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-argparser 0.4 Cross-platform command-line argument parser written purely in R with no external dependencies. It is useful with the Rscript front-end and facilitates turning an R script into an executable script. / GPL (>= 3) noarch
r-argumentcheck 0.10.2 The typical process of checking arguments in functions is iterative. In this process, an error may be returned and the user may fix it only to receive another error on a different argument. ‘ArgumentCheck’ facilitates a more helpful way to perform argument checks allowing the programmer to run all of the checks and then return all of the errors and warnings in a single message. / GPL-3 noarch
r-arht 0.1.0 Perform the Adaptable Regularized Hotelling’s T^2 test (ARHT) proposed by Li et al., (2016) <arXiv:1609.08725>. Both one-sample and two-sample mean test are available with various probabilistic alternative prior models. It contains a function to consistently estimate higher order moments of the population covariance spectral distribution using the spectral of the sample covariance matrix (Bai et al. (2010) <doi:10.1111/j.1467-842X.2010.00590.x>). In addition, it contains a function to sample from 3-variate chi-squared random vectors approximately with a given correlation matrix when the degrees of freedom are large. / GPL (>= 2) noarch
r-aricode 0.1.2 Implements an efficient O(n) algorithm based on bucket-sorting for fast computation of standard clustering comparison measures. Available measures include adjusted Rand index (ARI), normalized information distance (NID), normalized mutual information (NMI), adjusted mutual information (AMI), normalized variation information (NVI) and entropy, as described in Vinh et al (2009) <doi:10.1145/1553374.1553511>. / GPL (>= 3) linux-64, osx-64, win-64
r-arm 1.10_1 Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007. / GPL (>= 3) noarch
r-armspp 0.0.2 An efficient ‘Rcpp’ implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) <doi:10.2307/2986138>. This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density. / MIT file LICENSE linux-64, osx-64, win-64
r-arnie 0.1.2 Arnold Schwarzenegger movie weekend box office records from 1982-2014 / CC BY-SA 4.0 noarch
r-arpobservation 1.2.0 Tools for simulating data generated by direct observation recording. Behavior streams are simulated based on an alternating renewal process, given specified distributions of event durations and interim times. Different procedures for recording data can then be applied to the simulated behavior streams. Functions are provided for the following recording methods: continuous duration recording, event counting, momentary time sampling, partial interval recording, whole interval recording, and augmented interval recording. / GPL-3 noarch
r-arpr 0.1.1 Provides convenience functions for programming with magrittr pipes. Conditional pipes, a string prefixer and a function to pipe the given object into a specific argument given by character name are currently supported. It is named after the dadaist Hans Arp, a friend of Rene Magritte. / GPL (>= 3) noarch
r-arpsdca 1.1.1 Functions for Arps decline-curve analysis on oil and gas data. Includes exponential, hyperbolic, harmonic, and hyperbolic-to-exponential models as well as the preceding with initial curtailment or a period of linear rate buildup. Functions included for computing rate, cumulative production, instantaneous decline, EUR, time to economic limit, and performing least-squares best fits. / LGPL-2.1 noarch
r-arrangements 1.1.5 Fast generators and iterators for permutations, combinations and partitions. The iterators allow users to generate arrangements in a memory efficient manner and the generated arrangements are in lexicographical (dictionary) order. Permutations and combinations can be drawn with/without replacement and support multisets. It has been demonstrated that ‘arrangements’ outperforms most of the existing packages of similar kind. Some benchmarks could be found at <https://randy3k.github.io/arrangements/articles/benchmark.html>. / MIT file LICENSE linux-64, osx-64, win-64
r-arrapply 2.1 High performance variant of apply() for a fixed set of functions. Considerable speedup is a trade-off for universality, user defined functions cannot be used with this package. However, 21 most currently employed functions are available for usage. They can be divided in three types: reducing functions (like mean(), sum() etc., giving a scalar when applied to a vector), mapping function (like normalise(), cumsum() etc., giving a vector of the same length as the input vector) and finally, vector reducing function (like diff() which produces result vector of a length different from the length of input vector). Optional or mandatory additional arguments required by some functions (e.g. norm type for norm() or normalise() functions) can be passed as named arguments in ‘…’. / GPL (>= 2) linux-64, osx-64, win-64
r-arrow 0.14.1.1 ‘Apache’ ‘Arrow’ <https://arrow.apache.org/> is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. This package provides an interface to the ‘Arrow C’ library. / Apache License (>= 2.0) linux-64
r-ars 0.6 Adaptive Rejection Sampling, Original version. / GPL (>= 2) linux-64, osx-64, win-64
r-arsenal 3.2.0 An Arsenal of ‘R’ functions for large-scale statistical summaries, which are streamlined to work within the latest reporting tools in ‘R’ and ‘RStudio’ and which use formulas and versatile summary statistics for summary tables and models. The primary functions include tableby(), a Table-1-like summary of multiple variable types ‘by’ the levels of one or more categorical variables; paired(), a Table-1-like summary of multiple variable types paired across two time points; modelsum(), which performs simple model fits on one or more endpoints for many variables (univariate or adjusted for covariates); freqlist(), a powerful frequency table across many categorical variables; comparedf(), a function for comparing data.frames; and write2(), a function to output tables to a document. / GPL (>= 2) noarch
r-artiva 1.2.3 Reversible Jump MCMC (RJ-MCMC)sampling for approximating the posterior distribution of a time varying regulatory network, under the Auto Regressive TIme VArying (ARTIVA) model (for a detailed description of the algorithm, see Lebre et al. BMC Systems Biology, 2010). Starting from time-course gene expression measurements for a gene of interest (referred to as target gene) and a set of genes (referred to as parent genes) which may explain the expression of the target gene, the ARTIVA procedure identifies temporal segments for which a set of interactions occur between the parent genes and the target gene. The time points that delimit the different temporal segments are referred to as changepoints (CP). / GPL (>= 2) noarch
r-artp 2.0.5 For calculating gene and pathway p-values using the Adaptive Rank Truncated Product test. / GPL-2 linux-64, osx-64, win-64
r-artp2 0.9.45 Pathway and gene level association test using raw data or summary statistics. / GPL-2 | GPL-3 linux-64, osx-64, win-64
r-arules 1.6_3 Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat. / GPL-3 linux-64, osx-64, win-64
r-arulescba 1.1.4 Provides a function to build an association rule-based classifier for data frames, and to classify incoming data frames using such a classifier. / GPL-3 linux-64, osx-64, win-64
r-arulesnbminer 0.1_5 NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets presented in Michael Hahsler. A model-based frequency constraint for mining associations from transaction data. Data Mining and Knowledge Discovery, 13(2):137-166, September 2006. In addition an extension for NB-precise rules is implemented. / GPL-2 noarch
r-arulessequences 0.2_22 Add-on for arules to handle and mine frequent sequences. Provides interfaces to the C implementation of cSPADE by Mohammed J. Zaki. / GPL-2 linux-64, osx-64, win-64
r-arxiv 0.5.19 An interface to the API for ‘arXiv’ (<https://arxiv.org>), a repository of electronic preprints for computer science, mathematics, physics, quantitative biology, quantitative finance, and statistics. / MIT file LICENSE noarch
r-asaur 0.50 Data sets are referred to in the text Applied Survival Analysis Using R by Dirk F. Moore, Springer, 2016, ISBN: 978-3-319-31243-9, <DOI:10.1007/978-3-319-31245-3>. / CC0 noarch
r-asciiruler 0.2 An ASCII ruler is for measuring text and is especially useful for sequence analysis. Included in this package are methods to create ASCII rulers and associated GenBank sequence blocks, multi-column text displays that make it easy for viewers to locate nucleotides by position. / GPL-3 | file LICENSE noarch
r-asd 2.2 Package runs simulations for adaptive seamless designs with and without early outcomes for treatment selection and subpopulation type designs. / GPL-3 noarch
r-asdreader 0.1_3 A simple driver that reads binary data created by the ASD Inc. portable spectrometer instruments, such as the FieldSpec (for more information, see <http://www.asdi.com/products/fieldspec-spectroradiometers>). Spectral data can be extracted from the ASD files as raw (DN), white reference, radiance, or reflectance. Additionally, the metadata information contained in the ASD file header can also be accessed. / GPL-3 noarch
r-asgs.foyer 0.2.1 The Australian Statistical Geography Standard (‘ASGS’) is a set of shapefiles by the Australian Bureau of Statistics. This package provides an interface to those shapefiles, as well as methods for converting coordinates to shapefiles. / MPL noarch
r-ash 1.0_15 David Scott’s ASH routines ported from S-PLUS to R. / GPL (>= 2) linux-64, osx-64, win-64
r-asioheaders 1.12.1_1 ‘Asio’ is a cross-platform C library for network and low-level I/O programming that provides developers with a consistent asynchronous model using a modern C approach. It is also included in Boost but requires linking when used with Boost. Standalone it can be used header-only (provided a recent compiler). ‘Asio’ is written and maintained by Christopher M. Kohlhoff, and released under the ‘Boost Software License’, Version 1.0. / BSL-1.0 noarch
r-askpass 1.0 Cross-platform utilities for prompting the user for credentials or a passphrase, for example to authenticate with a server or read a protected key. Includes native programs for MacOS and Windows, hence no ‘tcltk’ is required. Password entry can be invoked in two different ways: directly from R via the askpass() function, or indirectly as password-entry back-end for ‘ssh-agent’ or ‘git-credential’ via the SSH_ASKPASS and GIT_ASKPASS environment variables. Thereby the user can be prompted for credentials or a passphrase if needed when R calls out to git or ssh. / MIT file LICENSE linux-64, osx-64, win-32, win-64
r-asnipe 1.1.11 Implements several tools that are used in animal social network analysis. In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data. / GPL-2 noarch
r-aspbay 1.2 This package allows to make inference on the properties of causal genetic variants in linkage disequilibrium with genotyped markers. In a first step, we select a subset of variants using a score statistic for affected sib-pairs. In a second step, on the selected subset, we make inference on causal genetic variants in the considered region. / GPL-2 linux-64, osx-64, win-64
r-aspc 0.1.2 The aSPC test is designed to test global association between two groups of variables potentially with moderate to high dimension (e.g. in hundreds). The aSPC is particularly useful when the association signals between two groups of variables are sparse. / GPL-3 noarch
r-aspect 1.0_5 Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models. / GPL-2 linux-64, osx-64, win-64
r-aspi 0.2.0 Tools for the analysis and visualization of bilateral asymmetry in parasitic infections. / GPL-3 noarch
r-assa 1.0 Functions to model and decompose time series into principal components using singular spectrum analysis (de Carvalho and Rua (2017) <doi:10.1016/j.ijforecast.2015.09.004>; de Carvalho et al (2012) <doi:10.1016/j.econlet.2011.09.007>). / GPL (>= 3) linux-64, osx-64, win-64
r-assertable 0.2.5 Simple, flexible, assertions on data.frame or data.table objects with verbose output for vetting. While other assertion packages apply towards more general use-cases, assertable is tailored towards tabular data. It includes functions to check variable names and values, whether the dataset contains all combinations of a given set of unique identifiers, and whether it is a certain length. In addition, assertable includes utility functions to check the existence of target files and to efficiently import multiple tabular data files into one data.table. / GPL-3 noarch
r-assertive 0.3_5 Lots of predicates (is_* functions) to check the state of your variables, and assertions (assert_* functions) to throw errors if they aren’t in the right form. / GPL-3 noarch
r-assertive.base 0.0_7 A minimal set of predicates and assertions used by the assertive package. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL (>= 3) noarch
r-assertive.code 0.0_3 A set of predicates and assertions for checking the properties of code. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL-3 noarch
r-assertive.data 0.0_3 A set of predicates and assertions for checking the properties of (country independent) complex data types. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL-3 noarch
r-assertive.data.uk 0.0_2 A set of predicates and assertions for checking the properties of UK-specific complex data types. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL-3 noarch
r-assertive.data.us 0.0_2 A set of predicates and assertions for checking the properties of US-specific complex data types. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL-3 noarch
r-assertive.datetimes 0.0_2 A set of predicates and assertions for checking the properties of dates and times. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL-3 noarch
r-assertive.files 0.0_2 A set of predicates and assertions for checking the properties of files and connections. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL (>= 3) noarch
r-assertive.matrices 0.0_2 A set of predicates and assertions for checking the properties of matrices. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL (>= 3) noarch
r-assertive.models 0.0_2 A set of predicates and assertions for checking the properties of models. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL (>= 3) noarch
r-assertive.numbers 0.0_2 A set of predicates and assertions for checking the properties of numbers. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL (>= 3) noarch
r-assertive.properties 0.0_4 A set of predicates and assertions for checking the properties of variables, such as length, names and attributes. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL (>= 3) noarch
r-assertive.reflection 0.0_4 A set of predicates and assertions for checking the state and capabilities of R, the operating system it is running on, and the IDE being used. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL (>= 3) noarch
r-assertive.sets 0.0_3 A set of predicates and assertions for checking the properties of sets. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL (>= 3) noarch
r-assertive.strings 0.0_3 A set of predicates and assertions for checking the properties of strings. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL-3 noarch
r-assertive.types 0.0_3 A set of predicates and assertions for checking the types of variables. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly. / GPL (>= 3) noarch
r-assertthat 0.2.1 An extension to stopifnot() that makes it easy to declare the pre and post conditions that you code should satisfy, while also producing friendly error messages so that your users know what’s gone wrong. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-assist 3.1.5 A comprehensive package for fitting various non-parametric/semi-parametric linear/nonlinear fixed/mixed smoothing spline models. / GPL (>= 2) linux-64, osx-64, win-64
r-assocafc 1.0.2 When doing association analysis one does not always have the genotypes for the control population. In such cases it may be necessary to fall back on frequency based tests using well known sources for the frequencies in the control population, for instance, from the 1000 Genomes Project. The Allele Frequency Comparison (‘AssocAFC’) package performs multiple rare variant association analyses in both population and family-based GWAS (Genome-Wide Association Study) designs. It includes three score tests that are based on the difference of the sum of allele frequencies between cases and controls. Two of these tests, Wcorrected() and Wqls(), are collapsing-based tests and suffer from having protective and risk variants. The third test, afcSKAT(), is a score test that overcomes the mix of SNP (Single-Nucleotide Polymorphism) effect directions. For more details see Saad M and Wijsman EM (2017) <doi:10.1093/bib/bbx107>. / GPL-3 noarch
r-assocind 1.0.1 Implements several new association indices that can control for various types of errors. Also includes existing association indices and functions for simulating the effects of different rates of error on estimates of association strength between individuals using each method. / GPL-2 noarch
r-assortnet 0.12 Functions to calculate the assortment of vertices in social networks. This can be measured on both weighted and binary networks, with discrete or continuous vertex values. / GPL-2 noarch
r-assotester 0.1_10 R package with statistical tests and methods for genetic association studies with emphasis on rare variants and binary (dichotomous) traits / GPL (>= 3) linux-64, osx-64, win-64
r-ast 0.1.0 Fits a model to adjust and consider additional variations in three dimensions of age groups, time, and space on residuals excluded from a prediction model that have residual such as: linear regression, mixed model and so on. Details are given in Foreman et al. (2015) <doi:10.1186/1478-7954-10-1>. / GPL-2 | GPL-3 noarch
r-aster2 0.3 Aster models are exponential family regression models for life history analysis. They are like generalized linear models except that elements of the response vector can have different families (e. g., some Bernoulli, some Poisson, some zero-truncated Poisson, some normal) and can be dependent, the dependence indicated by a graphical structure. Discrete time survival analysis, zero-inflated Poisson regression, and generalized linear models that are exponential family (e. g., logistic regression and Poisson regression with log link) are special cases. Main use is for data in which there is survival over discrete time periods and there is additional data about what happens conditional on survival (e. g., number of offspring). Uses the exponential family canonical parameterization (aster transform of usual parameterization). Unlike the aster package, this package does dependence groups (nodes of the graph need not be conditionally independent given their predecessor node), including multinomial and two-parameter normal as families. Thus this package also generalizes mark-capture-recapture analysis. / GPL (>= 2) linux-64, osx-64, win-64
r-astrodatr 0.1 A collection of 19 datasets from contemporary astronomical research. They are described the textbook `Modern Statistical Methods for Astronomy with R Applications’ by Eric D. Feigelson and G. Jogesh Babu (Cambridge University Press, 2012, Appendix C) or on the website of Penn State’s Center for Astrostatistics (http://astrostatistics.psu.edu/datasets). These datasets can be used to exercise methodology involving: density estimation; heteroscedastic measurement errors; contingency tables; two-sample hypothesis tests; spatial point processes; nonlinear regression; mixture models; censoring and truncation; multivariate analysis; classification and clustering; inhomogeneous Poisson processes; periodic and stochastic time series analysis. / GPL noarch
r-astrofns 4.1_0 Miscellaneous astronomy functions, utilities, and data. / GPL (>= 2) noarch
r-astrolibr 0.1 Several dozen low-level utilities and codes from the Interactive Data Language (IDL) Astronomy Users Library (http://idlastro.gsfc.nasa.gov) are implemented in R. They treat: time, coordinate and proper motion transformations; terrestrial precession and nutation, atmospheric refraction and aberration, barycentric corrections, and related effects; utilities for astrometry, photometry, and spectroscopy; and utilities for planetary, stellar, Galactic, and extragalactic science. / GPL noarch
r-astsa 1.9 Data sets and scripts to accompany Time Series Analysis and Its Applications: With R Examples (4th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2017, <DOI:10.1007/978-3-319-52452-8>, and Time Series: A Data Analysis Approach Using R. Chapman-Hall, 2019, <ISBN: 978-0367221096>. / GPL-3 noarch
r-asyk 0.1.0 Density estimation by using symmetrical kernels and to calculate mean square error. See Scaillet (2004) <doi:10.1080/10485250310001624819> and Khan and Akbar (2019). / GPL-2 noarch
r-asymld 0.1 Computes asymmetric LD measures (ALD) for multi-allelic genetic data. These measures are identical to the correlation measure (r) for bi-allelic data. / GPL-2 noarch
r-asymptest 0.1.4 One and two sample mean and variance tests (differences and ratios) are considered. The test statistics are all expressed in the same form as the Student t-test, which facilitates their presentation in the classroom. This contribution also fills the gap of a robust (to non-normality) alternative to the chi-square single variance test for large samples, since no such procedure is implemented in standard statistical software. / GPL (>= 2) noarch
r-asynchlong 2.0 Estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent response and covariates are mismatched and observed intermittently within subjects. Kernel weighted estimating equations are used for generalized linear models with either time-invariant or time-dependent coefficients. / GPL-2 noarch
r-asypow 2015.6.25 A set of routines written in the S language that calculate power and related quantities utilizing asymptotic likelihood ratio methods. / ACM | file LICENSE (Restricts use) noarch
r-ate 0.2.0 Nonparametric estimation and inference for average treatment effects based on covariate balancing. / GPL (>= 2) noarch
r-atlas 1.0.0 Stanford ‘ATLAS’ (Advanced Temporal Search Engine) is a powerful tool that allows constructing cohorts of patients extremely quickly and efficiently. This package is designed to interface directly with an instance of ‘ATLAS’ search engine and facilitates API queries and data dumps. Prerequisite is a good knowledge of the temporal language to be able to efficiently construct a query. More information available at <https://shahlab.stanford.edu/start>. / GPL-3 noarch
r-atmcmc 1.0 Uses adaptive diagnostics to tune and run a random walk Metropolis MCMC algorithm, to converge to a specified target distribution and estimate means of functionals. / GPL (>= 2) noarch
r-atmray 1.31 Calculates acoustic traveltimes and ray paths in 1-D, linear atmospheres. Later versions will support arbitrary 1-D atmospheric models, such as radiosonde measurements and standard reference atmospheres. / GPL linux-64, osx-64, win-64
r-atsa 3.1.2 Contains some tools for testing, analyzing time series data and fitting popular time series models such as ARIMA, Moving Average and Holt Winters, etc. Most functions also provide nice and clear outputs like SAS does, such as identify, estimate and forecast, which are the same statements in PROC ARIMA in SAS. / GPL-2 | GPL-3 noarch
r-atsd 1.2.0 Provides functions for retrieving time-series and related meta-data such as entities, metrics, and tags from the Axibase Time-Series Database (ATSD). ATSD is a non-relational clustered database used for storing performance measurements from IT infrastructure resources: servers, network devices, storage systems, and applications. / Apache License 2.0 noarch
r-attachment 0.0.9 Tools to help manage dependencies during package development. This can retrieve all dependencies that are used in R files in the R directory, in Rmd files in vignettes directory and in ‘roxygen2’ documentation of functions. There is a function to update the Description file of your package and a function to create a file with the R commands to install all dependencies of your package. All functions to retrieve dependencies of R scripts and Rmd files can be used independently of a package development. / GPL-3 noarch
r-attempt 0.3.0 Tools for defensive programming, inspired by ‘purrr’ mappers and based on ‘rlang’.’attempt’ extends and facilitates defensive programming by providing a consistent grammar, and provides a set of easy to use functions for common tests and conditions. ‘attempt’ only depends on ‘rlang’, and focuses on speed, so it can be easily integrated in other functions and used in data analysis. / MIT file LICENSE noarch
r-attrcusum 0.1.0 An implementation of tools for design of attribute variable sampling interval cumulative sum chart. It currently provides information for monitoring of mean increase such as average number of sample to signal, average time to signal, a matrix of transient probabilities, suitable control limits when the data are (zero inflated) Poisson/binomial distribution. Functions in the tools can be easily applied to other count processes. Also, tools might be extended to more complicated cumulative sum control chart. We leave these issues as our perpetual work. / GPL (>= 2) linux-64, osx-64, win-64
r-atus 0.2 Abridged data from the American Time Use Survey (ATUS) for years 2003-2016. The ATUS is an annual survey conducted on a sample of individuals across the United States studying how individuals spent their time over the course of a day. Individual respondents were interviewed about what activities they did, during what times (rounded to 15 minute increments), at what locations, and in the presence of which individuals. The activities are subsequently encoded based on 3 separate tier codes for classification. This package includes data from the multi-year ATUS Activities, ATUS-CPS, and ATUS Respondents files were included. Columns were selected based on completeness of data as well as presence on the Frequently Used Variables list provided by the ATUS website. All activity codes (other than code ‘50’ for ‘Unable to Code’) were included. Permission was obtained from the Bureau of Labor Statistics for inclusion in this package. The full data can be obtained from <http://www.bls.gov/tus/>. / GPL (>= 2) noarch
r-auc 0.3.0 This package includes functions to compute the area under the curve of selected measures: The area under the sensitivity curve (AUSEC), the area under the specificity curve (AUSPC), the area under the accuracy curve (AUACC), and the area under the receiver operating characteristic curve (AUROC). The curves can also be visualized. Support for partial areas is provided. / GPL (>= 2) noarch
r-aucrf 1.1 Variable selection using Random Forest based on optimizing the area-under-the ROC curve (AUC) of the Random Forest. / GPL (>= 2) noarch
r-audio 0.1_6 Interfaces to audio devices (mainly sample-based) from R to allow recording and playback of audio. Built-in devices include Windows MM, Mac OS X AudioUnits and PortAudio (the last one is very experimental). / MIT file LICENSE linux-64, osx-64, win-64
r-audit 0.1_1 Two Bayesian methods for Accounting Populations / MIT noarch
r-auth0 0.1.1 Uses Auth0 API (see <https://auth0.com> for more information) to use a simple and secure authentication system. It provides tools to log in and out a shiny application using social networks or a list of e-mails. / MIT file LICENSE noarch
r-auto.pca 0.3 PCA done by eigenvalue decomposition of a data correlation matrix, here it automatically determines the number of factors by eigenvalue greater than 1 and it gives the uncorrelated variables based on the rotated component scores, Such that in each principal component variable which has the high variance are selected. It will be useful for non-statisticians in selection of variables. For more information, see the <http://www.ijcem.org/papers032013/ijcem_032013_06.pdf> web page. / GPL-2 noarch
r-autodeskr 0.1.3 An interface to the ‘AutoDesk’ ‘API’ Platform including the Authentication ‘API’ for obtaining authentication to the ‘AutoDesk’ Forge Platform, Data Management ‘API’ for managing data across the platform’s cloud services, Design Automation ‘API’ for performing automated tasks on design files in the cloud, Model Derivative ‘API’ for translating design files into different formats, sending them to the viewer app, and extracting design data, and Viewer for rendering 2D and 3D models (see <https://developer.autodesk.com> for more information). / Apache License | file LICENSE noarch
r-autoencoder 1.1 Implementation of the sparse autoencoder in R environment, following the notes of Andrew Ng (http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/sparseAutoencoder.pdf). The features learned by the hidden layer of the autoencoder (through unsupervised learning of unlabeled data) can be used in constructing deep belief neural networks. / GPL-2 noarch
r-automl 1.2.8 Fits from simple regression to highly customizable deep neural networks either with gradient descent or metaheuristic, using automatic hyper parameters tuning and custom cost function. A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization. / GNU General Public License noarch
r-automultinomial 2.0.0 Fits the autologistic model described in Besag’s famous 1974 paper on auto- models <http://www.jstor.org/stable/2984812>. Fits a multicategory generalization of the autologistic model when there are more than 2 response categories. Provides support for both asymptotic and bootstrap confidence intervals. For full model descriptions and a guide to the use of this package, please see the vignette. / GPL-2 noarch
r-autopls 1.3 Some convenience functions for pls regression, including backward variable selection and validation procedures, image based predictions and plotting. / GPL-2 noarch
r-autoregressionmde 1.0 Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model. / GPL-2 noarch
r-autoshiny 0.0.2 Static code compilation of a ‘shiny’ app given an R function (into ‘ui.R’ and ‘server.R’ files or into a ‘shiny’ app object). See examples at <https://github.com/alekrutkowski/autoshiny>. / GPL-2 noarch
r-autovarcore 1.0_4 Automatically find the best vector autoregression models and networks for a given time series data set. ‘AutovarCore’ evaluates eight kinds of models: models with and without log transforming the data, lag 1 and lag 2 models, and models with and without weekday dummy variables. For each of these 8 model configurations, ‘AutovarCore’ evaluates all possible combinations for including outlier dummies (at 2.5x the standard deviation of the residuals) and retains the best model. Model evaluation includes the Eigenvalue stability test and a configurable set of residual tests. These eight models are further reduced to four models because ‘AutovarCore’ determines whether adding weekday dummies improves the model fit. / MIT file LICENSE linux-64, osx-64, win-64
r-av 0.2 Bindings to ‘FFmpeg’ <http://www.ffmpeg.org/> AV library for working with audio and video in R. Generate high quality videos files by capturing images from the R graphics device combined with custom audio stream. This package interfaces directly to the C API and does not require any command line utilities. / MIT file LICENSE linux-64, osx-64
r-averisk 1.0.3 Average population attributable fractions are calculated for a set of risk factors (either binary or ordinal valued) for both prospective and case- control designs. Confidence intervals are found by Monte Carlo simulation. The method can be applied to either prospective or case control designs, provided an estimate of disease prevalence is provided. In addition to an exact calculation of AF, an approximate calculation, based on randomly sampling permutations has been implemented to ensure the calculation is computationally tractable when the number of risk factors is large. / CC0 noarch
r-aweek 1.0.0 Which day a week starts depends heavily on the either the local or professional context. This package is designed to be a lightweight solution to easily switching between week-based date definitions. / MIT file LICENSE noarch
r-awr 1.11.189 Installs the compiled Java modules of the Amazon Web Services (‘AWS’) ‘SDK’ to be used in downstream R packages interacting with ‘AWS’. See <https://aws.amazon.com/sdk-for-java> for more information on the ‘AWS’ ‘SDK’ for Java. / AGPL-3 noarch
r-awr.athena 2.0.7_0 ‘RJDBC’ based ‘DBI’ driver to Amazon Athena, which is an interactive query service to analyze data in Amazon ‘S3’ using standard ‘SQL’. / AGPL-3 noarch
r-awr.kms 0.1 Encrypt plain text and ‘decrypt’ cipher text using encryption keys hosted at Amazon Web Services (‘AWS’) Key Management Service (‘KMS’), on which see <https://aws.amazon.com/kms> for more information. / AGPL-3 noarch
r-aws 2.2_1 We provide a collection of R-functions implementing adaptive smoothing procedures in 1D, 2D and 3D. This includes the Propagation-Separation Approach to adaptive smoothing as described in J. Polzehl and V. Spokoiny (2006) <DOI:10.1007/s00440-005-0464-1>, J. Polzehl and V. Spokoiny (2004) <DOI:10.20347/WIAS.PREPRINT.998> and J. Polzehl, K. Papafitsoros, K. Tabelow (2018) <DOI:10.20347/WIAS.PREPRINT.2520>, the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter. / GPL (>= 2) linux-64, osx-64, win-64
r-aws.cloudtrail 0.1.5 A simple client package for the Amazon Web Services (‘AWS’) ‘CloudTrail’ ‘API’ <https://aws.amazon.com/cloudtrail/>. / GPL-2 noarch
r-aws.comprehend 0.1.2 Client for ‘AWS Comprehend’ <https://aws.amazon.com/comprehend>, a cloud natural language processing service that can perform a number of quantitative text analyses, including language detection, sentiment analysis, and feature extraction. / GPL (>= 2) noarch
r-aws.ec2metadata 0.2.0 Retrieve Amazon EC2 instance metadata from within the running instance. / GPL (>= 2) noarch
r-aws.iam 0.1.7 A simple client for the Amazon Web Services (‘AWS’) Identity and Access Management (‘IAM’) ‘API’ <https://aws.amazon.com/iam/>. / GPL (>= 2) noarch
r-aws.kms 0.1.2 Client package for the ‘AWS Key Management Service’ <https://aws.amazon.com/kms/>, a cloud service for managing encryption keys. / GPL (>= 2) noarch
r-aws.lambda 0.1.6 A simple client package for the Amazon Web Services (‘AWS’) Lambda ‘API’ <https://aws.amazon.com/lambda/>. / GPL (>= 2) noarch
r-aws.s3 0.3.12 A simple client package for the Amazon Web Services (‘AWS’) Simple Storage Service (‘S3’) ‘REST’ ‘API’ <https://aws.amazon.com/s3/>. / GPL (>= 2) noarch
r-aws.ses 0.1.4 A simple client package for the Amazon Web Services (AWS) Simple Email Service (SES) <http://aws.amazon.com/ses/> REST API. / GPL (>= 2) noarch
r-aws.signature 0.5.2 Generates version 2 and version 4 request signatures for Amazon Web Services (‘AWS’) <https://aws.amazon.com/> Application Programming Interfaces (‘APIs’) and provides a mechanism for retrieving credentials from environment variables, ‘AWS’ credentials files, and ‘EC2’ instance metadata. For use on ‘EC2’ instances, users will need to install the suggested package ‘aws.ec2metadata’ <https://cran.r-project.org/package=aws.ec2metadata>. / GPL (>= 2) noarch
r-aws.sns 0.1.7 A simple client package for the Amazon Web Services (‘AWS’) Simple Notification Service (‘SNS’) ‘API’ <https://aws.amazon.com/sns/>. / GPL (>= 2) noarch
r-aws.sqs 0.1.10 A simple client package for the Amazon Web Services (‘AWS’) Simple Queue Service (‘SQS’) <https://aws.amazon.com/sqs/> ‘API’. / GPL (>= 2) noarch
r-aws.transcribe 0.1.2 Client for ‘AWS Transcribe’ <https://aws.amazon.com/documentation/transcribe>, a cloud transcription service that can convert an audio media file in English and other languages into a text transcript. / GPL (>= 2) noarch
r-aws.translate 0.1.3 A client for ‘AWS Translate’ <https://aws.amazon.com/documentation/translate>, a machine translation service that will convert a text input in one language into a text output in another language. / GPL (>= 2) noarch
r-awsjavasdk 0.2.0 Provides boilerplate access to all of the classes included in the Amazon Web Services (‘AWS’) Java Software Development Kit (SDK) via package:’rJava’. According to Amazon, the ‘SDK helps take the complexity out of coding by providing Java APIs for many AWS services including Amazon S3, Amazon EC2, DynamoDB, and more’. You can read more about the included Java code on Amazon’s website: <https://aws.amazon.com/sdk-for-java/>. / GPL-2 noarch
r-awsmethods 1.1_1 Defines the method extract and provides ‘openMP’ support as needed in several packages. / GPL (>= 2) linux-64, osx-64, win-64
r-azurevmmetadata 1.0.0 A simple interface to the instance metadata for a virtual machine running in Microsoft’s ‘Azure’ cloud. This provides information about the VM’s configuration, such as its processors, memory, networking, storage, and so on. Part of the ‘AzureR’ family of packages. / MIT noarch
r-b2z 1.4 This package fits the Bayesian two-Zone Models. / GPL-2 noarch
r-b6e6rl 1.1 This package contains b6e6rl algorithm, adaptive differential evolution for global optimization. / GPL-2 noarch
r-babar 1.0 Babar is designed to use nested sampling (a Bayesian analysis technique) to compare possible models for bacterial growth curves, as well as extracting parameters. It allows model evidence and parameter likelihood values to be extracted, and also contains helper functions for comparing distributions as well as direct access to the underlying nested sampling code. / GPL-2 noarch
r-backpipe 0.2.3 Provides a backward-pipe operator for ‘magrittr’ (%<%) or ‘pipeR’ (%<<%) that allows for a performing operations from right-to-left. This allows writing more legible code where right-to-left ordering is natural. This is common with hierarchies and nested structures such as trees, directories or markup languages (e.g. HTML and XML). The package also includes a R-Studio add-in that can be bound to a keyboard shortcut. / GPL-2 | file LICENSE noarch
r-backports 1.1.4 Implementations of functions which have been introduced in R since version 3.0.0. The backports are conditionally exported which results in R resolving the function names to the version shipped with R (if available) and uses the implemented backports as fallback. This way package developers can make use of the new functions without worrying about the minimum required R version. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
r-backtest 0.3_4 The backtest package provides facilities for exploring portfolio-based conjectures about financial instruments (stocks, bonds, swaps, options, et cetera). / GPL (>= 2) noarch
r-bacprior 2.0 The BACprior package provides an approximate sensitivity analysis of the Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012) with regards to the hyperparameter omega. The package also provides functions to guide the user in their choice of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014). / GPL (>= 2) noarch
r-badgecreatr 0.2.0 Tired of copy and pasting almost identical markdown for badges in every new R-package that you create, on Github or other code-sharing sites? This package allows you to easily paste badges. If you want to, it will also search your DESCRIPTION file and extract the package name, license, R-version, and current projectversion and transform that into badges. It will also search for a .travis.yml file and create a Travis badge, if you use Codecov.io to check your code coverage after a Travis build this package will also build a Codecov.io-badge. All the badges can be placed individually or can be placed below the top YAML content of your RMarkdown file (Readme.Rmd) or README.md file. Currently creates badges for Projectstatus (Repostatus.org), license Travis Build Status, Codecov, Minimal R version, CRAN status, CRAN downloads, Github stars and forks, Package rank, rdocumentation, current version of your package and last change of README.Rmd. / GPL-3 noarch
r-baessd 1.0.1 Implements sample size calculations following the approach described in Bayesian Average Error Based Approach to Hypothesis Testing and Sample Size Determination. / GPL-2 noarch
r-bagrboostr 0.0.2 bagRboostR is a set of ensemble classifiers for multinomial classification. The bagging function is the implementation of Breiman’s ensemble as described by Opitz & Maclin (1999). The boosting function is the implementation of Stagewise Additive Modeling using a Multi-class Exponential loss function (SAMME) created by Zhu et al (2006). Both bagging and SAMME implementations use randomForest as the weak classifier and expect a character outcome variable. Each ensemble classifier returns a character vector of predictions for the test set. / MIT file LICENSE noarch
r-bain 0.2.1 Computes approximated adjusted fractional Bayes factors for equality, inequality, and about equality constrained hypotheses. S3 methods are available for specific types of lm() models, namely ANOVA, ANCOVA, and multiple regression, and for the t_test(). The statistical underpinnings are described in Hoijtink, Mulder, van Lissa, and Gu, (2018) <doi:10.31234/osf.io/v3shc>, Gu, Mulder, and Hoijtink, (2018) <doi:10.1111/bmsp.12110>, Hoijtink, Gu, and Mulder, (2018) <doi:10.1111/bmsp.12145>, and Hoijtink, Gu, Mulder, and Rosseel, (2018) <doi:10.1037/met0000187>. / GPL (>= 3) linux-64, osx-64, win-64
r-bairt 0.1.2 Bayesian estimation of the two and three parameter models of item response theory (IRT). Also, it is possible to use a web interactive application intended for the making of an MCMC estimation and model-fit of the IRT models. / GPL (>= 2) noarch
r-balancecheck 0.2 Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies. / GPL (>= 2) noarch
r-bammtools 2.1.6 Provides functions for analyzing and visualizing complex macroevolutionary dynamics on phylogenetic trees. It is a companion package to the command line program BAMM (Bayesian Analysis of Macroevolutionary Mixtures) and is entirely oriented towards the analysis, interpretation, and visualization of evolutionary rates. Functionality includes visualization of rate shifts on phylogenies, estimating evolutionary rates through time, comparing posterior distributions of evolutionary rates across clades, comparing diversification models using Bayes factors, and more. / GPL (>= 2) linux-64, osx-64, win-64
r-bamp 2.0.7 Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08> Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman’s R. / GPL-3 linux-64, osx-64, win-64
r-bannercommenter 0.1.1 A convenience package for use while drafting code. It facilitates making stand-out comment lines decorated with bands of characters. The input text strings are converted into R comment lines, suitably formatted. These are then displayed in a console window and, if possible, automatically transferred to a clipboard ready for pasting into an R script. Designed to save time when drafting R scripts that will need to be navigated and maintained by other programmers. / GPL (>= 2) noarch
r-banxicor 0.9.0 Provides functions to scrape IQY calls to Bank of Mexico, downloading and ordering the data conveniently. / CC0 noarch
r-baprestopro 0.1 Bayesian estimation and prediction for stochastic processes based on the Euler approximation. Considered processes are: jump diffusion, (mixed) diffusion models, hidden (mixed) diffusion models, non-homogeneous Poisson processes (NHPP), (mixed) regression models for comparison and a regression model including a NHPP. / GPL (>= 2) noarch
r-barborgradient 1.0.5 Tool to find where a function has its lowest value(minimum). The functions can be any dimensions. Recommended use is with eps=10^-10, but can be run with 10^-20, although this depends on the function. Two more methods are in this package, simple gradient method (Gradmod) and Powell method (Powell). These are not recommended for use, their purpose are purely for comparison. / GPL-3 noarch
r-barcode 1.1 This package includes the function code{barcode()}, which produces a histogram-like plot of a distribution that shows granularity in the data. / GPL (>= 2) noarch
r-barcodingr 1.0_2 To perform species identification using DNA barcodes. / GPL-2 noarch
r-barnard 1.8 Barnard’s unconditional test for 2x2 contingency tables. / GPL-2 linux-64, osx-64, win-64
r-barsurf 0.3.1 Produces heat maps, contour plots, bar plots (in 3D) and surface plots (also, in 3D). Is designed for plotting functions of two variables, however, can plot relatively arbitrary matrices. Uses HCL color space, extensively. Also, supports triangular plots and nested matrices. / GPL (>= 2) noarch
r-bart 2.5 Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information on BART, see Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285> and Sparapani, Logan, McCulloch and Laud (2016) <doi:10.1002/sim.6893>. / GPL (>= 2) linux-64, osx-64, win-64
r-bartmachinejars 1.1 These are bartMachine’s Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without bartMachine. / GPL-3 noarch
r-barycenter 1.3.1 Computations of entropy regularized Wasserstein Distances, a.k.a. dual-Sinkhorn divergences, and entropy regularized Wasserstein Barycenters. Relevant papers are Marco Cuturi (2013) <arXiv:1306.0895>, Marco Cuturi (2014) <arXiv:1310.4375> and Jason Altschuler et al. <arXiv:1705.09634>. / GPL-2 linux-64, osx-64, win-64
r-bas 1.5.3 Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner’s g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors in GLMs of Li and Clyde (2018) <arXiv:1503.06913>. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using Sampling w/out Replacement or an efficient MCMC algorithm samples models using the BAS tree structure as an efficient hash table. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used. The user may force variables to always be included. Details behind the sampling algorithm are provided in Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049>. This material is based upon work supported by the National Science Foundation under Grant DMS-1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. / GPL (>= 3) linux-64, osx-64, win-64
r-base 3.6.1 R is a free software environment for statistical computing and graphics. / GPL-3.0 linux-32, linux-64, osx-64, win-32, win-64
r-base64 2.0 Compatibility wrapper to replace the orphaned package by Romain Francois. New applications should use the ‘openssl’ or ‘base64enc’ package instead. / MIT file LICENSE noarch
r-base64enc 0.1_3 This package provides tools for handling base64 encoding. It is more flexible than the orphaned base64 package. / GPL-2 | GPL-3 linux-32, linux-64, osx-64, win-32, win-64
r-base64url 1.4 In contrast to RFC3548, the 62nd character () is replaced with -, the 63rd character (/) is replaced with _. Furthermore, the encoder does not fill the string with trailing =. The resulting encoded strings comply to the regular expression pattern [A-Za-z0-9_-] and thus are safe to use in URLs or for file names. The package also comes with a simple base32 encoder/decoder suited for case insensitive file systems. / GPL-3 linux-64, osx-64, win-64
r-baseline 1.2_1 Collection of baseline correction algorithms, along with a framework and a GUI for optimising baseline algorithm parameters. Typical use of the package is for removing background effects from spectra originating from various types of spectroscopy and spectrometry, possibly optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more. / GPL-2 noarch
r-basetheme 0.1.1 Functions to create and select graphical themes for the base plotting system. Contains: 1) several custom pre-made themes 2) mechanism for creating new themes by making persistent changes to the graphical parameters of base plots. / GPL-2 noarch
r-basicmcmcplots 0.2.2 Provides a function for examining posterior MCMC samples from a single chain using trace plots and density plots, and from multiple chains by comparing posterior medians and credible intervals from each chain. These plotting functions have a variety of options, such as figure sizes, legends, parameters to plot, and saving plots to file. Functions interface with the NIMBLE software package, see de Valpine, Turek, Paciorek, Anderson-Bergman, Temple Lang and Bodik (2017) <doi:10.1080/10618600.2016.1172487>. / GPL-3 noarch
r-basicspace 0.20 Conducts Aldrich-McKelvey and Blackbox Scaling (Poole et al 2016) <doi:10.18637/jss.v069.i07> to recover latent dimensions of judgment. / GPL-2 linux-64, osx-64, win-64
r-basix 1.1 BASIX provides some efficient C/C implementations to speed up calculations in R. / GPL-2 linux-64, osx-64, win-64
r-bass 0.2.2 Bayesian fitting and sensitivity analysis methods for adaptive spline surfaces. Built to handle continuous and categorical inputs as well as functional or scalar output. An extension of the methodology in Denison, Mallick and Smith (1998) <doi:10.1023/A:1008824606259>. / GPL-3 linux-64, osx-64, win-64
r-batch 1.1_5 Functions to allow you to easily pass command-line arguments into R, and functions to aid in submitting your R code in parallel on a cluster and joining the results afterward (e.g. multiple parameter values for simulations running in parallel, splitting up a permutation test in parallel, etc.). See `parseCommandArgs(…)’ for the main example of how to use this package. / GPL noarch
r-batchmeans 1.0_3 Provides consistent batch means estimation of Monte Carlo standard errors. / GPL (>= 2) noarch
r-batchscr 0.1.0 Handy frameworks, such as error handling and log generation, for batch scripts. Use case: in scripts running in remote servers, set error handling mechanism for downloading and uploading and record operation log. / MIT file LICENSE noarch
r-batman 0.1.0 Survey systems and other third-party data sources commonly use non-standard representations of logical values when it comes to qualitative data - Yes, No and N/A, say. batman is a package designed to seamlessly convert these into logicals. It is highly localised, and contains equivalents to boolean values in languages including German, French, Spanish, Italian, Turkish, Chinese and Polish. / MIT file LICENSE linux-64, osx-64, win-64
r-bayesbio 1.0.0 A hodgepodge of hopefully helpful functions. Two of these perform shrinkage estimation: one using a simple weighted method where the user can specify the degree of shrinkage required, and one using James-Stein shrinkage estimation for the case of unequal variances. / GPL-3 noarch
r-bayescombo 1.0 Combine diverse evidence across multiple studies to test a high level scientific theory. The methods can also be used as an alternative to a standard meta-analysis. / GPL-3 noarch
r-bayescomm 0.1_2 Bayesian multivariate binary (probit) regression models for analysis of ecological communities. / GPL (>= 2) linux-64, osx-64, win-64
r-bayesda 2012.04_1 Functions for Bayesian Data Analysis, with datasets from the book Bayesian data Analysis (second edition) by Gelman, Carlin, Stern and Rubin. Not all datasets yet, hopefully completed soon. / GPL (>= 2) noarch
r-bayesdccgarch 2.0 Bayesian estimation of dynamic conditional correlation GARCH model for multivariate time series volatility (Fioruci, J.A., Ehlers, R.S. and Andrade-Filho, M.G., (2014), DOI:10.1080/02664763.2013.839635). / GPL (>= 2) linux-64, osx-64, win-64
r-bayesdistreg 0.1.0 Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) <doi:10.2139/ssrn.3048658> which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference. / GPL-2 noarch
r-bayesgarch 2.1.3 Provides the bayesGARCH() function which performs the Bayesian estimation of the GARCH(1,1) model with Student’s t innovations as described in Ardia (2008) <doi:10.1007/978-3-540-78657-3>. / GPL (>= 2) linux-64, osx-64, win-64
r-bayesianetas 1.0.3 The Epidemic Type Aftershock Sequence (ETAS) model is one of the best-performing methods for modeling and forecasting earthquake occurrences. This package implements Bayesian estimation routines to draw samples from the full posterior distribution of the model parameters, given an earthquake catalog. The paper on which this package is based is Gordon J. Ross - Bayesian Estimation of the ETAS Model for Earthquake Occurrences (2016), available from the below URL. / GPL-3 linux-64, osx-64, win-64
r-bayesianpower 0.1.6 A collection of methods to determine the required sample size for the evaluation of inequality constrained hypotheses by means of a Bayes factor. Alternatively, for a given sample size, the unconditional error probabilities or the expected conditional error probabilities can be determined. Additional material on the methods in this package is available in Klaassen, F., Hoijtink, H. & Gu, X. (2019) <doi:10.31219/osf.io/d5kf3>. / LGPL-3 noarch
r-bayesimages 0.6_0 Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. This package implements Bayesian image analysis using the hidden Potts model with external field prior of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>. Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC), and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to <doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for further details. / GPL (>= 2) | file LICENSE linux-64, osx-64, win-64
r-bayesloglin 1.0.1 The function MC3() searches for log-linear models with the highest posterior probability. The function gibbsSampler() is a blocked Gibbs sampler for sampling from the posterior distribution of the log-linear parameters. The functions findPostMean() and findPostCov() compute the posterior mean and covariance matrix for decomposable models which, for these models, is available in closed form. / GPL (>= 2) linux-64, osx-64, win-64
r-bayesm 3.1_3 Covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014). / GPL (>= 2) linux-64, osx-64, win-64
r-bayesmams 0.1 Calculating Bayesian sample sizes for multi-arm trials where several experimental treatments are compared to a common control, perhaps even at multiple stages. / GPL (>= 2) noarch
r-bayesmixsurv 0.9.1 Bayesian Mixture Survival Models using Additive Mixture-of-Weibull Hazards, with Lasso Shrinkage and Stratification. As a Bayesian dynamic survival model, it relaxes the proportional-hazard assumption. Lasso shrinkage controls overfitting, given the increase in the number of free parameters in the model due to presence of two Weibull components in the hazard function. / GPL (>= 2) noarch
r-bayesni 0.1 A Bayesian testing procedure for noninferiority trials with binary endpoints. The prior is constructed based on Bernstein polynomials with options for both informative and non-informative prior. The critical value of the test statistic (Bayes factor) is determined by minimizing total weighted error (TWE) criteria / GPL-2 noarch
r-bayespiecehazselect 1.1.0 Fits a piecewise exponential hazard to survival data using a Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive formulation for the spatial dependency in the hazard rates for each piece. This function uses Metropolis- Hastings-Green MCMC to allow the number of split points to vary and also uses Stochastic Search Variable Selection to determine what covariates drive the risk of the event. This function outputs trace plots depicting the number of split points in the hazard and the number of variables included in the hazard. The function saves all posterior quantities to the desired path. / GPL-2 noarch
r-bayespiecewiseicar 0.2.1 Fits a piecewise exponential hazard to survival data using a Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive formulation for the spatial dependency in the hazard rates for each piece. This function uses Metropolis- Hastings-Green MCMC to allow the number of split points to vary. This function outputs graphics that display the histogram of the number of split points and the trace plots of the hierarchical parameters. The function outputs a list that contains the posterior samples for the number of split points, the location of the split points, and the log hazard rates corresponding to these splits. Additionally, this outputs the posterior samples of the two hierarchical parameters, Mu and Sigma^2. / GPL-2 noarch
r-bayesqr 2.3 Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) <doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R. / GPL (>= 2) linux-64, osx-64, win-64
r-bayess 1.4 bayess contains a collection of functions that allows the reenactment of the R programs used in the book Bayesian Essentials with R (revision of Bayesian Core) without further programming. R code being available as well, they can be modified by the user to conduct one’s own simulations. / GPL-2 noarch
r-bayessae 1.0_2 Provides a variety of methods from Rao (2003, ISBN:0-471-41374-7) and some other research articles to deal with several specific small area area- level models in Bayesian framework. Models provided range from the basic Fay-Herriot model to its improvement such as You-Chapman models, unmatched models, spatial models and so on. Different types of priors for specific parameters could be chosen to obtain MCMC posterior draws. The main sampling function is written in C with GSL lab so as to facilitate the computation. Model internal checking and model comparison criteria are also involved. / GPL-2 linux-64, osx-64, win-64
r-bayest 1.0 Provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>. / GPL-2 noarch
r-bayestree 0.3_1.4 This is an implementation of BART:Bayesian Additive Regression Trees, by Chipman, George, McCulloch (2010). / GPL (>= 2) linux-64, osx-64, win-64
r-bayestreeprior 1.0.1 Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) <DOI:10.2307/2669832>. The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) <DOI:10.2307/2669832> is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016). / GPL-3 noarch
r-bayesvalidate 0.0 BayesValidate implements the software validation method described in the paper Validation of Software for Bayesian Models using Posterior Quantiles (Cook, Gelman, and Rubin, 2005). It inputs a function to perform Bayesian inference as well as functions to generate data from the Bayesian model being fit, and repeatedly generates and analyzes data to check that the Bayesian inference program works properly. / GPL (>= 2) noarch
r-bayesvarsel 1.8.0 Conceived to calculate Bayes factors in linear models and then to provide a formal Bayesian answer to testing and variable selection problems. From a theoretical side, the emphasis in this package is placed on the prior distributions and it allows a wide range of them: Jeffreys (1961); Zellner and Siow(1980)<DOI:10.1007/bf02888369>; Zellner and Siow(1984); Zellner (1986)<DOI:10.2307/2233941>; Fernandez et al. (2001)<DOI:10.1016/s0304-4076(00)00076-2>; Liang et al. (2008)<DOI:10.1198/016214507000001337> and Bayarri et al. (2012)<DOI:10.1214/12-aos1013>. The interaction with the package is through a friendly interface that syntactically mimics the well-known lm() command of R. The resulting objects can be easily explored providing the user very valuable information (like marginal, joint and conditional inclusion probabilities of potential variables; the highest posterior probability model, HPM; the median probability model, MPM) about the structure of the true -data generating- model. Additionally, this package incorporates abilities to handle problems with a large number of potential explanatory variables through parallel and heuristic versions of the main commands, Garcia-Donato and Martinez-Beneito (2013)<DOI:10.1080/01621459.2012.742443>. / GPL-2 linux-64, osx-64, win-64
r-bayesxsrc 3.0_1 BayesX performs Bayesian inference in structured additive regression (STAR) models. The R package BayesXsrc provides the BayesX command line tool for easy installation. A convenient R interface is provided in package R2BayesX. / GPL-2 | GPL-3 linux-64, osx-64, win-64
r-bayfoxr 0.0.1 A Bayesian, global planktic foraminifera core top calibration to modern sea-surface temperatures. Includes four calibration models, considering species-specific calibration parameters and seasonality. / GPL (>= 3) noarch
r-bayhap 1.0.1 The package BayHap performs simultaneous estimation of uncertain haplotype frequencies and association with haplotypes based on generalized linear models for quantitative, binary and survival traits. Bayesian statistics and Markov Chain Monte Carlo techniques are the theoretical framework for the methods of estimation included in this package. Prior values for model parameters can be included by the user. Convergence diagnostics and statistical and graphical analysis of the sampling output can be also carried out. / GPL-2 linux-64, osx-64, win-64
r-bayhaz 0.1_3 A suite of R functions for Bayesian estimation of smooth hazard rates via Compound Poisson Process (CPP) and Bayesian Penalized Spline (BPS) priors. / GPL (>= 2) noarch
r-bayloredpsych 0.5 Functions and data used for Baylor University Educational Psychology Quantitative Courses / GPL (>= 2) noarch
r-baystar 0.2_9 The manuscript introduces the BAYSTAR package, which provides the functionality for Bayesian estimation in autoregressive threshold models. / GPL (>= 2) noarch
r-bb 2014.10_1 Barzilai-Borwein spectral methods for solving nonlinear system of equations, and for optimizing nonlinear objective functions subject to simple constraints. A tutorial style introduction to this package is available in a vignette on the CRAN download page or, when the package is loaded in an R session, with vignette(BB). / GPL-3 noarch
r-bbefkr 4.2 Estimating optimal bandwidths for the regression mean function approximated by the functional Nadaraya-Watson estimator and the error density approximated by a kernel density of residuals simultaneously in a scalar-on-function regression. As a by-product of Markov chain Monte Carlo, the optimal choice of semi-metric is selected based on largest marginal likelihood. / GPL (>= 2) noarch
r-bbemkr 2.0 Bayesian bandwidth estimation for Nadaraya-Watson type multivariate kernel regression with Gaussian error density / GPL (>= 2) noarch
r-bbl 0.1.5 Supervised learning using Boltzmann Bayes model inference, which extends naive Bayes model to include interactions. Enables classification of data into multiple response groups based on a large number of discrete predictors that can take factor values of heterogeneous levels. Either pseudo-likelihood and mean field inference can be used with L2 regularization, cross-validation, and prediction on new data. Woo et al. (2016) <doi:10.1186/s12864-016-2871-3>. / GPL-2 linux-64, osx-64, win-64
r-bbmisc 1.11 Miscellaneous helper functions for and from B. Bischl and some other guys, mainly for package development. / BSD_2_clause file LICENSE linux-64, osx-64, win-64
r-bbmle 1.0.20 Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the ‘mle’ classes in the ‘stats4’ package. / GPL noarch
r-bbmm 3.0 The model provides an empirical estimate of a movement path using discrete location data obtained at relatively short time intervals. / GNU General Public License noarch
r-bbmv 2.1 Provides a set of functions to fit general macroevolutionary models for continuous traits evolving in adaptive landscapes of any shape. This package implements the Fokker-Planck-Kolmogorov model (FPK), in which the trait evolves under random diffusion but is also subject to a force that pulls it towards specific values - this force can be of any shape. FPK has a version in which hard reflective bounds exist at the extremes of the trait interval: this second model is called BBMV. / GPL-2 noarch
r-bbo 0.2 This package provides an R implementation of Biogeography-Based Optimization (BBO), originally invented by Prof. Dan Simon, Cleveland State University, Ohio. This method is an application of the concept of biogeography, a study of the geographical distribution of biological organisms, to optimization problems. More information about this method can be found here: http://academic.csuohio.edu/simond/bbo/. / GPL (>= 3) noarch
r-bcaboot 0.2_1 Computation of bootstrap confidence intervals in an almost automatic fashion. / GPL (>= 2) noarch
r-bcbcsf 1.0_1 Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features. / GPL (>= 2) linux-64, osx-64, win-64
r-bcc1997 0.1.1 Calculates the prices of European options based on the universal solution provided by Bakshi, Cao and Chen (1997) <doi:10.1111/j.1540-6261.1997.tb02749.x>. This solution considers stochastic volatility, stochastic interest and random jumps. Please cite their work if this package is used. / GPL (>= 2) noarch
r-bcdating 0.9.8 Tools for Dating Business Cycles using Harding-Pagan (Quarterly Bry-Boschan) method and various plotting features. / GPL-2 noarch
r-bcf 1.2.1 Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2017) <arXiv:1706.09523> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <arXiv:1309.1906>. / GPL-3 linux-64, osx-64, win-64
r-bcgee 0.1 Provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>. / GPL-2 noarch
r-bclust 1.5 Builds a dendrogram using log posterior as a natural distance defined by the model and meanwhile waits the clustering variables. It is also capable to computing equivalent Bayesian discrimination probabilities. The adopted method suites small sample large dimension setting. The model parameter estimation maybe difficult, depending on data structure and the chosen distribution family. / GPL (>= 2) linux-64, osx-64, win-64
r-bcp 4.0.3 Provides an implementation of the Barry and Hartigan (1993) product partition model for the normal errors change point problem using Markov Chain Monte Carlo. It also extends the methodology to regression models on a connected graph (Wang and Emerson, 2015); this allows estimation of change point models with multivariate responses. Parallel MCMC, previously available in bcp v.3.0.0, is currently not implemented. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
r-bcpa 1.1 The Behavioral Change Point Analysis (BCPA) is a method of identifying hidden shifts in the underlying parameters of a time series, developed specifically to be applied to animal movement data which is irregularly sampled. The method is based on: E. Gurarie, R. Andrews and K. Laidre A novel method for identifying behavioural changes in animal movement data (2009) Ecology Letters 12:5 395-408. / Unlimited linux-64, osx-64, win-64
r-bcpmeta 1.0 A Bayesian approach to detect mean shifts in AR(1) time series while accommodating metadata (if available). In addition, a linear trend component is allowed. / GPL (>= 2) noarch
r-bcra 2.1 Functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute’s Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals. / GPL (>= 2) noarch
r-bcrypt 1.1 Bindings to the ‘blowfish’ password hashing algorithm derived from the ‘OpenBSD’ implementation. / BSD_2_clause file LICENSE linux-64, osx-64, win-64
r-bcv 1.0.1 Methods for choosing the rank of an SVD approximation via cross validation. The package provides both Gabriel-style block holdouts and Wold-style speckled holdouts. It also includes an implementation of the SVDImpute algorithm. For more information about Bi-cross-validation, see Owen & Perry’s 2009 AoAS article (at http://arxiv.org/abs/0908.2062) and Perry’s 2009 PhD thesis (at http://arxiv.org/abs/0909.3052). / BSD_3_clause file LICENSE linux-64, osx-64, win-64
r-bda 10.1.9 Functions for density estimation based on grouped data, and next-generation gene expression data. / Unlimited linux-64, osx-64, win-64
r-bdgraph 2.60 Statistical tools for Bayesian structure learning in undirected graphical models for continuous, discrete, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models literature, including Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi and Wit (2019) <doi:10.18637/jss.v089.i03>. / GPL (>= 2) linux-64, osx-64, win-64
r-bdots 0.1.19 Analyze differences among time series curves with p-value adjustment for multiple comparisons introduced in Oleson et al (2015) <DOI:10.1177/0962280215607411>. / GPL-3 noarch
r-bdp2 0.1.3 Tools and workflow to choose design parameters in Bayesian adaptive single-arm phase II trial designs with binary endpoint (response, success) with possible stopping for efficacy and futility at interim analyses. Also contains routines to determine and visualize operating characteristics. See Kopp-Schneider et al. (2018) <doi:10.1002/bimj.201700209>. / GPL-2 noarch
r-bdpv 1.3 Computation of asymptotic confidence intervals for negative and positive predictive values in binary diagnostic tests in case-control studies. Experimental design for hypothesis tests on predictive values. / GPL (>= 2) noarch
r-bdsmatrix 1.3_3 This is a special case of sparse matrices, used by coxme. / LGPL-2 linux-32, linux-64, osx-64, win-32, win-64
r-beanplot 1.2 Plots univariate comparison graphs, an alternative to boxplot/stripchart/violin plot. / GPL-2 noarch
r-beast 1.1 Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2017) <arXiv:1709.06111> for a detailed presentation of the method. / GPL-2 noarch
r-bedmatrix 1.6.1 A matrix-like data structure that allows for efficient, convenient, and scalable subsetting of binary genotype/phenotype files generated by PLINK (<https://www.cog-genomics.org/plink2>), the whole genome association analysis toolset, without loading the entire file into memory. / MIT linux-64, osx-64, win-64
r-beepr 1.3 The main function of this package is beep(), with the purpose to make it easy to play notification sounds on whatever platform you are on. It is intended to be useful, for example, if you are running a long analysis in the background and want to know when it is ready. / GPL-3 noarch
r-beeswarm 0.2.3 The bee swarm plot is a one-dimensional scatter plot like stripchart, but with closely-packed, non-overlapping points. / Artistic-2.0 noarch
r-beginr 0.1.7 Useful functions for R beginners, including hints for the arguments of the ‘plot()’ function, self-defined functions for error bars, user-customized pair plots and hist plots, enhanced linear regression figures, etc.. This package could be helpful to R experts as well. / MIT noarch
r-behavr 0.3.2 Implements an S3 class based on ‘data.table’ to store and process efficiently ethomics (high-throughput behavioural) data. / GPL-3 noarch
r-belex 0.1.0 Tools for downloading historical financial data from the www.belex.rs. / GPL-3 noarch
r-belg 0.2.3 Calculates the Boltzmann entropy of a landscape gradient. This package uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018 (<doi:10.1111/tgis.12315>). It also extend the original idea by allowing calculations on data with missing values. / MIT file LICENSE linux-64, osx-64, win-64
r-benchden 1.0.5 Full implementation of the 28 distributions introduced as benchmarks for nonparametric density estimation by Berlinet and Devroye (1994). Includes densities, cdfs, quantile functions and generators for samples as well as additional information on features of the densities. Also contains the 4 histogram densities used in Rozenholc/Mildenberger/Gather (2010). / GPL (>= 2) noarch
r-benchr 0.2.3_1 Provides infrastructure to accurately measure and compare the execution time of R expressions. / GPL (>= 2) linux-64, osx-64, win-64
r-bender 0.1.1 R client for Bender Hyperparameters optimizer : <https://bender.dreem.com> The R client allows you to communicate with the Bender API and therefore submit some new trials within your R script itself. / MIT file LICENSE noarch
r-benford.analysis 0.1.5 Provides tools that make it easier to validate data using Benford’s Law. / GPL-3 noarch
r-benfordtests 1.2.0 Several specialized statistical tests and support functions for determining if numerical data could conform to Benford’s law. / GPL-3 linux-64, osx-64, win-64
r-bentcablear 0.3.0 Included are two main interfaces for fitting and diagnosing bent-cable regressions for autoregressive time-series data or independent data (time series or otherwise): ‘bentcable.ar()’ and ‘bentcable.dev.plot()’. Some components in the package can also be used as stand-alone functions. The bent cable (linear-quadratic-linear) generalizes the broken stick (linear-linear), which is also handled by this package. Version 0.2 corrects a glitch in the computation of confidence intervals for the CTP. References that were updated from Versions 0.2.1 and 0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improves robustness of the error-message producing mechanism. It is the author’s intention to distribute any future updates via GitHub. / GPL (>= 3) noarch
r-beqi2 2.0_0 Tool for analysing benthos data. It estimates several quality indices like the total abundance of species, species richness, Margalef’s d, AZTI Marine Biotic Index (AMBI), and the BEQI-2 index. Furthermore, additional (optional) features are provided that enhance data preprocessing: (1) genus to species conversion, i.e.,taxa counts at the taxonomic genus level can optionally be converted to the species level and (2) pooling: small samples are combined to bigger samples with a standardized size to (a) meet the data requirements of the AMBI, (b) generate comparable species richness values and (c) give a higher benthos signal to noise ratio. / GPL (>= 3) noarch
r-ber 4.0 The functions in this package remove batch effects from microarrary normalized data. The expression levels of the genes are represented in a matrix where rows correspond to independent samples and columns to genes (variables). The batches are represented by categorical variables (objects of class factor). When further covariates of interest are available they can be used to remove efficiently the batch effects and adjust the data. / GPL-2 noarch
r-berryfunctions 1.18.2 Draw horizontal histograms, color scattered points by 3rd dimension, enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, fit multiple functions. / GPL (>= 2) noarch
r-bess 1.0.6 An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2017) <arXiv:1709.06254>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables. / GPL-3 linux-64, osx-64, win-64
r-bestglm 0.37 Best subset glm using information criteria or cross-validation. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
r-bestree 0.5.2 Decision tree algorithm with a major feature added. Allows for users to define an ordering on the partitioning process. Resulting in Branch-Exclusive Splits Trees (BEST). Cedric Beaulac and Jeffrey S. Rosentahl (2019) <arXiv:1804.10168>. / MIT file LICENSE noarch
r-betabit 1.3 Three games: proton, frequon and regression. Each one is a console-based data-crunching game for younger and older data scientists. Act as a data-hacker and find Slawomir Pietraszko’s credentials to the Proton server. In proton you have to solve four data-based puzzles to find the login and password. There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools. Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try. In frequon you will help to perform statistical cryptanalytic attack on a corpus of ciphered messages. This time seven sub-tasks are pushing the bar much higher. Do you accept the challenge? In regression you will test your modeling skills in a series of eight sub-tasks. Try only if ANOVA is your close friend. It’s a part of Beta and Bit project. You will find more about the Beta and Bit project at <http://betabit.wiki>. / GPL-2 noarch
r-betacal 0.1.0 Fit beta calibration models and obtain calibrated probabilities from them. / MIT file LICENSE noarch
r-betafam 1.0 To detecting rare variants for quantitative traits using nuclear families, the linear combination methods are proposed using the estimated regression coefficients from the multiple regression and regularized regression as the weights. / GPL (>= 2) noarch
r-betalink 2.2.1 Measures of beta-diversity in networks, and easy visualization of why two networks are different. / BSD_2_clause file LICENSE noarch
r-betategarch 3.3 Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions). / GPL-2 linux-64, osx-64, win-64
r-bethel 0.2 The sample size according to the Bethel’s procedure. / GPL (>= 2) noarch
r-bevimed 5.3 A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - ‘A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases’, Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>. / GPL (>= 2) linux-64, osx-64, win-64
r-beyondbenford 1.1 Allows to compare the goodness of fit of Benford’s and Blondeau Da Silva’s digit distributions in a dataset. It is used to check whether the data distribution is consistent with theoretical distributions highlighted by Blondeau Da Silva or not (through the dat.distr() function): this ideal theoretical distribution must be at least approximately followed by the data for the use of Blondeau Da Silva’s model to be well-founded. It also enables to plot histograms of digit distributions, both observed in the dataset and given by the two theoretical approaches (with the digit.ditr() function). Finally, it proposes to quantify the goodness of fit via Pearson’s chi-squared test (with the chi2() function). / GPL-2 noarch
r-bezier 1.1.2 The bezier package is a toolkit for working with Bezier curves and splines. The package provides functions for point generation, arc length estimation, degree elevation and curve fitting. / GPL (>= 2) noarch
r-bfa 0.4 Provides model fitting for several Bayesian factor models including Gaussian, ordinal probit, mixed and semiparametric Gaussian copula factor models under a range of priors. / GPL-3 linux-64, osx-64, win-64
r-bfp 0.0_40 Implements the Bayesian paradigm for fractional polynomial models under the assumption of normally distributed error terms, see Sabanes Bove, D. and Held, L. (2011) <doi:10.1007/s11222-010-9170-7>. / GPL (>= 2) linux-64, osx-64, win-64
r-bfsl 0.1.0 Provides the solution from York (1968) <doi:10.1016/S0012-821X(68)80059-7> for fitting a straight line to bivariate data with errors in both coordinates. It gives unbiased estimates of the intercept, slope and standard errors of the best-fit straight line to independent points with (possibly correlated) normally distributed errors in both x and y. Other commonly used errors-in-variables methods, such as orthogonal distance regression, geometric mean regression or Deming regression are special cases of York’s solution. / GPL-3 noarch
r-bgge 0.6.5 Application of genome prediction for a continuous variable, focused on genotype by environment (GE) genomic selection models (GS). It consists a group of functions that help to create regression kernels for some GE genomic models proposed by Jarquín et al. (2014) <doi:10.1007/s00122-013-2243-1> and Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>. Also, it computes genomic predictions based on Bayesian approaches. The prediction function uses an orthogonal transformation of the data and specific priors present by Cuevas et al. (2014) <doi:10.1534/g3.114.013094>. / GPL-3 noarch
r-bgmfiles 0.0.6 A collection of box-geometry model (BGM) files for the Atlantis ecosystem model. Atlantis is a deterministic, biogeochemical, whole-of-ecosystem model (see <http://atlantis.cmar.csiro.au/> for more information). / CC0 noarch
r-bgphazard 1.2.3 Computes the hazard rate estimate as described by Nieto-Barajas and Walker (2002) and Nieto-Barajas (2003). / GPL (>= 2) noarch
r-bgsimd 1.0 Implement an efficient block Gibbs sampler with incomplete data from a multinomial distribution taking values from the k categories 1,2,…,k, where data are assumed to miss at random and each missing datum belongs to one and only one of m distinct non-empty proper subsets A1, A2,…, Am of 1,2,…,k and the k categories are labelled such that only consecutive A’s may overlap. / GPL (>= 2) noarch
r-bh 1.69.0_1 Boost provides free peer-reviewed portable C source libraries. A large part of Boost is provided as C template code which is resolved entirely at compile-time without linking. This package aims to provide the most useful subset of Boost libraries for template use among CRAN package. By placing these libraries in this package, we offer a more efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. As of release 1.69.0-1, the following Boost libraries are included: ‘algorithm’ ‘align’ ‘any’ ‘atomic’ ‘bimap’ ‘bind’ ‘circular_buffer’ ‘compute’ ‘concept’ ‘config’ ‘container’ ‘date_time’ ‘detail’ ‘dynamic_bitset’ ‘exception’ ‘filesystem’ ‘flyweight’ ‘foreach’ ‘functional’ ‘fusion’ ‘geometry’ ‘graph’ ‘heap’ ‘icl’ ‘integer’ ‘interprocess’ ‘intrusive’ ‘io’ ‘iostreams’ ‘iterator’ ‘math’ ‘move’ ‘mpl’ ‘multiprcecision’ ‘numeric’ ‘pending’ ‘phoenix’ ‘preprocessor’ ‘propery_tree’ ‘random’ ‘range’ ‘scope_exit’ ‘smart_ptr’ ‘sort’ ‘spirit’ ‘tuple’ ‘type_traits’ ‘typeof’ ‘unordered’ ‘utility’ ‘uuid’. / BSL-1.0 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-bhat 0.9_10 Functions for MLE, MCMC, CIs (originally in Fortran) / GPL (>= 2) noarch
r-bhh2 2016.05.31 Functions and data sets reproducing some examples in Box, Hunter and Hunter II. Useful for statistical design of experiments, especially factorial experiments. / GPL (>= 2) noarch
r-bhm 1.15 Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method. / GPL (>= 2) noarch
r-bhsbvar 1.0.4 Provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) <doi:10.3982/ECTA12356>, Baumeister and Hamilton (2017) <doi:10.3386/w24167>, and Baumeister and Hamilton (2018) <doi:10.1016/j.jmoneco.2018.06.005>. Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided. / GPL (>= 3) linux-64, osx-64, win-64
r-biasedurn 1.07 Statistical models of biased sampling in the form of univariate and multivariate noncentral hypergeometric distributions, including Wallenius’ noncentral hypergeometric distribution and Fisher’s noncentral hypergeometric distribution (also called extended hypergeometric distribution). See vignette(UrnTheory) for explanation of these distributions. / GPL-3 linux-64, osx-64, win-64
r-bibplots 0.0.6 Currently, the package provides four functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations, and two plot function to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software ‘CRExplorer’ (see <http://crexplorer.net>). Further extension to more plot variants is planned. / EUPL noarch
r-bibtex 0.4.2 Utility to parse a bibtex file. / GPL (>= 2) linux-64, osx-64, win-32, win-64
r-biclique 1.0.3 A tool for enumerating maximal complete bipartite graphs. The input should be a edge list file or a binary matrix file. The output are maximal complete bipartite graphs. Algorithms used can be found in this paper Y Zhang et al. BMC Bioinformatics 2014 15:110 <doi:10.1186/1471-2105-15-110>. / GPL-2 linux-64, osx-64, win-64
r-bicorn 0.1.0 Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor. / GPL-2 noarch
r-bidimregression 2.0.0 Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965). / GPL-3 noarch
r-bife 0.6 Estimates fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and computes average partial effects. Incidental parameter bias can be reduced with an asymptotic bias-correction proposed by Fernandez-Val (2009) <doi:10.1016/j.jeconom.2009.02.007>. / GPL-2 linux-64, osx-64, win-64
r-bigalgebra 0.8.4.1 This package provides arithmetic functions for R matrix and big.matrix objects. / LGPL-3 linux-64, osx-64, win-64
r-biganalytics 1.1.14 Extend the ‘bigmemory’ package with various analytics. Functions ‘bigkmeans’ and ‘binit’ may also be used with native R objects. For ‘tapply’-like functions, the bigtabulate package may also be helpful. For linear algebra support, see ‘bigalgebra’. For mutex (locking) support for advanced shared-memory usage, see ‘synchronicity’. / LGPL-3 linux-64, osx-64, win-64
r-bigintegeralgos 0.1.2 Features the multiple polynomial quadratic sieve algorithm for factoring large integers and a vectorized factoring function that returns the complete factorization of an integer. Utilizes the C library GMP (GNU Multiple Precision Arithmetic) and classes created by Antoine Lucas et al. found in the ‘gmp’ package. / GPL-2 linux-64, osx-64, win-64
r-biglm 0.9_1 Regression for data too large to fit in memory / GPL-3 linux-64, osx-64, win-64
r-biglmm 0.9_1 Regression for data too large to fit in memory. This package functions exactly like the ‘biglm’ package, but works with later versions of R. / GPL-3 linux-64, osx-64, win-64
r-bigmap 2.1.0 Unsupervised clustering protocol for large scale structured data, based on a low dimensional representation of the data. Dimensionality reduction is performed using a parallelized implementation of the t-Stochastic Neighboring Embedding algorithm (Garriga J. and Bartumeus F. (2018), <arXiv:1812.09869>). / GPL-3 linux-64, osx-64
r-bigmemory 4.5.33 Create, store, access, and manipulate massive matrices. Matrices are allocated to shared memory and may use memory-mapped files. Packages ‘biganalytics’, ‘bigtabulate’, ‘synchronicity’, and ‘bigalgebra’ provide advanced functionality. / LGPL-3 linux-64, osx-64, win-64
r-bigmemory.sri 0.1.3 This package provides a shared resource interface for the bigmemory and synchronicity packages. / LGPL-3 noarch
r-bigml 0.1.2 The ‘bigml’ package contains bindings for the BigML API. The package includes methods that provide straightforward access to basic API functionality, as well as methods that accommodate idiomatic R data types and concepts. / LGPL-3 noarch
r-bigreadr 0.1.9 Read large text files by splitting them in smaller files. Package bigreadr also provides some convenient wrappers of fread/fwrite. / GPL-3 linux-64, osx-64, win-64
r-bigreg 0.1.2 Allows the user to carry out GLM on very large data sets. Data can be created using the data_frame() function and appended to the object with object$append(data); data_frame and data_matrix objects are available that allow the user to store large data on disk. The data is stored as doubles in binary format and any character columns are transformed to factors and then stored as numeric (binary) data while a look-up table is stored in a separate .meta_data file in the same folder. The data is stored in blocks and GLM regression algorithm is modified and carries out a MapReduce- like algorithm to fit the model. The functions bglm(), and summary() and bglm_predict() are available for creating and post-processing of models. The library requires Armadillo installed on your system. It probably won’t function on windows since multi-core processing is done using mclapply() which forks R on Unix/Linux type operating systems. / GPL-2 linux-64, osx-64
r-bigsplines 1.1_1 Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial. / GPL-2 linux-64, osx-64, win-64
r-bigtabulate 1.1.5 Extend the bigmemory package with ‘table’, ‘tapply’, and ‘split’ support for ‘big.matrix’ objects. The functions may also be used with native R matrices for improving speed and memory-efficiency. / LGPL-3 linux-64, osx-64, win-64
r-bigtcr 1.1 For studying recurrent disease and death with competing risks, comparisons based on the well-known cumulative incidence function can be confounded by different prevalence rates of the competing events. Alternatively, comparisons of the conditional distribution of the survival time given the failure event type are more relevant for investigating the prognosis of different patterns of recurrence disease. This package implements a nonparametric estimator for the conditional cumulative incidence function and a nonparametric conditional bivariate cumulative incidence function for the bivariate gap times proposed in Huang et al. (2016) <doi:10.1111/biom.12494>. / GPL-3 linux-64, osx-64, win-64
r-bigtime 0.1.0 Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Bien and Matteson (2017) <arXiv:1412.5250v2> and Wilms, Basu, Bien and Matteson (2017) <arXiv:1707.09208>. / GPL-2 linux-64, osx-64, win-64
r-bigvar 1.0.4 Estimates VAR and VARX models with structured Lasso Penalties. / GPL-2 linux-64, osx-64, win-64
r-bikeshare14 0.1.2 Anonymised Bay Area bike share trip data for the year 2014. Also contains additional metadata on stations and weather. / CC0 noarch
r-bild 1.1_5 Performs logistic regression for binary longitudinal data, allowing for serial dependence among observations from a given individual and a random intercept term. Estimation is via maximization of the exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed, with some restrictions. / GPL-2 linux-64, osx-64, win-64
r-bimetallic 1.0 A power calculator for Genome-wide association studies (GWAs) with combined gold (error-free) and silver (erroneous) phenotyping per McDavid A, Crane PK, Newton KM, Crosslin DR, et al. (2011) / GPL-2 noarch
r-bimets 1.4.0 Time series analysis, (dis)aggregation and manipulation, e.g. time series extension, merge, projection, lag, lead, delta, moving and cumulative average and product, selection by index, date and year-period, conversion to daily, monthly, quarterly, (semi)annually. Simultaneous equation models definition, estimation, simulation and forecasting with coefficient restrictions, error autocorrelation, exogenization, add-factors, impact and interim multipliers analysis, conditional equation evaluation, endogenous targeting and model renormalization. / EUPL noarch
r-bimixt 1.0 Estimates non-Gaussian mixture models of case-control data. The four types of models supported are binormal, two component constrained, two component unconstrained, and four component. The most general model is the four component model, under which both cases and controls are distributed according to a mixture of two unimodal distributions. In the four component model, the two component distributions of the control mixture may be distinct from the two components of the case mixture distribution. In the two component unconstrained model, the components of the control and case mixtures are the same; however the mixture probabilities may differ for cases and controls. In the two component constrained model, all controls are distributed according to one of the two components while cases follow a mixture distribution of the two components. In the binormal model, cases and controls are distributed according to distinct unimodal distributions. These models assume that Box-Cox transformed case and control data with a common lambda parameter are distributed according to Gaussian mixture distributions. Model parameters are estimated using the expectation-maximization (EM) algorithm. Likelihood ratio test comparison of nested models can be performed using the lr.test function. AUC and PAUC values can be computed for the model-based and empirical ROC curves using the auc and pauc functions, respectively. The model-based and empirical ROC curves can be graphed using the roc.plot function. Finally, the model-based density estimates can be visualized by plotting a model object created with the bimixt.model function. / GPL-3 noarch
r-binarize 1.3 Provides methods for the binarization of one-dimensional data and some visualization functions. / Artistic-2.0 linux-64, osx-64, win-64
r-binaryemvs 0.1 Implements variable selection for high dimensional datasets with a binary response variable using the EM algorithm. Both probit and logit models are supported. Also included is a useful function to generate high dimensional data with correlated variables. / GPL-3 noarch
r-binarylogic 0.3.9 Provides the binary S3 class. The instance of binary is used to convert a decimal number (Base10) to a binary number (Base2). The Class provides some features e.G. shift(), rotate(), summary(). Based on logical vectors. / GPL-3 noarch
r-binb 0.0.4 A collection of ‘LaTeX’ styles using ‘Beamer’ customization for pdf-based presentation slides in ‘RMarkdown’. At present it contains ‘RMarkdown’ adaptations of the LaTeX themes ‘Metropolis’ (formerly ‘mtheme’) theme by Matthias Vogelgesang and others (now included in ‘TeXLive’), the ‘IQSS’ by Ista Zahn (which is included here), and the ‘Monash’ theme by Rob J Hyndman. Additional (free) fonts may be needed: ‘Metropolis’ prefers ‘Fira’, and ‘IQSS’ requires ‘Libertinus’. / GPL-2 noarch
r-binda 1.0.3 The binda package implements functions for multi-class discriminant analysis using binary predictors, for corresponding variable selection, and for dichotomizing continuous data. / GPL-3 noarch
r-bindata 0.9_19 Generation of correlated artificial binary data. / GPL-2 noarch
r-bindr 0.1.1 Provides a simple interface for creating active bindings where the bound function accepts additional arguments. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-bindrcpp 0.2.2 Provides an easy way to fill an environment with active bindings that call a C function. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
r-binford 0.1.0 Binford’s hunter-gatherer data includes more than 200 variables coding aspects of hunter-gatherer subsistence, mobility, and social organization for 339 ethnographically documented groups of hunter-gatherers. / GPL-3 noarch
r-binmto 0.0_7 Asymptotic simultaneous confidence intervals for comparison of many treatments with one control, for the difference of binomial proportions, allows for Dunnett-like-adjustment, Bonferroni or unadjusted intervals. Simulation of power of the above interval methods, approximate calculation of any-pair-power, and sample size iteration based on approximate any-pair power. Exact conditional maximum test for many-to-one comparisons to a control. / GPL-2 noarch
r-binnonnor 1.5.1 Generation of multiple binary and continuous non-normal variables simultaneously given the marginal characteristics and association structure based on the methodology proposed by Demirtas et al. (2012) <DOI:10.1002/sim.5362>. / GPL-2 | GPL-3 noarch
r-binnor 2.3.1 Generating multiple binary and normal variables simultaneously given marginal characteristics and association structure based on the methodology proposed by Demirtas and Doganay (2012) <DOI:10.1080/10543406.2010.521874>. / GPL-2 noarch
r-binom 1.1_1 Constructs confidence intervals on the probability of success in a binomial experiment via several parameterizations / GPL-3 linux-64, osx-64, win-64
r-binomialcftp 1.0 Binomial random numbers are generated via the perfect sampling algorithm. At each iteration dual markov chains are generated and coalescence is checked. In case coalescence occurs, the resulting number is outputted. In case not, then the algorithm is restarted from T(t)=2*T(t) until coalescence occurs. / GPL-2 noarch
r-binomlogit 1.2 The R package contains different MCMC schemes to estimate the regression coefficients of a binomial (or binary) logit model within a Bayesian framework: a data-augmented independence MH-sampler, an auxiliary mixture sampler and a hybrid auxiliary mixture (HAM) sampler. All sampling procedures are based on algorithms using data augmentation, where the regression coefficients are estimated by rewriting the logit model as a latent variable model called difference random utility model (dRUM). / GPL-3 noarch
r-binomsamsize 0.1_5 A suite of functions to compute confidence intervals and necessary sample sizes for the parameter p of the Bernoulli B(p) distribution under simple random sampling or under pooled sampling. Such computations are e.g. of interest when investigating the incidence or prevalence in populations. The package contains functions to compute coverage probabilities and coverage coefficients of the provided confidence intervals procedures. Sample size calculations are based on expected length. / GPL-3 linux-64, osx-64, win-64
r-binostics 0.1.2 Calculates graph theoretic scagnostics. Scagnostics describe various measures of interest for pairs of variables, based on their appearance on a scatterplot. They are useful tool for discovering interesting or unusual scatterplots from a scatterplot matrix, without having to look at every individual plot. / GPL-3 linux-64, osx-64, win-64
r-binr 1.1 Implementation of algorithms for cutting numerical values exhibiting a potentially highly skewed distribution into evenly distributed groups (bins). This functionality can be applied for binning discrete values, such as counts, as well as for discretization of continuous values, for example, during generation of features used in machine learning algorithms. / Apache License (== 2.0) noarch
r-binseqtest 1.0.3 For a series of binary responses, create stopping boundary with exact results after stopping, allowing updating for missing assessments. / GPL-3 noarch
r-binst 0.2.1 Various supervised and unsupervised binning tools including using entropy, recursive partition methods and clustering. / MIT noarch
r-bio.infer 1.3_3 Imports benthic count data, reformats this data, and computes environmental inferences from this data. / GPL-2 noarch
r-bio3d 2.3_4 Utilities to process, organize and explore protein structure, sequence and dynamics data. Features include the ability to read and write structure, sequence and dynamic trajectory data, perform sequence and structure database searches, data summaries, atom selection, alignment, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, normal mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal mode and molecular dynamics data. In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data. Please refer to the URLs below for more information. / GPL-2 linux-64, osx-64, win-64
r-biocircos 0.3.4 Implement in ‘R’ interactive Circos-like visualizations of genomic data, to map information such as genetic variants, genomic fusions and aberrations to a circular genome, as proposed by the ‘JavaScript’ library ‘BioCircos.js’, based on the ‘JQuery’ and ‘D3’ technologies. The output is by default displayed in stand-alone HTML documents or in the ‘RStudio’ viewer pane. Moreover it can be integrated in ‘R Markdown’ documents and ‘Shiny’ applications. / GPL-2 noarch
r-biocmanager 1.30.4 A convenient tool to install and update Bioconductor packages. / Artistic-2.0 noarch
r-biodem 0.4 The Biodem package provides a number of functions for Biodemographic analysis. / GPL-2 noarch
r-biodry 0.6 Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity. Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions. / GPL-3 noarch
r-bioftf 1.2_0 The main drawback of the most common biodiversity indices is that different measures may lead to different rankings among communities. This instrument overcomes this limit using some functional tools with the diversity profiles. In particular, the derivatives, the curvature, the radius of curvature, the arc length, and the surface area are proposed. The goal of this method is to interpret in detail the diversity profiles and obtain an ordering between different ecological communities on the basis of diversity. In contrast to the typical indices of diversity, the proposed method is able to capture the multidimensional aspect of biodiversity, because it takes into account both the evenness and the richness of the species present in an ecological community. / GPL-2 noarch
r-biogas 1.10.3 High- and low-level functions for processing biogas data and predicting biogas production. Molar mass and calculated oxygen demand (COD’) can be determined from a chemical formula. Measured gas volume can be corrected for water vapor and to (possibly user-defined) standard temperature and pressure. Gas quantity can be converted between volume, mass, and moles. Gas composition, cumulative production, or other variables can be interpolated to a specified time. Cumulative biogas and methane production (and rates) can be calculated using volumetric, manometric, or gravimetric methods for any number of reactors. With cumulative methane production data and data on reactor contents, biochemical methane potential (BMP) can be calculated and summarized, including subtraction of the inoculum contribution and normalization by substrate mass. Cumulative production and production rates can be summarized in several different ways (e.g., omitting normalization) using the same function. Biogas quantity and composition can be predicted from substrate composition and additional, optional data. Lastly, inoculum and substrate mass can be determined for planning BMP experiments. / GPL-2 noarch
r-biom.utils 0.9 Provides utilities to facilitate import, export and computation with the BIOM (Biological Observation Matrix) format (http://biom-format.org). / BSD_2_clause noarch
r-bionetdata 1.0.1 Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured. / GPL-2 noarch
r-biopn 1.2.0 bioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction networks. Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions are defined. For deterministic solutions, bioPN creates the associated system of differential equations on the fly, and solves it with a Runge Kutta Dormand Prince 45 explicit algorithm. For stochastic solutions, bioPN offers variants of Gillespie algorithm, or SSA. For hybrid deterministic/stochastic, it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions. bioPN algorithms are developed in C to achieve adequate performance. / GPL-2 linux-64, osx-64, win-64
r-bios2cor 1.2 Utilities for computation and analysis of correlation/co-variation in multiple sequence alignments and in side chain motions during molecular dynamics simulations. Features include the computation of correlation/co-variation scores using a variety of scoring functions between either sequence positions in alignments or side chain dihedral angles in molecular dynamics simulations and to analyze the correlation/co-variation matrix through a variety of tools including network representation and principal components analysis. In addition, several utility functions are based on the R graphical environment to provide friendly tools for help in data interpretation. Examples of sequence co-variation analysis and utility tools are provided in: Pele J, Moreau M, Abdi H, Rodien P, Castel H, Chabbert M. (2014) <doi:10.1002/prot.24570>. This work was supported by the French National Research Agency (Grant number: ANR-11-BSV2-026). / GPL-2 noarch
r-biotic 0.1.2 Calculates a range of UK freshwater invertebrate biotic indices including BMWP, Whalley, WHPT, Habitat-specific BMWP, AWIC, LIFE and PSI. / GPL-3 noarch
r-birk 2.1.2 Collection of tools to make R more convenient. Includes tools to summarize data using statistics not available with base R and manipulate objects for analyses. / GPL-3 noarch
r-birtr 1.0.0 R functions for The Basics of Item Response Theory Using R by Frank B. Baker and Seock-Ho Kim (Springer, 2017, ISBN-13: 978-3-319-54204-1) including iccplot(), icccal(), icc(), iccfit(), groupinv(), tcc(), ability(), tif(), and rasch(). For example, iccplot() plots an item characteristic curve under the two-parameter logistic model. / GPL-2 noarch
r-bisect 0.9.0 An implementation of Bisect, a method for inferring cell type composition of samples based on methylation sequencing data (Whole Genome Bisulfite Sequencing and Reduced Representation Sequencing). The method is specifically tailored for sequencing data, and therefore works better than methods developed for methylation arrays. It contains a supervised mode that requires a reference (the methylation probabilities in the pure cell types), and a semi-supervised mode, that requires cell counts for a subset of the samples, but does not require a reference. / GPL-3 noarch
r-bisectr 0.1.0 Tools to find bad commits with git bisect. See https://github.com/wch/bisectr for examples and test script templates. / GPL-2 noarch
r-bisrna 0.2.2 Bisulfite-treated RNA non-conversion in a set of samples is analysed as follows : each sample’s non-conversion distribution is identified to a Poisson distribution. P-values adjusted for multiple testing are calculated in each sample. Combined non-conversion P-values and standard errors are calculated on the intersection of the set of samples. For further details, see C Legrand, F Tuorto, M Hartmann, R Liebers, D Jakob, M Helm and F Lyko (2017) <doi:10.1101/gr.210666.116>. / GPL-2 noarch
r-bit 1.1_14 bitmapped vectors of booleans (no NAs), coercion from and to logicals, integers and integer subscripts; fast boolean operators and fast summary statistics. With ‘bit’ vectors you can store true binary booleans {FALSE,TRUE} at the expense of 1 bit only, on a 32 bit architecture this means factor 32 less RAM and ~ factor 32 more speed on boolean operations. Due to overhead of R calls, actual speed gain depends on the size of the vector: expect gains for vectors of size > 10000 elements. Even for one-time boolean operations it can pay-off to convert to bit, the pay-off is obvious, when such components are used more than once. Reading from and writing to bit is approximately as fast as accessing standard logicals - mostly due to R’s time for memory allocation. The package allows to work with pre-allocated memory for return values by calling .Call() directly: when evaluating the speed of C-access with pre-allocated vector memory, coping from bit to logical requires only 70% of the time for copying from logical to logical; and copying from logical to bit comes at a performance penalty of 150%. the package now contains further classes for representing logical selections: ‘bitwhich’ for very skewed selections and ‘ri’ for selecting ranges of values for chunked processing. All three index classes can be used for subsetting ‘ff’ objects (ff-2.1-0 and higher). / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
r-bit64 0.9_7 Package ‘bit64’ provides serializable S3 atomic 64bit (signed) integers. These are useful for handling database keys and exact counting in -2^63. WARNING: do not use them as replacement for 32bit integers, integer64 are not supported for subscripting by R-core and they have different semantics when combined with double, e.g. integer64 double => integer64. Class integer64 can be used in vectors, matrices, arrays and data.frames. Methods are available for coercion from and to logicals, integers, doubles, characters and factors as well as many elementwise and summary functions. Many fast algorithmic operations such as ‘match’ and ‘order’ support inter- active data exploration and manipulation and optionally leverage caching. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
r-bitops 1.0_6 Functions for bitwise operations on integer vectors. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
r-bitrina 1.3 Provides methods for the binarization and trinarization of one-dimensional data and some visualization functions. / Artistic-2.0 linux-64, osx-64, win-64
r-bitrugs 0.1 MCMC methods to estimate transmission dynamics and infection routes in hospitals using genomic sampling data. / GPL-3 linux-64, osx-64, win-64
r-bitsqueezr 0.1.0 Provides a implementation of floating-point quantization algorithms for use in precision-preserving compression, similar to the approach taken in the ‘netCDF operators’ (NCO) software package and described in Zender (2016) <doi:10.5194/gmd-2016-63>. / GPL-3 linux-64, osx-64, win-64
r-bivariate.pareto 1.0.2 Perform competing risks analysis under bivariate Pareto models. See Shih et al. (2018) <doi:10.1080/03610926.2018.1425450> for details. / GPL-2 noarch
r-bivarp 1.0 Parameter estimation of bivariate distribution functions modeled as a Archimedean copula function. The input data may contain values from right censored. Used marginal distributions are two-parameter. Methods for density, distribution, survival, random sample generation. / GPL-3 noarch
r-bivarripower 1.2 Implements sample size calculations for bivariate random intercept regression model that are described in Comulada and Weiss (2010) / GPL-2 noarch
r-bivgeo 2.0.1 Computes the joint probability mass function (pmf), the joint cumulative function (cdf), the joint survival function (sf), the correlation coefficient, the covariance, the cross-factorial moment and generate random deviates for the Basu-Dhar bivariate geometric distribution as well the joint probability mass, cumulative and survival function assuming the presence of a cure fraction given by the standard bivariate mixture cure fraction model. The package also computes the estimators based on the method of moments. / GPL-2 noarch
r-bivgeom 1.0 Implements Roy’s bivariate geometric model (Roy (1993) <doi:10.1006/jmva.1993.1065>): joint probability mass function, distribution function, survival function, random generation, parameter estimation, and more. / GPL-3 noarch
r-bivrec 1.0.0 Alternating recurrent event data arise frequently in biomedical and social sciences where 2 types of events such as hospital admissions and discharge occur alternatively over time. As such we implement a collection of non-parametric and semiparametric methods to analyze such data. The main functions are biv.rec.fit() and biv.rec.np(). Use biv.rec.fit() for estimation of covariate effects on the two alternating event gap times (xij and yij) using semiparametric methods. The method options are Lee.et.al and Chang. Use biv.rec.np() for estimation of the joint cumulative distribution function (cdf) for the two alternating events gap times (xij and yij) as well as the marginal survival function for type I gap times (xij) and the conditional cdf of the type II gap times (yij) given an interval of type I gap times (xij) in a non-parametric fashion. The package also provides options to simulate and visualize the data and results of analysis. / GPL-3 linux-64, osx-64, win-64
r-bizdays 1.0.6 Business days calculations based on a list of holidays and nonworking weekdays. Quite useful for fixed income and derivatives pricing. / MIT noarch
r-bkpc 1.0.1 Bayesian kernel projection classifier is a nonlinear multicategory classifier which performs the classification of the projections of the data to the principal axes of the feature space. A Gibbs sampler is implemented to find the posterior distributions of the parameters. / GPL-2 linux-64, osx-64, win-64
r-blakerci 1.0_6 Fast and accurate calculation of Blaker’s binomial and Poisson confidence limits (and some related stuff). / GPL-3 noarch
r-blandaltmanleh 0.3.1 Bland-Altman Plots using either base graphics or ggplot2, augmented with confidence intervals, with detailed return values and a sunflowerplot option for data with ties. / GPL-3 noarch
r-blatr 1.0.1 A wrapper around the ‘Blat’ command line SMTP mailer for Windows. ‘Blat’ is public domain software, but be sure to read the license before use. It can be found at the Blat website http://www.blat.net. / MIT win-64
r-blendedlink 1.0 A new link function that equals one specified link function up to a cutover then a linear rescaling of another specified link function. For use in glm() or glm2(). The intended use is in binary regression, in which case the first link should be set to log and the second to logit. This ensures that fitted probabilities are between 0 and 1 and that exponentiated coefficients can be interpreted as relative risks for probabilities up to the cutoff. / GPL-2 | GPL-3 noarch
r-blendstat 1.0.2 Performs a joint analysis of experiments with mixtures and random effects, taking on a process variable represented by a covariable. / GPL-2 noarch
r-blin 0.0.1 Estimate influence networks from longitudinal bipartite relational data, where the longitudinal relations are continuous. The outputs are estimates of weighted influence networks among each actor type in the data set. The generative model is the Bipartite Longitudinal Influence Network (BLIN) model, a linear autoregressive model for these type of data. The supporting paper is ``Inferring Influence Networks from Longitudinal Bipartite Relational Data’‘, which is in preparation by the same authors. The model may be estimated using maximum likelihood methods and Bayesian methods. For more detail on methods, see Marrs et. al. <arXiv:1809.03439>. / MIT noarch
r-blm 2013.2.4.4 Implements regression models for binary data on the absolute risk scale. These models are applicable to cohort and population-based case-control data. / GPL-2 noarch
r-blme 1.0_4 Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting. Extends ‘lme4’ by Douglas Bates, Martin Maechler, Ben Bolker, and Steve Walker. / GPL-2 noarch
r-blmodel 1.0.2 Posterior distribution in the Black-Litterman model is computed from a prior distribution given in the form of a time series of asset returns and a continuous distribution of views provided by the user as an external function. / GNU General Public License version 3 noarch
r-blob 1.1.1 R’s raw vector is useful for storing a single binary object. What if you want to put a vector of them in a data frame? The ‘blob’ package provides the blob object, a list of raw vectors, suitable for use as a column in data frame. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-blockfest 1.6 An R implementation of an extension of the ‘BayeScan’ software (Foll, 2008) <DOI:10.1534/genetics.108.092221> for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing one or more SNPs that evolved under directional selection. / GPL-2 linux-64, osx-64, win-64
r-blockforest 0.2.3 A random forest variant ‘block forest’ (‘BlockForest’) tailored to the prediction of binary, survival and continuous outcomes using block-structured covariate data, for example, clinical covariates plus measurements of a certain omics data type or multi-omics data, that is, data for which measurements of different types of omics data and/or clinical data for each patient exist. Examples of different omics data types include gene expression measurements, mutation data and copy number variation measurements. Block forest are presented in Hornung & Wright (2019). The package includes four other random forest variants for multi-omics data: ‘RandomBlock’, ‘BlockVarSel’, ‘VarProb’, and ‘SplitWeights’. These were also considered in Hornung & Wright (2019), but performed worse than block forest in their comparison study based on 20 real multi-omics data sets. Therefore, we recommend to use block forest (‘BlockForest’) in applications. The other random forest variants can, however, be consulted for academic purposes, for example, in the context of further methodological developments. Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. <doi:10.1186/s12859-019-2942-y>. / GPL-3 linux-64, osx-64, win-64
r-blockmatrix 1.0 Some elementary matrix algebra tools are implemented to manage block matrices or partitioned matrix, i.e. matrix of matrices (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here defined as a new S3 object. In this package, some methods for matrix object are rewritten for blockmatrix object. New methods are implemented. This package was created to solve equation systems with block matrices for the analysis of environmental vector time series . Bugs/comments/questions/collaboration of any kind are warmly welcomed. / GPL-2 noarch
r-blockmessage 1.0 Creates strings that show a text message in 8 by 8 block letters / GPL-2 noarch
r-blockmodeling 0.3.4 This is primarily meant as an implementation of generalized blockmodeling for valued networks. In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted: Žiberna (2007)<doi:10.1016/j.socnet.2006.04.002>, Žiberna (2008)<doi:10.1080/00222500701790207>, Žiberna (2014)<doi:10.1016/j.socnet.2014.04.002>. / GPL-2 linux-64, osx-64, win-64
r-blockmodels 1.1.1 Latent and Stochastic Block Model estimation by a Variational EM algorithm. Various probability distribution are provided (Bernoulli, Poisson…), with or without covariates. / LGPL-2.1 linux-64, osx-64, win-64
r-blockrand 1.3 Create randomizations for block random clinical trials. Can also produce a pdf file of randomization cards. / GPL-2 noarch
r-blocksdesign 3.5 Constructs D-optimal or near D-optimal nested and crossed block designs for unstructured or general factorial treatment designs. The treatment design, if required, is found from a model matrix design formula and can be added sequentially, if required. The block design is found from a defined set of block factors and is conditional on the defined treatment design. The block factors are added in sequence and each added block factor is optimized conditional on all previously added block factors. The block design can have repeated nesting down to any required depth of nesting with either simple nested blocks or a crossed blocks design at each level of nesting. Outputs include a table showing the allocation of treatments to blocks and tables showing the achieved D-efficiency factors for each block and treatment design. / GPL-2 noarch
r-blr 1.5 Bayesian Linear Regression. / GPL-2 linux-64, osx-64, win-64
r-blrpm 1.0 Due to a limited availability of observed high-resolution precipitation records with adequate length, simulations with stochastic precipitation models are used to generate series for subsequent studies [e.g. Khaliq and Cunmae, 1996, <doi:10.1016/0022-1694(95)02894-3>, Vandenberghe et al., 2011, <doi:10.1029/2009WR008388>]. This package contains an R implementation of the original Bartlett-Lewis rectangular pulse model (BLRPM), developed by Rodriguez-Iturbe et al. (1987) <doi:10.1098/rspa.1987.0039>. It contains a function for simulating a precipitation time series based on storms and cells generated by the model with given or estimated model parameters. Additionally BLRPM parameters can be estimated from a given or simulated precipitation time series. The model simulations can be plotted in a three-layer plot including an overview of generated storms and cells by the model (which can also be plotted individually), a continuous step-function and a discrete precipitation time series at a chosen aggregation level. / GPL-2 noarch
r-blsapi 0.2.1 Allows users to request data for one or multiple series through the U.S. Bureau of Labor Statistics API. Users provide parameters as specified in <https://www.bls.gov/developers/api_signature.htm> and the function returns a JSON string. / GPL-2 noarch
r-blsm 0.1.0 Provides a Bayesian latent space model for complex networks, either weighted or unweighted. Given an observed input graph, the estimates for the latent coordinates of the nodes are obtained through a Bayesian MCMC algorithm. The overall likelihood of the graph depends on a fundamental probability equation, which is defined so that ties are more likely to exist between nodes whose latent space coordinates are close. The package is mainly based on the model by Hoff, Raftery and Handcock (2002) <doi:10.1198/016214502388618906> and contains some extra features (e.g., removal of the Procrustean step, weights implemented as coefficients of the latent distances, 3D plots). The original code related to the above model was retrieved from <https://www.stat.washington.edu/people/pdhoff/Code/hoff_raftery_handcock_2002_jasa/>. Users can inspect the MCMC simulation, create and customize insightful graphical representations or apply clustering techniques. / GPL-2 linux-64, osx-64, win-64
r-bmamevt 1.0.3 Toolkit for Bayesian estimation of the dependence structure in multivariate extreme value parametric models. / GPL-2 linux-64, osx-64, win-64
r-bmass 1.0.3 Multivariate tool for analyzing genome-wide association study results in the form of univariate summary statistics. The goal of ‘bmass’ is to comprehensively test all possible multivariate models given the phenotypes and datasets provided. Multivariate models are determined by assigning each phenotype to being either Unassociated (U), Directly associated (D) or Indirectly associated (I) with the genetic variant of interest. Test results for each model are presented in the form of Bayes factors, thereby allowing direct comparisons between models. The underlying framework implemented here is based on the modeling developed in A Unified Framework for Association Analysis with Multiple Related Phenotypes, M. Stephens (2013) <doi:10.1371/journal.pone.0065245>. / GPL-3 noarch
r-bmix 0.6 This is a bare-bones implementation of sampling algorithms for a variety of Bayesian stick-breaking (marginally DP) mixture models, including particle learning and Gibbs sampling for static DP mixtures, particle learning for dynamic BAR stick-breaking, and DP mixture regression. The software is designed to be easy to customize to suit different situations and for experimentation with stick-breaking models. Since particles are repeatedly copied, it is not an especially efficient implementation. / GPL-2 linux-64, osx-64, win-64
r-bmixture 1.3 Provides statistical tools for Bayesian estimation for finite mixture of distributions, mainly mixture of Gamma, Normal and t-distributions. The package is implemented the recent improvements in Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) <doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012) <doi:10.1080/03610918.2011.588358>. / GPL-2 linux-64, osx-64, win-64
r-bmk 1.0 MCMC diagnostic package that contains tools to diagnose convergence as well as to evaluate sensitivity studies, Includes summary functions which output mean, median, 95percentCI, Gelman & Rubin diagnostics and the Hellinger distance based diagnostics, Also contains functions to determine when an MCMC chain has converged via Hellinger distance, A function is also provided to compare outputs from identically dimensioned chains for determining sensitivy to prior distribution assumptions / GPL-2 noarch
r-bmp 0.3 Reads Windows BMP format images. Currently limited to 8 bit greyscale images and 24,32 bit (A)RGB images. Pure R implementation without external dependencies. / GPL-2 noarch
r-bmrbr 0.2.0 Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for studying molecular configuration, conformation, and dynamics, especially those of biomacromolecules such as proteins. Biological Magnetic Resonance Data Bank (‘BMRB’) is a repository for Data from NMR Spectroscopy on Proteins, Peptides, Nucleic Acids, and other Biomolecules. Currently, ‘BMRB’ offers an R package ‘RBMRB’ to fetch data, however, it doesn’t easily offer individual data file downloading and storing in a local directory. When using ‘RBMRB’, the data will stored as an R object, which fundamentally hinders the NMR researches to access the rich information from raw data, for example, the metadata. Here, ‘BMRBr’ File Downloader (‘BMRBr’) offers a more fundamental, low level downloader, which will download original deposited .str format file. This type of file contains information such as entry title, authors, citation, protein sequences, and so on. Many factors affect NMR experiment outputs, such as temperature, resonance sensitivity and etc., approximately 40% of the entries in the ‘BMRB’ have chemical shift accuracy problems [1,2] Unfortunately, current reference correction methods are heavily dependent on the availability of assigned protein chemical shifts or protein structure. This is my current research project is going to solve, which will be included in the future release of the package. The current version of the package is sufficient and robust enough for downloading individual ‘BMRB’ data file from the ‘BMRB’ database <http://www.bmrb.wisc.edu>. The functionalities of this package includes but not limited: * To simplifies NMR researches by combine data downloading and results analysis together. * To allows NMR data reaches a broader audience that could utilize more than just chemical shifts but also metadata. * To offer reference corrected data for entries without assignment or structure information (future release). Reference: [1] E.L. Ulrich, H. Akutsu, J.F. Doreleijers, Y. Harano, Y.E. Ioannidis, J. Lin, et al., BioMagResBank, Nucl. Acids Res. 36 (2008) D402–8. <doi:10.1093/nar/gkm957>. [2] L. Wang, H.R. Eghbalnia, A. Bahrami, J.L. Markley, Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications, J. Biomol. NMR. 32 (2005) 13–22. <doi:10.1007/s10858-005-1717-0>. / GPL-3 noarch
r-bmrv 1.32 Provides two Bayesian models for detecting the association between rare genetic variants and a trait that can be continuous, ordinal or binary. Bayesian latent variable collapsing model (BLVCM) detects interaction effect and is dedicated to twin design while it can also be applied to independent samples. Hierarchical Bayesian multiple regression model (HBMR) incorporates genotype uncertainty information and can be applied to either independent or family samples. Furthermore, it deals with continuous, binary and ordinal traits. / GPL-2 linux-64, osx-64, win-64
r-bms 0.3.4 Bayesian model averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, flexible and hyper-g priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. / Artistic-2.0 noarch
r-bndatagenerator 1.0 Data generator based on Bayesian network model / GPL-2 noarch
r-bnlearn 4.5 Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from <http://www.bnlearn.com>. / GPL-2 linux-64, osx-64, win-64
r-bnn 1.0.2 Perform Bayesian variable selection for high-dimensional nonlinear systems and also can be used to test nonlinearity for a general regression problem. The computation can be accelerated using multiple CPUs. You can refer to <doi:10.1080/01621459.2017.1409122> for more detail. / GPL-2 linux-64, osx-64, win-64
r-bnormnlr 1.0 Implementation of Bayesian estimation in normal heteroscedastic nonlinear regression Models following Cepeda-Cuervo, (2001). / GPL-2 noarch
r-bnpmr 1.2 Implements the Bayesian nonparametric monotonic regression method described in Bornkamp & Ickstadt (2009), Biometrics, 65, 198-205. / GPL-3 linux-64, osx-64, win-64
r-bnpsd 1.1.1 The Pritchard-Stephens-Donnelly (PSD) admixture model has k intermediate subpopulations from which n individuals draw their alleles dictated by their individual-specific admixture proportions. The BN-PSD model additionally imposes the Balding-Nichols (BN) allele frequency model to the intermediate populations, which therefore evolved independently from a common ancestral population T with subpopulation-specific FST (Wright’s fixation index) parameters. The BN-PSD model can be used to yield complex population structures. Method described in Ochoa and Storey (2016) <doi:10.1101/083923>. / GPL-3 noarch
r-bnptsclust 2.0 Performs the algorithm for time series clustering described in Nieto-Barajas and Contreras-Cristan (2014). / GPL-2 noarch
r-bnsl 0.1.4 From a given data frame, this package learns its Bayesian network structure based on a selected score. / GPL-2 linux-64, osx-64, win-64
r-bnstruct 1.0.6 Bayesian Network Structure Learning from Data with Missing Values. The package implements the Silander-Myllymaki complete search, the Max-Min Parents-and-Children, the Hill-Climbing, the Max-Min Hill-climbing heuristic searches, and the Structural Expectation-Maximization algorithm. Available scoring functions are BDeu, AIC, BIC. The package also implements methods for generating and using bootstrap samples, imputed data, inference. / GPL-2 linux-64, osx-64, win-64
r-bnviewer 0.1.4 Bayesian networks provide an intuitive framework for probabilistic reasoning and its graphical nature can be interpreted quite clearly. Graph based methods of machine learning are becoming more popular because they offer a richer model of knowledge that can be understood by a human in a graphical format. The ‘bnviewer’ is an R Package that allows the interactive visualization of Bayesian Networks. The aim of this package is to improve the Bayesian Networks visualization over the basic and static views offered by existing packages. / MIT noarch
r-boa 1.1.8_2 A menu-driven program and library of functions for carrying out convergence diagnostics and statistical and graphical analysis of Markov chain Monte Carlo sampling output. / GPL-2 noarch
r-boardgames 1.0.0 Tools for constructing board/grid based games, as well as readily available game(s) for your entertainment. / GPL-2 noarch
r-bodenmiller 0.1 This data package contains a subset of the Bodenmiller et al, Nat Biotech 2012 dataset for testing single cell, high dimensional analysis and visualization methods. / CC0 noarch
r-boilerpiper 1.3 Generic Extraction of main text content from HTML files; removal of ads, sidebars and headers using the boilerpipe (http://code.google.com/p/boilerpipe/) Java library. The extraction heuristics from boilerpipe show a robust performance for a wide range of web site templates. / Apache License (== 2.0) noarch
r-bolstad 0.2_40 A set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2. / GPL-2 noarch
r-bolstad2 1.0_28 A set of R functions and data sets for the book Understanding Computational Bayesian Statistics, Bolstad, W.M. (2009), John Wiley & Sons ISBN 978-0470046098 / GPL-2 noarch
r-boltzmm 0.1.4 Provides probability computation, data generation, and model estimation for fully-visible Boltzmann machines. It follows the methods described in Nguyen and Wood (2016a) <doi:10.1162/NECO_a_00813> and Nguyen and Wood (2016b) <doi:10.1109/TNNLS.2015.2425898>. / GPL-3 linux-64, osx-64, win-64
r-bondvaluation 0.1.0 Analysis of large datasets of fixed coupon bonds, allowing for irregular first and last coupon periods and various day count conventions. With this package you can compute the yield to maturity, the modified and MacAulay durations and the convexity of fixed-rate bonds. It provides the function AnnivDates, which can be used to evaluate the quality of the data and return time-invariant properties and temporal structure of a bond. / GPL-3 linux-64, osx-64, win-64
r-bookdown 0.13 Output formats and utilities for authoring books and technical documents with R Markdown. / GPL-3 noarch
r-boolnet 2.1.5 Provides methods to reconstruct and generate synchronous, asynchronous, probabilistic and temporal Boolean networks, and to analyze and visualize attractors in Boolean networks. / Artistic-2.0 linux-64, osx-64, win-64
r-boom 0.9.1 A C library for Bayesian modeling, with an emphasis on Markov chain Monte Carlo. Although boom contains a few R utilities (mainly plotting functions), its primary purpose is to install the BOOM C library on your system so that other packages can link against it. / LGPL-2.1 linux-64, osx-64, win-64
r-boomspikeslab 1.1.1 Spike and slab regression a la McCulloch and George (1997). / LGPL-2.1 linux-64, osx-64, win-64
r-boostr 1.0.0 boostr provides a modular framework that return the focus of ensemble learning back to ‘learning’ (instead of programming). / GPL-2 noarch
r-boot 1.3_20 Functions and datasets for bootstrapping from the book Bootstrap Methods and Their Application by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S. / Unlimited linux-32, linux-64, noarch, osx-64, win-32, win-64
r-bootes 1.2 Calculate robust measures of effect sizes using the bootstrap. / GPL-2 noarch
r-bootlr 1.0.2 Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology, using the method of Marill et al. (2015) <doi:10.1177/0962280215592907>. It is particularly useful when the sensitivity or specificity in the sample is 100%. Note that this does not perform the test on nested models–for that, see ‘epicalc::lrtest’. / LGPL-2.1 noarch
r-bootmrmr 0.1 Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications). / GPL-2 noarch
r-bootpr 0.60 Bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series / GPL-2 noarch
r-bootres 1.2.4 Bootstrapped correlation and response functions are widely used in dendrochronology to calibrate tree-ring proxy data against multiple instrumental time series of climatic variables. With this package, we provide functionality similar to DENDROCLIM2002 (Biondi and Waikul (2004) <10.1016/j.cageo.2003.11.004>). A full description with examples is given in Zang and Biondi (2013) <10.1016/j.dendro.2012.08.001>. / GPL-3 noarch
r-bootruin 1.2_4 We provide a framework for testing the probability of ruin in the classical (compound Poisson) risk process. It also includes some procedures for assessing and comparing the performance between the bootstrap test and the test using asymptotic normality. / AGPL-3 linux-64, osx-64, win-64
r-bootspecdens 3.0 Bootstrap for testing the hypothesis that the spectral densities of a number m, m>=2, not necessarily independent time series are equal / GPL-2 noarch
r-bootstepaic 1.2_0 Model selection by bootstrapping the stepAIC() procedure. / GPL-2 noarch
r-bootstrap 2019.6 Software (bootstrap, cross-validation, jackknife) and data for the book An Introduction to the Bootstrap by B. Efron and R. Tibshirani, 1993, Chapman and Hall. This package is primarily provided for projects already based on it, and for support of the book. New projects should preferentially use the recommended package boot. / BSD_3_clause linux-64, osx-64, win-64
r-boottol 2.0 Used to create bootstrap tolerance levels for the Kolmogorov-Smirnov (KS) statistic, the area under receiver operator characteristic curve (AUROC) statistic, and the Gini coefficient for each score cutoff. Also provides a bootstrap alternative to the Vasicek test. / GPL-2 noarch
r-bor 0.1.0 Transforms focal observations’ data, where different types of social interactions can be recorded by multiple observers, into asymmetric data matrices. Each cell in these matrices provides counts on the number of times a specific type of social interaction was initiated by the row subject and directed to the column subject. / GPL-3 noarch
r-borrowr 0.1.0 Estimate population average treatment effects from a primary data source with borrowing from supplemental sources. Causal estimation is done with either a Bayesian linear model or with Bayesian additive regression trees (BART) to adjust for confounding. Borrowing is done with multisource exchangeability models (MEMs). For information on BART, see Chipman, George, & McCulloch (2010) <doi:10.1214/09-AOAS285>. For information on MEMs, see Kaizer, Koopmeiners, & Hobbs (2018) <doi10.1093/biostatistics/kxx031>. / GPL-3 linux-64, osx-64, win-64
r-boruta 6.0.0 An all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes’ importance with importance achievable at random, estimated using their permuted copies (shadows). / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-bosonsampling 0.1.1 Classical Boson Sampling using the algorithm of Clifford and Clifford (2017) <arXiv:1706.01260>. Also provides functions for generating random unitary matrices, evaluation of matrix permanents (both real and complex) and evaluation of complex permanent minors. / GPL-2 linux-64, osx-64, win-64
r-boussinesq 1.0.3 This package is a collection of R functions implemented from published and available analytic solutions for the One-Dimensional Boussinesq Equation (ground-water). In particular, the function beq.lin is the analytic solution of the linearized form of Boussinesq Equation between two different head-based boundary (Dirichlet) conditions; beq.song is the non-linear power-series analytic solution of the motion of a wetting front over a dry bedrock (Song at al, 2007, see complete reference on function documentation). Bugs/comments/questions/collaboration of any kind are warmly welcomed. / GPL-2 noarch
r-boxoffice 1.2.2 Download daily box office information (how much each movie earned in theaters) using data from either Box Office Mojo (<http://www.boxofficemojo.com/>) or The Numbers (<http://www.the-numbers.com/>). / GPL-3 noarch
r-boxplotdbl 1.3.0 Correlation chart of two set (x and y) of data. Using Quartiles with boxplot style. Visualize the effect of factor. / MIT (FOSS) noarch
r-bpa 0.1.1 Run basic pattern analyses on character sets, digits, or combined input containing both characters and numeric digits. Useful for data cleaning and for identifying columns containing multiple or nonstandard formats. / GPL-2 noarch
r-bpbounds 0.1.3 Implementation of the nonparametric bounds for the average causal effect under an instrumental variable model by Balke and Pearl (Bounds on Treatment Effects from Studies with Imperfect Compliance, JASA, 1997, 92, 439, 1171-1176). The package can calculate bounds for a binary outcome, a binary treatment/phenotype, and an instrument with either 2 or 3 categories. The package implements bounds for situations where these 3 variables are measured in the same dataset (trivariate data) or where the outcome and instrument are measured in one study and the treatment/phenotype and instrument are measured in another study (bivariate data). / GPL-3 noarch
r-bpcp 1.3.4 Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time. Especially important for small sample sizes or heavily censored data. Includes mid-p options. / GPL-2 noarch
r-bpeaks 1.2 bPeaks is a simple approach to identify transcription factor binding sites from ChIP-seq data. Our general philosophy is to provide an easy-to-use tool, well-adapted for small eukaryotic genomes (< 20 Mb). bPeaks uses a combination of 4 cutoffs (T1, T2, T3 and T4) to mimic good peak properties as described by biologists who visually inspect the ChIP-seq data on a genome browser. For yeast genomes, bPeaks calculates the proportion of peaks that fall in promoter sequences. These peaks are good candidates as transcription factor binding sites. / GPL-3 noarch
r-bpkde 1.0_7 Nonparametric multivariate kernel density estimation using a back-projected kernel. / GPL-2 linux-64, osx-64, win-64
r-bpp 1.0.0 Implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio. / GPL-2 noarch
r-bqtl 1.0_32 QTL mapping toolkit for inbred crosses and recombinant inbred lines. Includes maximum likelihood and Bayesian tools. / GPL-2 linux-64, osx-64, win-64
r-bracer 1.0.1 Performs brace expansions on strings. Made popular by Unix shells, brace expansion allows users to quickly generate certain character vectors by taking a single string and (recursively) expanding the comma-separated lists and double-period-separated integer and character sequences enclosed within braces in that string. The double-period-separated numeric integer expansion also supports padding the resulting numbers with zeros. / MIT noarch
r-bradleyterry2 1.0_9 Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-braidrm 0.71 Contains functions for evaluating, analyzing, and fitting combined action dose response surfaces with the Bivariate Response to Additive Interacting Dose (BRAID) model of combined action. / GPL-3 noarch
r-branching 0.9.4 Simulation and parameter estimation of multitype Bienayme - Galton - Watson processes. / GPL-2 linux-64, osx-64, win-64
r-brandwatchr 0.3.0 Interact with the ‘Brandwatch’ API <https://developers.brandwatch.com/docs>. Allows you to authenticate to the API and obtain data for projects, queries, query groups tags and categories. Also allows you to directly obtain mentions and aggregate data for a specified query or query group. / MIT noarch
r-brant 0.2_0 Tests the parallel regression assumption for ordinal logit models generated with the function polr() from the package ‘MASS’. / GPL-2 noarch
r-brea 0.2.0 A function to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models. / GPL-3 noarch
r-breakaway 3.0 Species richness estimation is an important problem in biodiversity analysis. This package provides methods for total species richness estimation (observed plus unobserved) and a method for modelling total diversity with covariates. breakaway() estimates total (observed plus unobserved) species richness. Microbial diversity datasets are characterized by a large number of rare species and a small number of highly abundant species. The class of models implemented by breakaway() is flexible enough to model both these features. breakaway_nof1() implements a similar procedure however does not require a singleton count. betta() provides a method for modelling total diversity with covariates in a way that accounts for its estimated nature and thus accounts for unobserved taxa, and betta_random() permits random effects modelling. / GPL-2 noarch
r-breakfast 1.0.0 The breakfast package performs multiple change-point detection in data sequences, or sequence segmentation, using computationally efficient multiscale methods. This version of the package implements the Tail-Greedy Unbalanced Haar, Wild Binary Segmentation and Adaptive Wild Binary Segmentation change-point detection and segmentation methodologies. To start with, see the function segment.mean. / GPL-3 noarch
r-breeze 0.4_3 A collection of functions to analyse, visualize and interpret wind data and to calculate the potential energy production of wind turbines. / MIT noarch
r-bretigea 1.0.0 Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the ‘CellCODE’ R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses. / MIT noarch
r-brew 1.0_6 brew implements a templating framework for mixing text and R code for report generation. brew template syntax is similar to PHP, Ruby’s erb module, Java Server Pages, and Python’s psp module. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-brglm 0.6.2 Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as ‘glm’. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
r-bridgedist 0.1.0 An implementation of the bridge distribution with logit-link in R. In Wang and Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate bridge distribution was derived as the distribution of the random intercept that ‘bridged’ a marginal logistic regression and a conditional logistic regression. The conditional and marginal regression coefficients are a scalar multiple of each other. Such is not the case if the random intercept distribution was Gaussian. / GPL-2 noarch
r-brlrmr 0.1.5 Provides two main functions, il() and fil(). The il() function implements the EM algorithm developed by Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068> to estimate the parameters of a logistic regression model with the missing response when the missing data mechanism is nonignorable. The fil() function implements the algorithm proposed by Maity et. al. (2017) <https://github.com/arnabkrmaity/brlrmr> to reduce the bias produced by the method of Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068>. / GPL-3 noarch
r-brm 1.0 Fits novel models for the conditional relative risk, risk difference and odds ratio. / MIT noarch
r-brnn 0.7 Bayesian regularization for feed-forward neural networks. / GPL-2 linux-64, osx-64, win-64
r-brobdingnag 1.2_6 Handles very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. The package includes a vignette that gives a step-by-step introduction to using S4 methods. / GPL-3 noarch
r-broom 0.5.2 Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-brotli 1.2 A lossless compressed data format that uses a combination of the LZ77 algorithm and Huffman coding. Brotli is similar in speed to deflate (gzip) but offers more dense compression. / MIT linux-64, osx-64, win-64
r-browndog 0.2.1 An R interface for the Brown Dog which allows researchers to leverage Brown Dog Services that provides modules to identify the conversion options for a file, to convert file to appropriate format, or to extract data from a file. See <http://browndog.ncsa.illinois.edu/> for more information. / FreeBSD | file LICENSE noarch
r-brq 2.5 Bayesian estimation and variable selection for quantile regression models. / GPL-2 noarch
r-brunnermunzel 1.3.5 Provides the functions for Brunner-Munzel test and permuted Brunner-Munzel test, which enable to use formula, matrix, and table as argument. These functions are based on Brunner and Munzel (2000) <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U> and Neubert and Brunner (2007) <doi:10.1016/j.csda.2006.05.024>, and are written with FORTRAN. / GPL-2 | GPL-3 linux-64, osx-64, win-64
r-bs4dash 0.4.0 Make ‘Bootstrap 4’ dashboards. Use the full power of ‘AdminLTE3’, a dashboard template built on top of ‘Bootstrap 4’ <https://github.com/almasaeed2010/AdminLTE/tree/v3-dev>. / GPL-2 noarch
r-bsda 1.2.0 Data sets for book Basic Statistics and Data Analysis by Larry J. Kitchens. / GPL-2 noarch
r-bsearchtools 0.0.61 Exposes the binary search functions of the C standard library (std::lower_bound, std::upper_bound) plus other convenience functions, allowing faster lookups on sorted vectors. / GPL-2 linux-64, osx-64, win-64
r-bsmd 2013.0718_1 Bayes screening and model discrimination follow-up designs. / GPL-2 linux-64, osx-64, win-64
r-bspec 1.5 Bayesian inference on the (discrete) power spectrum of time series. / GPL-2 noarch
r-bsplinepsd 0.6.0 Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series. The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented in Edwards, M.C., Meyer, R. and Christensen, N., Statistics and Computing (2018). <doi.org/10.1007/s11222-017-9796-9>. / GPL-3 linux-64, osx-64, win-64
r-bsplus 0.1.1 The Bootstrap framework lets you add some JavaScript functionality to your web site by adding attributes to your HTML tags - Bootstrap takes care of the JavaScript <https://getbootstrap.com/javascript>. If you are using R Markdown or Shiny, you can use these functions to create collapsible sections, accordion panels, modals, tooltips, popovers, and an accordion sidebar framework (not described at Bootstrap site). / MIT noarch
r-bspmma 0.1_2 The main functions carry out Gibbs’ sampler routines for nonparametric and semiparametric Bayesian models for random effects meta-analysis. / GPL-2 noarch
r-bsts 0.9.1 Time series regression using dynamic linear models fit using MCMC. See Scott and Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many other sources. / LGPL-2.1 linux-64, osx-64, win-64
r-btm 0.2.1 Biterm Topic Models find topics in collections of short texts. It is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns which are called biterms. This in contrast to traditional topic models like Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis which are word-document co-occurrence topic models. A biterm consists of two words co-occurring in the same short text window. This context window can for example be a twitter message, a short answer on a survey, a sentence of a text or a document identifier. The techniques are explained in detail in the paper ‘A Biterm Topic Model For Short Text’ by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013) <https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/BTM-WWW13.pdf>. / Apache License 2.0 linux-64, osx-64, win-64
r-bucky 1.0.5 Provides functions for various statistical techniques commonly used in the social sciences, including functions to compute clustered robust standard errors, combine results across multiply-imputed data sets, and simplify the addition of robust and clustered robust standard errors. The package was originally developed, in part, to assist porting of replication code from ‘Stata’ and attempts to replicate default options from ‘Stata’ where possible. / GPL-3 noarch
r-bucss 1.1.0 Implements a method of correcting for publication bias and uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562). / GPL-3 noarch
r-buddle 1.0 Statistical classification has been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades. It is a veritable technique which was originally designed for the classification, and hence, the EzDL package can provide sublime solutions to various challenging classification problems encountered in the clinical trials. The EzDL package is based on the back-propagation algorithm which performs a multi-layer feed-forward neural network. This package contains two functions: Buddle_Main() and Buddle_Predict(). Buddle_Main() builds a feed-forward neural network model and trains the model. Buddle_Predict() provokes the trained model which is the output of Buddle_Main(), classifies given data, and make a final prediction for the data. / GPL-2 linux-64, osx-64, win-64
r-buildmer 1.2 Finds the largest possible regression model that will still converge for various types of regression analyses (including mixed models and generalized additive models) and then optionally performs stepwise elimination similar to the forward and backward effect selection methods in SAS, based on the change in log-likelihood or its significance, Akaike’s Information Criterion, or the Bayesian Information Criterion. / FreeBSD noarch
r-bullwhipgame 0.1.0 The bullwhipgame is an educational game that has as purpose the illustration and exploration of the bullwhip effect,i.e, the increase in demand variability along the supply chain. Marchena Marlene (2010) <arXiv:1009.3977>. / GPL-3 noarch
r-bunchr 1.2.0 View and analyze data where bunching is expected. Estimate counter- factual distributions. For earnings data, estimate the compensated elasticity of earnings w.r.t. the net-of-tax rate. / MIT noarch
r-bundesbank 0.1_9 Download data from the time-series databases of the Bundesbank, the German central bank. See the overview at the Bundesbank website (<https://www.bundesbank.de/en/statistics/time-series-databases>) for available series. The package provides only a single function, getSeries(), which supports both traditional and real-time datasets; it will also download meta data if available. Downloaded data can automatically be arranged in various formats, such as data frames or ‘zoo’ series. The data may optionally be cached, so as to avoid repeated downloads of the same series. / GPL-3 noarch
r-bundesligr 0.1.0 All final tables of Germany’s highest football (soccer!) league, the Bundesliga. Contains data from 1964 to 2016. / GPL-3 noarch
r-burstfin 1.02 A suite of functions for finance, including the estimation of variance matrices via a statistical factor model or Ledoit-Wolf shrinkage. / Unlimited noarch
r-burstmisc 1.1 Script search, corner, genetic optimization, permutation tests, write expect test. / Unlimited noarch
r-bursts 1.0_1 An implementation of Jon Kleinberg’s burst detection algorithm. Uses an infinite Markov model to detect periods of increased activity in a series of discrete events with known times, and provides a simple visualization of the results. / MIT noarch
r-busdater 0.2.0 Get a current financial year, start of current month, End of current month, start of financial year and end of it. Allow for offset from the date. / MIT noarch
r-businessduration 0.2.0 Calculates business duration between two dates. This excluding weekends, public holidays and non-business hours. / AGPL-3 noarch
r-buysetest 1.7 Implementation of the Generalized Pairwise Comparisons (GPC). GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints. The net benefit and win ratio statistics can then be estimated and corresponding confidence intervals and p-values can be estimated using resampling methods or the asymptotic U-statistic theory. The software enables the use of thresholds of minimal importance difference, stratification, and corrections to deal with right-censored endpoints or missing values. / GPL-3 linux-64, osx-64, win-64
r-bvar 0.1.5 Toolkit for the estimation of hierarchical Bayesian vector autoregressions. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>. Allows for the computation of impulse responses and forecasts and provides functionality for assessing results. / GPL-3 noarch
r-bvarsv 1.1 R/C implementation of the model proposed by Primiceri (Time Varying Structural Vector Autoregressions and Monetary Policy, Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses. / GPL-2 linux-64, osx-64, win-64
r-bvartools 0.0.2 Assists in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) models. Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Koop and Korobilis (2010) <doi:10.1561/0800000013> and Luetkepohl (2007, ISBN: 9783540262398). / GPL-2 linux-64, osx-64, win-64
r-bvenn 0.1 This package implements a simple alternative to the traditional Venn diagram. It depicts each overlap as a separate bubble with area proportional to the overlap size. Relation of the bubbles to input sets is shown by their their arrangement. / GPL-2 noarch
r-bvls 1.4 An R interface to the Stark-Parker implementation of an algorithm for bounded-variable least squares / GPL-2 linux-64, osx-64, win-64
r-bwd 0.1.0 Implements a backward procedure for single and multiple change point detection proposed by Shin et al. <arXiv:1812.10107>. The backward approach is particularly useful to detect short and sparse signals which is common in copy number variation (CNV) detection. / GPL-2 linux-64, osx-64, win-64
r-bwgr 1.6.5 Whole-genome regression methods on Bayesian framework fitted via EM or Gibbs sampling, univariate and multivariate, with optional kernel term and sampling techniques. / GPL-3 linux-64, osx-64, win-64
r-bwimage 1.0 A computational tool to describe patterns in black and white images from natural structures. ‘bwimage’ implemented functions for exceptionally broad subject. For instance, ‘bwimage’ provide examples that range from calculation of canopy openness, description of patterns in vertical vegetation structure, to patterns in bird nest structure. / GPL-2 noarch
r-bwstest 0.2.2 Performs the ‘Baumgartner-Weiss-Schindler’ two-sample test of equal probability distributions, <doi:10.2307/2533862>. Also performs similar rank-based tests for equal probability distributions due to Neuhauser <doi:10.1080/10485250108832874> and Murakami <doi:10.1080/00949655.2010.551516>. / LGPL-3 linux-64, osx-64, win-64
r-bytescircle 1.1 Shows statistics about bytes contained in a file as a circle graph of deviations from mean in sigma increments. The function can be useful for statistically analyze the content of files in a glimpse: text files are shown as a green centered crown, compressed and encrypted files should be shown as equally distributed variations with a very low CV (sigma/mean), and other types of files can be classified between these two categories depending on their text vs binary content, which can be useful to quickly determine how information is stored inside them (databases, multimedia files, etc). / GPL-3 linux-64, osx-64, win-64
r-bzinb 1.0.3 Provides a maximum likelihood estimation of Bivariate Zero-Inflated Negative Binomial (BZINB) model or the nested model parameters. Also estimates the underlying correlation of the a pair of count data. See Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation) for details. / GPL-2 linux-64, osx-64, win-64
r-c212 0.95 Methods for detecting safety signals in clinical trials using groupings of adverse events by body-system or system organ class.The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12. / GPL-2 linux-64, osx-64, win-64
r-c2c 0.1.0 Compare two classifications or clustering solutions that may or may not have the same number of classes, and that might have hard or soft (fuzzy, probabilistic) membership. Calculate various metrics to assess how the clusters compare to each other. The calculations are simple, but provide a handy tool for users unfamiliar with matrix multiplication. This package is not geared towards traditional accuracy assessment for classification/ mapping applications - the motivating use case is for comparing a probabilistic clustering solution to a set of reference or existing class labels that could have any number of classes (that is, without having to degrade the probabilistic clustering to hard classes). / GPL-3 noarch
r-c3net 1.1.1 This package allows inferring gene regulatory networks with direct physical interactions from microarray expression data using C3NET. / GPL-3 noarch
r-ca 0.71 Computation and visualization of simple, multiple and joint correspondence analysis. / GPL-3 noarch
r-cabootcrs 1.0 Performs correspondence analysis on a two-way contingency table and produces bootstrap-based elliptical confidence regions around the projected coordinates for the category points. Includes routines to plot the results in a variety of styles. Also reports the standard numerical output for correspondence analysis. / GPL-2 noarch
r-cacirt 1.4 Computes classification accuracy and consistency indices under Item Response Theory. Implements the total score IRT-based methods in Lee, Hanson & Brennen (2002) and Lee (2010), the IRT-based methods in Rudner (2001, 2005), and the total score nonparametric methods in Lathrop & Cheng (2014). For dichotomous and polytomous tests. / GPL-2 noarch
r-caic4 0.8 Provides functions for the estimation of the conditional Akaike information in generalized mixed-effect models fitted with (g)lmer() from ‘lme4’. / GPL-2 noarch
r-cairo 1.5_10 Cairo graphics device that can be use to create high-quality vector (PDF, PostScript and SVG) and bitmap output (PNG,JPEG,TIFF), and high-quality rendering in displays (X11 and Win32). Since it uses the same back-end for all output, copying across formats is WYSIWYG. Files are created without the dependence on X11 or other external programs. This device supports alpha channel (semi-transparent drawing) and resulting images can contain transparent and semi-transparent regions. It is ideal for use in server environments (file output) and as a replacement for other devices that don’t have Cairo’s capabilities such as alpha support or anti-aliasing. Backends are modular such that any subset of backends is supported. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
r-calacs 2.2.2 Implements several string comparison algorithms, including calACS (count all common subsequences), lenACS (calculate the lengths of all common subsequences), and lenLCS (calculate the length of the longest common subsequence). Some algorithms differentiate between the more strict definition of subsequence, where a common subsequence cannot be separated by any other items, from its looser counterpart, where a common subsequence can be interrupted by other items. This difference is shown in the suffix of the algorithm (-Strict vs -Loose). For example, q-w is a common subsequence of q-w-e-r and q-e-w-r on the looser definition, but not on the more strict definition. calACSLoose Algorithm from Wang, H. All common subsequences (2007) IJCAI International Joint Conference on Artificial Intelligence, pp. 635-640. / GPL-3 noarch
r-calculator.lr.fns 1.3 Arithmetic operations scalar multiplication, addition, subtraction, multiplication and division of LR fuzzy numbers (which are on the basis of extension principle) have a complicate form for using in fuzzy Statistics, fuzzy Mathematics, machine learning, fuzzy data analysis and etc. Calculator for LR Fuzzy Numbers package relieve and aid applied users to achieve a simple and closed form for some complicated operator based on LR fuzzy numbers and also the user can easily draw the membership function of the obtained result by this package. / LGPL-3 noarch
r-calibrate 1.7.2 Package for drawing calibrated scales with tick marks on (non-orthogonal) variable vectors in scatterplots and biplots. / GPL-2 noarch
r-calibrator 1.2_8 Performs Bayesian calibration of computer models as per Kennedy and O’Hagan 2001. The package includes routines to find the hyperparameters and parameters; see the help page for stage1() for a worked example using the toy dataset. A tutorial is provided in the calex.Rnw vignette; and a suite of especially simple one dimensional examples appears in inst/doc/one.dim/. / GPL-2 noarch
r-callr 3.2.0 It is sometimes useful to perform a computation in a separate R process, without affecting the current R process at all. This packages does exactly that. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-caman 0.74 Tools for the analysis of finite semiparametric mixtures. These are useful when data is heterogeneous, e.g. in pharmacokinetics or meta-analysis. The NPMLE and VEM algorithms (flexible support size) and EM algorithms (fixed support size) are provided for univariate and bivariate data. / GPL-2 linux-64, osx-64, win-64
r-camsrad 0.3.0 Copernicus Atmosphere Monitoring Service (CAMS) radiations service provides time series of global, direct, and diffuse irradiations on horizontal surface, and direct irradiation on normal plane for the actual weather conditions as well as for clear-sky conditions. The geographical coverage is the field-of-view of the Meteosat satellite, roughly speaking Europe, Africa, Atlantic Ocean, Middle East. The time coverage of data is from 2004-02-01 up to 2 days ago. Data are available with a time step ranging from 15 min to 1 month. For license terms and to create an account, please see <http://www.soda-pro.com/web-services/radiation/cams-radiation-service>. / MIT noarch
r-canprot 0.1.2 Datasets are collected here for differentially (up- and down-) expressed proteins identified in proteomic studies of cancer and in cell culture experiments. Tables of amino acid compositions of proteins are used for calculations of chemical composition, projected into selected basis species. Plotting functions are used to visualize the compositional differences and thermodynamic potentials for proteomic transformations. / GPL-3 noarch
r-canvasxpress 1.24.8 Enables creation of visualizations using the CanvasXpress framework in R. CanvasXpress is a standalone JavaScript library for reproducible research with complete tracking of data and end-user modifications stored in a single PNG image that can be played back. See <http://canvasxpress.org> for more information. / GPL-3 noarch
r-caper 1.0.1 Functions for performing phylogenetic comparative analyses. / GPL-2 noarch
r-capitalr 1.2.0 Provides Capital Budgeting Analysis functionality and the essential Annuity loan functions. Also computes Loan Amortization Schedules including schedules with irregular payments. / GPL-3 noarch
r-capn 1.0.0 Implements approximation methods for natural capital asset prices suggested by Fenichel and Abbott (2014) <doi:10.1086/676034> in Journal of the Associations of Environmental and Resource Economists (JAERE), Fenichel et al. (2016) <doi:10.1073/pnas.1513779113> in Proceedings of the National Academy of Sciences (PNAS), and Yun et al. (2017) in PNAS (accepted), and their extensions: creating Chebyshev polynomial nodes and grids, calculating basis of Chebyshev polynomials, approximation and their simulations for: V-approximation (single and multiple stocks, PNAS), P-approximation (single stock, PNAS), and Pdot-approximation (single stock, JAERE). Development of this package was generously supported by the Knobloch Family Foundation. / GPL-2 noarch
r-captioner 2.2.3 Provides a method for automatically numbering figures, tables, or other objects. Captions can be displayed in full, or as citations. This is especially useful for adding figures and tables to R markdown documents without having to numbering them manually. / MIT noarch
r-captr 0.3.0 Get text from images of text using Captricity Optical Character Recognition (OCR) API. Captricity allows you to get text from handwritten forms — think surveys — and other structured paper documents. And it can output data in form a delimited file keeping field information intact. For more information, read <https://shreddr.captricity.com/developer/overview/>. / MIT noarch
r-capushe 1.1.1 Calibration of penalized criteria for model selection. The calibration methods available are based on the slope heuristics. / GPL (>= 2.0) noarch
r-capwire 1.1.4 Fits models from Miller et al. 2005 to estimate population sizes from natural populations. Several models are implemented. Package also includes functions to perform a likelihood ratio test to choose between models, perform parametric bootstrapping to obtain confidence intervals and multiple functions to simulate data. / GPL-2 noarch
r-car 3.0_2 Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, in press. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-cardata 3.0_2 Datasets to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage (forthcoming). / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-cardiomodel 1.4 Includes over 100 mixed-effects model structures describing the relationship between drug concentration and QT interval, heart rate/pulse rate or blood pressure. Given an exposure-response dataset, the tool fits each model structure to the observed data. / GPL-3 noarch
r-care 1.1.10 Implements the regression approach of Zuber and Strimmer (2011) High-dimensional regression and variable selection using CAR scores SAGMB 10: 34, <DOI:10.2202/1544-6115.1730>. CAR scores measure the correlation between the response and the Mahalanobis-decorrelated predictors. The squared CAR score is a natural measure of variable importance and provides a canonical ordering of variables. This package provides functions for estimating CAR scores, for variable selection using CAR scores, and for estimating corresponding regression coefficients. Both shrinkage as well as empirical estimators are available. / GPL-3 noarch
r-care1 1.1.0 The R package CARE1, the first part of the program CARE (Capture-Recapture) in http://chao.stat.nthu.edu.tw/softwareCE.html, can be used to analyze epidemiological data via sample coverage approach (Chao et al. 2001a). Based on the input of records from several incomplete lists (or samples) of individuals, the R package CARE1 provides output of population size estimate and related statistics. / GPL-3 noarch
r-careless 1.1.3 When taking online surveys, participants sometimes respond to items without regard to their content. These types of responses, referred to as careless or insufficient effort responding, constitute significant problems for data quality, leading to distortions in data analysis and hypothesis testing, such as spurious correlations. The ‘R’ package ‘careless’ provides solutions designed to detect such careless / insufficient effort responses by allowing easy calculation of indices proposed in the literature. It currently supports the calculation of longstring, even-odd consistency, psychometric synonyms/antonyms, Mahalanobis distance, and intra-individual response variability (also termed inter-item standard deviation). For a review of these methods, see Curran (2016) <doi:10.1016/j.jesp.2015.07.006>. / MIT noarch
r-caret 6.0_83 Misc functions for training and plotting classification and regression models. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
r-caribou 1.1 This is a package for estimating the population size of migratory caribou herds based on large scale aggregations monitored by radio telemetry. It implements the methodology found in the article by Rivest et al. (1998) about caribou abundance estimation. It also includes a function based on the Lincoln-Petersen Index as applied to radio telemetry data by White and Garrott (1990). / GPL-2 noarch
r-carletonstats 2.0 Includes commands for bootstrapping and permutation tests, a command for created grouped bar plots, and a demo of the quantile-normal plot for data drawn from different distributions. / GPL-2 noarch
r-carlit 1.0 Functions to calculate and plot ecological quality ratios (EQR) as specified by Ballesteros et al. 2007. / GPL-2 noarch
r-caroline 0.7.6 The caroline R library contains dozens of functions useful for: database migration (dbWriteTable2), database style joins & aggregation (nerge, groupBy & bestBy), data structure conversion (nv, tab2df), legend table making (sstable & leghead), plot annotation (labsegs & mvlabs), data visualization (violins, pies & raPlot), character string manipulation (m & pad), file I/O (write.delim), batch scripting and more. The package’s greatest contributions lie in the database style merge, aggregation and interface functions as well as in it’s extensive use and propagation of row, column and vector names in most functions. / Artistic-2.0 noarch
r-carrier 0.1.0 Sending functions to remote processes can be wasteful of resources because they carry their environments with them. With the carrier package, it is easy to create functions that are isolated from their environment. These isolated functions, also called crates, print at the console with their total size and can be easily tested locally before being sent to a remote. / GPL-3 noarch
r-cascadedata 1.2 These experimental expression data (5 leukemic ‘CLL’ B-lymphocyte of aggressive form from ‘GSE39411’, <doi:10.1073/pnas.1211130110>), after B-cell receptor stimulation, are used as examples by packages such as the ‘Cascade’ one, a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>. / GPL-2 noarch
r-casematch 1.0.7 Allows users to identify similar cases for qualitative case studies using statistical matching methods. / GPL-2 noarch
r-casmap 0.6.0 A significant pattern mining-based toolbox for region-based genome-wide association studies and higher-order epistasis analyses, implementing the methods described in Llinares-López et al. (2017) <doi:10.1093/bioinformatics/btx071>. / GPL-2 linux-64, osx-64, win-64
r-cat 0.0_6.5 Analysis of categorical-variable with missing values / file LICENSE linux-64, osx-64, win-64
r-catchr 0.2.1 R has a unique way of dealing with warnings, errors, messages, and other conditions, but it can often be troublesome to users coming from different programming backgrounds. The purpose of this package is to provide flexible and useful tools for handling R conditions with less hassle. In order to lower the barrier of entry, keep code clean and readable, and reduce the amount of typing required, catchr uses a very simple domain-specific language that simplifies things on the front-end. This package aims to maintain a continuous learning curve that lets new users jump straight in to condition-handling, while simultaneously offering depth and complexity for more advanced users. / MIT noarch
r-catdap 1.3.4 Categorical data analysis by AIC. The methodology is described in Sakamoto (1992) <ISBN 978-0-7923-1429-5>. / GPL-2 linux-64, osx-64, win-64
r-catdata 1.2.1 This R-package contains examples from the book Regression for Categorical Data, Tutz 2011, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used. / GPL-2 noarch
r-catencoders 0.1.1 Contains some commonly used categorical variable encoders, such as ‘LabelEncoder’ and ‘OneHotEncoder’. Inspired by the encoders implemented in Python ‘sklearn.preprocessing’ package (see <http://scikit-learn.org/stable/modules/preprocessing.html>). / GPL-2 | GPL-3 noarch
r-cateselection 1.0 A multi-factor dimensionality reduction based forward selection method for genetic association mapping. / GPL-2 noarch
r-catirt 0.5_0 Functions designed to simulate data that conform to basic unidimensional IRT models (for now 3-parameter binary response models and graded response models) along with Post-Hoc CAT simulations of those models with various item selection methods, ability estimation methods, and termination criteria. / GPL-2 linux-64, osx-64, win-64
r-catnet 1.15.5 Structure learning and parameter estimation of discrete Bayesian networks using likelihood-based criteria. Exhaustive search for fixed node orders and stochastic search of optimal orders via simulated annealing algorithm are implemented. / GPL-2 linux-64, osx-64, win-64
r-catools 1.17.1.2 Contains several basic utility functions including: moving (rolling, running) window statistic functions, read/write for GIF and ENVI binary files, fast calculation of AUC, LogitBoost classifier, base64 encoder/decoder, round-off-error-free sum and cumsum, etc. / GPL-3 linux-32, linux-64, osx-64, win-32, win-64
r-catr 3.16 Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) <doi:10.18637/jss.v076.c01>). / GPL-2 noarch
r-catseyes 0.2.3 Provides the tools to produce catseye plots, principally by catseyesplot() function which calls R’s standard plot() function internally, or alternatively by the catseyes() function to overlay the catseye plot onto an existing R plot window. Catseye plots illustrate the normal distribution of the mean (picture a normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence interval; they are an intuitive way of illustrating and comparing normally distributed estimates, and are arguably a superior alternative to standard confidence intervals, since they show the full distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require pre-calculated means and standard errors (or standard deviations), provided as numeric vectors; this allows the flexibility of obtaining this information from a variety of sources, such as direct calculation or prediction from a model. Catseye plots, as illustrations of the normal distribution of the means, are described in Cumming (2013 & 2014). Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. <doi:10.1177/0956797613504966> pmid:24220629. / GPL-3 noarch
r-catspec 0.97 `ctab’ creates (multiway) percentage tables. `sqtab’ contains a set of functions for estimating models for square tables such as quasi-independence, symmetry, uniform association. Examples show how to use these models in a loglinear model using glm or in a multinomial logistic model using mlogit or clogit / GPL-2 noarch
r-catt 2.0 This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default. / GPL-3 noarch
r-cattexact 0.1.0 Provides functions for computing the one-sided p-values of the Cochran-Armitage trend test statistic for the asymptotic and the exact conditional test. The computation of the p-value for the exact test is performed using an algorithm following an idea by Mehta, et al. (1992) <doi:10.2307/1390598>. / GPL-2 | GPL-3 noarch
r-causalmgm 0.1.1 Allows users to learn undirected and directed (causal) graphs over mixed data types (i.e., continuous and discrete variables). To learn a directed graph over mixed data, it first calculates the undirected graph (Sedgewick et al, 2016) and then it uses local search strategies to prune-and-orient this graph (Sedgewick et al, 2017). AJ Sedgewick, I Shi, RM Donovan, PV Benos (2016) <doi:10.1186/s12859-016-1039-0>. AJ Sedgewick, JD Ramsey, P Spirtes, C Glymour, PV Benos (2017) <arXiv:1704.02621>. / GPL-2 noarch
r-causalsens 0.1.2 The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects. / GPL-2 noarch
r-cbanalysis 0.2.0 A set of functions that helps you to generate descriptive statistics based on the variable types. / GPL-2 noarch
r-cbinom 1.3 Implementation of the d/p/q/r family of functions for a continuous analog to the standard discrete binomial with continuous size parameter and continuous support with x in [0, size 1], following Ilienko (2013) <arXiv:1303.5990>. / GPL-2 linux-64, osx-64, win-64
r-cbird 1.0 The clustering of binary data with reducing the dimensionality (CLUSBIRD) proposed by Yamamoto and Hayashi (2015) <doi:10.1016/j.patcog.2015.05.026>. / GPL-2 linux-64, osx-64, win-64
r-cblasr 1.0.0 Provides the ‘cblas.h’ header file as C interface to the underlying internal ‘BLAS’ library in R. ‘CBLAS’ <https://www.netlib.org/blas/cblas.h> is a collection of wrappers originally written by Keita Teranishi and provides a C interface to the FORTRAN ‘BLAS’ library <https://www.netlib.org/blas/>. Note that as internal ‘BLAS’ library provided by R <https://svn.r-project.org/R/trunk/src/include/R_ext/BLAS.h> is used and only the double precision / double complex ‘BLAS’ routines are supported. / GPL-2 linux-64, osx-64, win-64
r-cbsem 1.0.0 The composites are linear combinations of their indicators in composite based structural equation models. Structural models are considered consisting of two blocks. The indicators of the exogenous composites are named by X, the indicators of the endogenous by Y. Reflective relations are given by arrows pointing from the composite to their indicators. Their values are called loadings. In a reflective-reflective scenario all indicators have loadings. Arrows are pointing to their indicators only from the endogenous composites in the formative-reflective scenario. There are no loadings at all in the formative-formative scenario. The covariance matrices are computed for these three scenarios. They can be used to simulate these models. These models can also be estimated and a segmentation procedure is included as well. / GPL-3 noarch
r-cbsodatar 0.3.4 The data and meta data from Statistics Netherlands (<https://www.cbs.nl>) can be browsed and downloaded. The client uses the open data API of Statistics Netherlands. / GPL-2 noarch
r-cbt 1.0 The Confidence Bound Target (CBT) algorithm is designed for infinite arms bandit problem. It is shown that CBT algorithm achieves the regret lower bound for general reward distributions. Reference: Hock Peng Chan and Shouri Hu (2018) <arXiv:1805.11793>. / GPL-2 noarch
r-cc 1.0 Tools for creating and visualizing statistical process control charts. Control charts are used for monitoring measurement processes, such as those occurring in manufacturing. The objective is to monitor the history of such processes and flag outlying measurements: out-of-control signals. Montgomery, D. (2009, ISBN:978-0-470-16992-6) contains an extensive discussion of the methodology. / GPL-2 noarch
r-ccagfa 1.0.8 Variational Bayesian algorithms for learning canonical correlation analysis (CCA), inter-battery factor analysis (IBFA), and group factor analysis (GFA). Inference with several random initializations can be run with the functions CCAexperiment() and GFAexperiment(). / GPL-2 noarch
r-ccapp 0.3.2 Canonical correlation analysis and maximum correlation via projection pursuit, as well as fast implementations of correlation estimators, with a focus on robust and non-parametric methods. / GPL-2 linux-64, osx-64, win-64
r-ccchooser 0.2.6 ccChooser can be used to developing and evaluation of core collections for germplasm collections (entire collection). This package used to develop a core collection for biological resources like genbanks. A core collection is defined as a sample of accessions that represent, with the lowest possible level of redundancy, the genetic diversity (the richness of gene or genotype categories) of the entire collection. The establishing a core collection that represents genetic diversity of the entire collection with minimum loss of its original diversity and minimum redundancies is an important problem for gene-banks curators and crop breeders. ccChooser establish core collection base on phenotypic data (agronomic, morphological, phenological). / GPL-2 noarch
r-cccp 0.2_4 Routines for solving convex optimization problems with cone constraints by means of interior-point methods. The implemented algorithms are partially ported from CVXOPT, a Python module for convex optimization (see <http://cvxopt.org> for more information). / GPL-3 linux-64, osx-64, win-64
r-cccrm 1.2.1 Estimates the Concordance Correlation Coefficient to assess agreement. The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. The estimation approaches implemented are variance components and U-statistics approaches. / GPL-2 noarch
r-ccda 1.1 This package implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. DOI: http://dx.doi.org/10.1016/j.envsoft.2014.01.010 / GPL-2 noarch
r-cchs 0.4.1 Contains a function, also called ‘cchs’, that calculates Estimator III of Borgan et al (2000), <DOI:10.1023/A:1009661900674>. This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling. / GPL-3 noarch
r-cclust 0.6_21 Convex Clustering methods, including K-means algorithm, On-line Update algorithm (Hard Competitive Learning) and Neural Gas algorithm (Soft Competitive Learning), and calculation of several indexes for finding the number of clusters in a data set. / GPL-2 linux-64, osx-64, win-64
r-ccm 1.2 Classification method described in Dancik et al (2011) <doi:10.1158/0008-5472.CAN-11-2427> that classifies a sample according to the class with the maximum mean (or any other function of) correlation between the test and training samples with known classes. / GPL-2 noarch
r-ccmm 1.0 Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision). / GPL-2 noarch
r-ccp 1.1 Significance tests for canonical correlation analysis, including asymptotic tests and a Monte Carlo method / GPL-3 noarch
r-ccpop 1.0 Tests of association between SNPs or pairs of SNPs and binary phenotypes, in case-control / case-population / case-control-population studies. / GPL-2 noarch
r-ccremover 1.0.4 Implements a method for identifying and removing the cell-cycle effect from scRNA-Seq data. The description of the method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. Different from previous methods, ccRemover implements a mechanism that formally tests whether a component is cell-cycle related or not, and thus while it often thoroughly removes the cell-cycle effect, it preserves other features/signals of interest in the data. / GPL-3 noarch
r-cdb 0.0.1 A constant database is a data structure created by Daniel J. Bernstein in his cdb package. Its format consists on a sequence of (key,value)-pairs. This R package replicates the basic utilities for reading (cdbget) and writing (cdbdump) constant databases. / GPL-2 noarch
r-cde 0.4.1 Facilitates searching, download and plotting of Water Framework Directive (WFD) reporting data for all waterbodies within the UK Environment Agency area. The types of data that can be downloaded are: WFD status classification data, Reasons for Not Achieving Good (RNAG) status, objectives set for waterbodies, measures put in place to improve water quality and details of associated protected areas. The site accessed is <https://environment.data.gov.uk/catchment-planning/>. The data are made available under the Open Government Licence v3.0 <https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/>. This package has been peer-reviewed by rOpenSci (v. 0.4.0). / GPL-3 noarch
r-cdft 1.0.1 This package proposes a statistical downscaling method for cumulative distribution functions (CDF), as well as the computation of the Cram`er-von Mises statistics U, and the Kolmogorov-Smirnov statistics KS. / GPL-2 noarch
r-cdlasso 1.1 Coordinate Descent Algorithms for Lasso Penalized L1, L2, and Logistic Regression / GPL-2 linux-64, osx-64, win-64
r-cdltools 0.14 Downloads USDA National Agricultural Statistics Service (NASS) cropscape data for a specified state. Utilities for fips, abbreviation, and name conversion are also provided. Full functionality requires an internet connection, but data sets can be cached for later off-line use. / Unlimited linux-64, osx-64, win-64
r-cdnmoney 2012.4_2 Components of Canadian Credit Aggregates and Monetary Aggregates with continuity adjustments. / GPL-2 noarch
r-cdrom 1.1 Classification is based on the recently developed phylogenetic approach by Assis and Bachtrog (2013). The method classifies the evolutionary mechanisms retaining pairs of duplicate genes (conservation, neofunctionalization, subfunctionalization, or specialization) by comparing gene expression profiles of duplicate genes in one species to those of their single- copy ancestral genes in a sister species. / GPL-2 noarch
r-cdvine 1.4 Functions for statistical inference of canonical vine (C-vine) and D-vine copulas. Tools for bivariate exploratory data analysis and for bivariate as well as vine copula selection are provided. Models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and plotting methods are also included. Data is assumed to lie in the unit hypercube (so-called copula data). / GPL-2 linux-64, osx-64, win-64
r-cec 0.10.2 CEC divides data into Gaussian type clusters. The implementation allows the simultaneous use of various type Gaussian mixture models, performs the reduction of unnecessary clusters and it’s able to discover new groups. Based on Spurek, P. and Tabor, J. (2014) <doi:10.1016/j.patcog.2014.03.006>. / GPL-3 linux-64, osx-64, win-64
r-cec2005benchmark 1.0.4 This package is a wrapper for the C implementation of the 25 benchmark functions for the CEC 2005 Special Session on Real-Parameter Optimization. The original C code by Santosh Tiwari and related documentation are available at http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC-05/CEC05.htm. / GPL-3 linux-64, osx-64, win-64
r-cec2013 0.1_5 This package provides R wrappers for the C implementation of 28 benchmark functions defined for the Special Session and Competition on Real-Parameter Single Objective Optimization at CEC-2013. The focus of this package is to provide an open-source and multi-platform implementation of the CEC2013 benchmark functions, in order to make easier for researchers to test the performance of new optimization algorithms in a reproducible way. The original C code (Windows only) was provided by Jane Jing Liang, while GNU/Linux comments were made by Janez Brest. This package was gently authorised for publication on CRAN by Ponnuthurai Nagaratnam Suganthan. The official documentation is available at http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2013/CEC2013.htm. Bugs reports/comments/questions are very welcomed (in English, Spanish or Italian). / GPL-3 linux-64, osx-64, win-64
r-cellranger 1.1.0 Helper functions to work with spreadsheets and the A1:D10 style of cell range specification. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-cengam 0.5.3 Implementation of Tobit type I and type II families for censored regression using the ‘mgcv’ package, based on methods detailed in Woods (2016) <doi:10.1080/01621459.2016.1180986>. / GPL-2 noarch
r-censcov 1.0_0 Implementations of threshold regression approaches for linear regression models with a covariate subject to random censoring, including deletion threshold regression and completion threshold regression. Reverse survival regression, which flip the role of response variable and the covariate, is also considered. / GPL-3 noarch
r-censnid 0_0_1 Implements AS138, AS139. / GPL-2 linux-64, osx-64, win-64
r-censorcopula 2.0 Implement an interval censor method to break ties when using data with ties to fitting a bivariate copula. / GPL-2 noarch
r-censregmod 1.0 Fits univariate censored linear regression model under Normal or Student-t distribution / GPL (>= 3.0) noarch
r-census2016 0.2.0 Contains selected variables from the time series profiles for statistical areas level 2 from the 2006, 2011, and 2016 censuses of population and housing, Australia. Also provides methods for viewing the questions asked for convenience during analysis. / CC BY 4.0 noarch
r-censusapi 0.6.0 A wrapper for the U.S. Census Bureau APIs that returns data frames of Census data and metadata. Available datasets include the Decennial Census, American Community Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, Population Estimates and Projections, and more. / GPL-3 noarch
r-censys 0.1.0 The ‘Censys’ public search engine enables researchers to quickly ask questions about the hosts and networks that compose the Internet. Details on how ‘Censys’ was designed and how it is operated are available at <https://www.censys.io/about>. Both basic and extended research access queries are made available. More information on the SQL dialect used by the ‘Censys’ engine can be found at <https://cloud.google.com/bigquery/docs/reference/legacy-sql>. / AGPL-3 noarch
r-centiserve 1.0.0 Calculates centrality indices additional to the ‘igraph’ package centrality functions. / GPL-2 noarch
r-cents 0.1_41 Fit censored time series / GPL-2 linux-64, osx-64, win-64
r-cepreader 1.1_3 Read Condensed Cornell Ecology Program (‘CEP’) and legacy ‘CANOCO’ files into R data frames. / MIT linux-64, osx-64
r-ceriolioutlierdetection 1.1.9 Implements the iterated RMCD method of Cerioli (2010) for multivariate outlier detection via robust Mahalanobis distances. Also provides the finite-sample RMCD method discussed in the paper, as well as the methods provided in Hardin and Rocke (2005) <doi:10.1198/106186005X77685> and Green and Martin (2017). / GPL-2 noarch
r-cernaseek 2.1 Provides several functions to identify and analyse miRNA sponge, including popular methods for identifying miRNA sponge interactions, two types of global ceRNA regulation prediction methods and four types of context-specific prediction methods( Li Y et al.(2017) <doi:10.1093/bib/bbx137>), which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. In addition, For predictive ceRNA relationship pairs, this package provides several downstream analysis algorithms, including regulatory network analysis and functional annotation analysis, as well as survival prognosis analysis based on expression of ceRNA ternary pair. / GPL-3 noarch
r-cernn 0.1 An implementation of the covariance estimation method proposed in Chi and Lange (2014), Stable estimation of a covariance matrix guided by nuclear norm penalties, Computational Statistics and Data Analysis 80:117-128. / MIT noarch
r-cetcolor 0.2.0 Collection of perceptually uniform colour maps made by Peter Kovesi (2015) Good Colour Maps: How to Design Them <arXiv:1509.03700> at the Centre for Exploration Targeting (CET). / CC BY-SA 4.0 noarch
r-cfa 0.10_0 Analysis of configuration frequencies for simple and repeated measures, multiple-samples CFA, hierarchical CFA, bootstrap CFA, functional CFA, Kieser-Victor CFA, and Lindner’s test using a conventional and an accelerated algorithm. / GPL-2 linux-64, osx-64, win-64
r-cfc 1.1.2 Numerical integration of cause-specific survival curves to arrive at cause-specific cumulative incidence functions, with three usage modes: 1) Convenient API for parametric survival regression followed by competing-risk analysis, 2) API for CFC, accepting user-specified survival functions in R, and 3) Same as 2, but accepting survival functions in C. / GPL-2 linux-64, osx-64, win-64
r-cfestimatequantiles 1.0 Estimate quantiles using formula (18) from http://www.jaschke-net.de/papers/CoFi.pdf (Yaschke; 2001) / GPL-2 noarch
r-cfma 1.0 Performs causal functional mediation analysis (CFMA) for functional treatment, functional mediator, and functional outcome. This package includes two functional mediation model types: (1) a concurrent mediation model and (2) a historical influence mediation model. See Zhao et al. (2018), Functional Mediation Analysis with an Application to Functional Magnetic Resonance Imaging Data, <arXiv:1805.06923> for details. / GPL-2 noarch
r-cgauc 1.2.1 The cgAUC can calculate the AUC-type measure of Obuchowski(2006) when gold standard is continuous, and find the optimal linear combination of variables with respect to this measure. / GPL-2 linux-64, osx-64, win-64
r-cgdsr 1.3.0 Provides a basic set of R functions for querying the Cancer Genomics Data Server (CGDS), hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC) at <www.cbioportal.org>. / LGPL-3 noarch
r-cge 0.1.9 Developing general equilibrium models, computing general equilibrium and simulating economic dynamics with structural dynamic models in LI (2019, ISBN: 9787521804225) General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press. / GPL-2 | GPL-3 noarch
r-cggp 1.0.1 Run computer experiments using the adaptive composite grid algorithm with a Gaussian process model. The algorithm works best when running an experiment that can evaluate thousands of points from a deterministic computer simulation. This package is an implementation of a forthcoming paper by Plumlee, Erickson, Ankenman, et al. For a preprint of the paper, contact the maintainer of this package. / GPL-3 linux-64, osx-64, win-64
r-cgh 1.0_7.1 Functions to analyze microarray comparative genome hybridization data using the Smith-Waterman algorithm / GPL-2 linux-64, osx-64, win-64
r-cglasso 1.1.1 The l1-penalized censored Gaussian graphical model is an extension of the graphical lasso estimator developed to handle datasets with censored observations. An EM-like algorithm is implemented to estimate the parameters of the censored Gaussian graphical models. / GPL-2 linux-64, osx-64, win-64
r-cgp 2.1_1 Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) Composite Gaussian Process Models for Emulating Expensive Functions, Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary. Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP. / LGPL-2.1 noarch
r-cgraph 4.0.3 Allows to create, evaluate, and differentiate computational graphs in R. A computational graph is a graph representation of a multivariate function decomposed by its (elementary) operations. Nodes in the graph represent arrays while edges represent dependencies among the arrays. An advantage of expressing a function as a computational graph is that this enables to differentiate the function by automatic differentiation. The ‘cgraph’ package supports various operations including basic arithmetic, trigonometry operations, and linear algebra operations. It differentiates computational graphs by reverse automatic differentiation. The flexible architecture of the package makes it applicable to solve a variety of problems including local sensitivity analysis, gradient-based optimization, and machine learning. / Apache License 2.0 linux-64, osx-64, win-64
r-cgwtools 3.0.1 Functions for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc. In addition, functions which mimic Unix shell commands, including ‘head’, ‘tail’ ,’pushd’ ,and ‘popd’. The functions ‘seqle’ and ‘reverse.seqle’ mimic the base ‘rle’ but can search for linear sequences. The function ‘splatnd’ allows the user to generate zero-argument commands without the need for ‘makeActiveBinding’ . / LGPL-3 noarch
r-chandwich 1.1.2 Performs adjustments of a user-supplied independence loglikelihood function using a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions or for performing inferences that are robust to certain types of model misspecification. Functions for profiling the adjusted loglikelihoods are also provided, as are functions for calculating and plotting confidence intervals, for single model parameters, and confidence regions, for pairs of model parameters. Nested models can be compared using an adjusted likelihood ratio test. / GPL-2 noarch
r-changepoint 2.2.2 Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call. / GPL-3 linux-64, osx-64, win-64
r-changepointshd 0.3.1 This implements the methods developed in, L. Bybee and Y. Atchade. (2018). Contains a series of methods for estimating change-points given user specified black-box models. The methods include binary segmentation for multiple change-point estimation. For estimating each individual change-point the package includes simulated annealing, brute force, and, for Gaussian graphical models, an applications specific rank-one update implementation. Additionally, code for estimating Gaussian graphical models is included. The goal of this package is to allow for the efficient estimation of change-points in complicated models with high dimensional data. / GPL-2 linux-64, osx-64, win-64
r-changepointsvar 0.1.0 Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, <doi:10.1080/03610918.2011.592248>. / GPL-2 noarch
r-changepointtesting 1.0 A multiple testing procedure for clustered alternative hypotheses. It is assumed that the p-values under the null hypotheses follow U(0,1) and that the distributions of p-values from the alternative hypotheses are stochastically smaller than U(0,1). By aggregating information, this method is more sensitive to detecting signals of low magnitude than standard methods. Additionally, sporadic small p-values appearing within a null hypotheses sequence are avoided by averaging on the neighboring p-values. / GPL-2 noarch
r-channelattribution 1.16 Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called the online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C. / GPL-2 linux-64, osx-64, win-64
r-chaos01 1.2.1 Computes and visualize the results of the 0-1 test for chaos proposed by Gottwald and Melbourne (2004) <DOI:10.1137/080718851>. The algorithm is available in parallel for the independent values of parameter c. Additionally, fast RQA is added to distinguish chaos from noise. / GPL-3 linux-64, osx-64, win-64
r-chargetransport 1.0.2 This package provides functions to compute Marcus, Marcus-Levich-Jortner or Landau-Zener charge transfer rates. These rates can then be used to perform kinetic Monte Carlo simulations to estimate charge carrier mobilities in molecular materials. The preparation of this package was supported by the the Fondazione Cariplo (PLENOS project, ref. 2011-0349). / GPL-3 linux-64, osx-64, win-64
r-chcn 1.5 A compilation of historical through contemporary climate measurements scraped from the Environment Canada Website Including tools for scraping data, creating metadata and formating temperature files. / GPL-2 noarch
r-cheb 0.3 Discrete Linear Chebyshev Approximation / GPL-3 linux-64, osx-64, win-64
r-checkarg 0.1.0 Utility functions that allow checking the basic validity of a function argument or any other value, including generating an error and assigning a default in a single line of code. The main purpose of the package is to provide simple and easily readable argument checking to improve code robustness. / GPL-2 noarch
r-checkdigit 0.1_1 A set of functions to calculate check digits according to various algorithms and to verify whether a string ends in a valid check digit / GPL-3 noarch
r-checkmate 1.9.1 Tests and assertions to perform frequent argument checks. A substantial part of the package was written in C to minimize any worries about execution time overhead. / BSD_3_clause file LICENSE linux-32, linux-64, osx-64, win-32, win-64
r-checkpoint 0.4.6 The goal of checkpoint is to solve the problem of package reproducibility in R. Specifically, checkpoint allows you to install packages as they existed on CRAN on a specific snapshot date as if you had a CRAN time machine. To achieve reproducibility, the checkpoint() function installs the packages required or called by your project and scripts to a local library exactly as they existed at the specified point in time. Only those packages are available to your project, thereby avoiding any package updates that came later and may have altered your results. In this way, anyone using checkpoint’s checkpoint() can ensure the reproducibility of your scripts or projects at any time. To create the snapshot archives, once a day (at midnight UTC) Microsoft refreshes the Austria CRAN mirror on the Microsoft R Archived Network server (<https://mran.microsoft.com/>). Immediately after completion of the rsync mirror process, the process takes a snapshot, thus creating the archive. Snapshot archives exist starting from 2014-09-17. / GPL-2 linux-64, noarch, osx-64, win-64
r-cheddar 0.1_633 Provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. Allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients. / BSD_2_clause linux-64, osx-64, win-64
r-chemcal 0.2.1 Simple functions for plotting linear calibration functions and estimating standard errors for measurements according to the Handbook of Chemometrics and Qualimetrics: Part A by Massart et al. There are also functions estimating the limit of detection (LOD) and limit of quantification (LOQ). The functions work on model objects from - optionally weighted - linear regression (lm) or robust linear regression (‘rlm’ from the ‘MASS’ package). / GPL-2 noarch
r-chemometricswithr 0.1.13 Functions and scripts used in the book Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences by Ron Wehrens, Springer (2011). Data used in the package are available from github. / GPL-2 noarch
r-chemospec 5.1.48 A collection of functions for top-down exploratory data analysis of spectral data including nuclear magnetic resonance (NMR), infrared (IR), Raman, X-ray fluorescence (XRF) and other similar types of spectroscopy. Includes functions for plotting and inspecting spectra, peak alignment, hierarchical cluster analysis (HCA), principal components analysis (PCA) and model-based clustering. Robust methods appropriate for this type of high-dimensional data are available. ChemoSpec is designed for structured experiments, such as metabolomics investigations, where the samples fall into treatment and control groups. Graphical output is formatted consistently for publication quality plots. ChemoSpec is intended to be very user friendly and to help you get usable results quickly. A vignette covering typical operations is available. / GPL-3 noarch
r-chemospec2d 0.3.166 A collection of functions for exploratory chemometrics of 2D spectroscopic data sets such as COSY (correlated spectroscopy) and HSQC (heteronuclear single quantum coherence) 2D NMR (nuclear magnetic resonance) spectra. ‘ChemoSpec2D’ deploys methods aimed primarily at classification of samples and the identification of spectral features which are important in distinguishing samples from each other. Each 2D spectrum (a matrix) is treated as the unit of observation, and thus the physical sample in the spectrometer corresponds to the sample from a statistical perspective. In addition to chemometric tools, a few tools are provided for plotting 2D spectra, but these are not intended to replace the functionality typically available on the spectrometer. ‘ChemoSpec2D’ takes many of its cues from ‘ChemoSpec’ and tries to create consistent graphical output and to be very user friendly. / GPL-3 noarch
r-chemospecutils 0.3.39 Functions supporting the common needs of packages ‘ChemoSpec’ and ‘ChemoSpec2D’. / GPL-3 noarch
r-cherry 0.6_12 Provides an alternative approach to multiple testing by calculating a simultaneous upper confidence bounds for the number of true null hypotheses among any subset of the hypotheses of interest, using the methods of Goeman and Solari (2011) <doi:10.1214/11-STS356>. / GPL-2 noarch
r-chff 0.1.0 The software matches the current history to the closest history in a time series to build a forecast. / GPL-3 noarch
r-chi 0.1 Light weight implementation of the standard distribution functions for the chi distribution, wrapping those for the chi-squared distribution in the stats package. / GPL-2 noarch
r-chi2x3way 1.1 Provides two index partitions for three-way contingency tables: partition of the association measure chi-squared and of the predictability index tau under several representative hypotheses about the expected frequencies (hypothesized probabilities). / GPL (> 2) noarch
r-chillmodels 1.0.0 Calculates the chilling and heat accumulation for studies of the temperate fruit trees. The models in this package are: Utah (Richardson et al., 1974, ISSN:0018-5345), Positive Chill Units - PCU (Linsley-Noaks et al., 1995, ISSN:1017-0316), GDH-A - Growing Degree Hours by Anderson et al.(1986, ISSN:0567-7572), GDH-R - Growing Degree Hours by Richardson et al.(1975, ISSN:0018-5345), North Carolina (Shaltout e Unrath, 1983, ISSN:0003-1062), Landsberg Model (Landsberg, 1974, ISSN:0305-7364), Q10 Model (Bidabe, 1967, ISSN:0031-9368), Jones Model (Jones et al., 2013 <DOI:10.1111/j.1438-8677.2012.00590.x>), Low-Chill Model (Gilreath and Buchanan, 1981, ISSN:0003-1062), Model for Cherry Sweetheart (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Model for apple Gala (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Taiwan Model (Lu et al., 2012 <DOI:10.17660/ActaHortic.2012.962.35>), Dynamic Model (Fishman et al., 1987, ISSN:0022-5193) adapted from the function Dynamic_Model() of the ‘chillR’ package (Luedeling, 2018), Unified Model (Chuine et al., 2016 <DOI:10.1111/gcb.13383>) and Heat Restriction model. / GPL-3 noarch
r-chiptest 1.0 Nonparametric Tests to identify the differential enrichment region for two conditions or time-course ChIP-seq data. It includes: data preprocessing function, estimation of a small constant used in hypothesis testing, a kernel-based two sample nonparametric test, two assumption-free two sample nonparametric test. / GPL (>= 2.15.1) linux-64, osx-64, win-64
r-chnosz 1.3.3 An integrated set of tools for thermodynamic calculations in aqueous geochemistry and geobiochemistry. Functions are provided for writing balanced reactions to form species from user-selected basis species and for calculating the standard molal properties of species and reactions, including the standard Gibbs energy and equilibrium constant. Calculations of the non-equilibrium chemical affinity and equilibrium chemical activity of species can be portrayed on diagrams as a function of temperature, pressure, or activity of basis species; in two dimensions, this gives a maximum affinity or predominance diagram. The diagrams have formatted chemical formulas and axis labels, and water stability limits can be added to Eh-pH, oxygen fugacity- temperature, and other diagrams with a redox variable. The package has been developed to handle common calculations in aqueous geochemistry, such as solubility due to complexation of metal ions, mineral buffers of redox or pH, and changing the basis species across a diagram (mosaic diagrams). CHNOSZ also has unique capabilities for comparing the compositional and thermodynamic properties of different proteins. / GPL-2 linux-64, osx-64, win-64
r-choicedes 0.9_3 Design functions for DCMs and other types of choice studies (including MaxDiff and other tradeoffs). / GPL-2 noarch
r-choicemodelr 1.2 Implements an MCMC algorithm to estimate a hierarchical multinomial logit model with a normal heterogeneity distribution. The algorithm uses a hybrid Gibbs Sampler with a random walk metropolis step for the MNL coefficients for each unit. Dependent variable may be discrete or continuous. Independent variables may be discrete or continuous with optional order constraints. Means of the distribution of heterogeneity can optionally be modeled as a linear function of unit characteristics variables. / GPL-3 noarch
r-cholwishart 1.0.1 Sampling from the Cholesky factorization of a Wishart random variable, sampling from the inverse Wishart distribution, sampling from the Cholesky factorization of an inverse Wishart random variable, sampling from the pseudo Wishart distribution, sampling from the generalized inverse Wishart distribution, computing densities for the Wishart and inverse Wishart distributions, and computing the multivariate gamma and digamma functions. / GPL-3 linux-64, osx-64, win-64
r-choplump 1.0_0.4 Choplump Tests are Permutation Tests for Comparing Two Groups with Some Positive but Many Zero Responses / GPL-3 noarch
r-chopthin 0.2.2 Resampling is a standard step in particle filtering and in sequential Monte Carlo. This package implements the chopthin resampler, which keeps a bound on the ratio between the largest and the smallest weights after resampling. / GPL-3 linux-64, osx-64, win-64
r-chor 0.0_4 Learning the structure of graphical models from datasets with thousands of variables. More information about the research papers detailing the theory behind Chordalysis is available at <http://www.francois-petitjean.com/Research> (KDD 2016, SDM 2015, ICDM 2014, ICDM 2013). The R package development site is <https://github.com/HerrmannM/Monash-ChoR>. / GPL-3 noarch
r-chords 0.95.4 Maximum likelihood estimation in respondent driven samples. / GPL-2 noarch
r-choroplethrmaps 1.0.1 Contains 3 maps. 1) US States 2) US Counties 3) Countries of the world. / BSD_3_clause noarch
r-chromomap 0.2 Provides interactive, configurable and elegant graphics visualization of the chromosomes or chromosome regions of any living organism allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the chromosome heatmap that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. Because of the large size of the chromosomes, it is impractical to visualize each element on the same plot. However, the plot provides a magnified view for each of chromosome locus to render additional information and visualization specific for that location. You can map thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order. The package provide multiple features like visualizing multiple sets, chromosome heat-maps, group annotations, adding hyperlinks, and labelling. The plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R ‘Shiny’ applications. / GPL-3 noarch
r-chron 2.3_53 Provides chronological objects which can handle dates and times. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
r-chunkr 1.1.1 Read tables chunk by chunk using a C backend and a simple R interface. / GPL-2 linux-64, osx-64, win-64
r-ciaawconsensus 1.3 Calculation of consensus values for atomic weights, isotope amount ratios, and isotopic abundances with the associated uncertainties using multivariate meta-regression approach for consensus building. / Unlimited noarch
r-ciee 0.1.1 In many studies across different disciplines, detailed measures of the variables of interest are available. If assumptions can be made regarding the direction of effects between the assessed variables, this has to be considered in the analysis. The functions in this package implement the novel approach CIEE (causal inference using estimating equations; Konigorski et al., 2018, <DOI:10.1002/gepi.22107>) for estimating and testing the direct effect of an exposure variable on a primary outcome, while adjusting for indirect effects of the exposure on the primary outcome through a secondary intermediate outcome and potential factors influencing the secondary outcome. The underlying directed acyclic graph (DAG) of this considered model is described in the vignette. CIEE can be applied to studies in many different fields, and it is implemented here for the analysis of a continuous primary outcome and a time-to-event primary outcome subject to censoring. CIEE uses estimating equations to obtain estimates of the direct effect and robust sandwich standard error estimates. Then, a large-sample Wald-type test statistic is computed for testing the absence of the direct effect. Additionally, standard multiple regression, regression of residuals, and the structural equation modeling approach are implemented for comparison. / GPL-2 noarch
r-cifsmry 1.0.1.1 Estimate of cumulative incidence function in two samples. Provide weighted summary statistics based on various methods and weights. / GPL-2 linux-64, osx-64, win-64
r-cim 1.0.0 Produces statistical indicators of the impact of migration on the socio-demographic composition of an area. Three measures can be used: ratios, percentages and the Duncan index of dissimilarity. The input data files are assumed to be in an origin-destination matrix format, with each cell representing a flow count between an origin and a destination area. Columns are expected to represent origins, and rows are expected to represent destinations. The first row and column are assumed to contain labels for each area. See Rodriguez-Vignoli and Rowe (2018) <doi:10.1080/00324728.2017.1416155> for technical details. / GPL-2 noarch
r-cin 0.1 Many experiments in neuroscience involve randomized and fast stimulation while the continuous outcome measures respond at much slower time scale, for example event-related fMRI. This package provide valid statistical tools with causal interpretation under these challenging settings, without imposing model assumptions. / GPL-2 noarch
r-cinid 1.2 This package provides functions to compute a method for identifying the instar of Curculionid larvae from the observed distribution of the headcapsule size of mature larvae. / GPL (>= 2.0) noarch
r-cinterpolate 1.0.0 Simple interpolation methods designed to be used from C code. Supports constant, linear and spline interpolation. An R wrapper is included but this package is primarily designed to be used from C code using ‘LinkingTo’. The spline calculations are classical cubic interpolation, e.g., Forsythe, Malcolm and Moler (1977) <ISBN: 9780131653320>. / MIT linux-64, osx-64, win-64
r-ciplot 1.0 Plot confidence interval from the objects of statistical tests such as t.test(), var.test(), cor.test(), prop.test() and fisher.test() (‘htest’ class), Tukey test [TukeyHSD()], Dunnett test [glht() in ‘multcomp’ package], logistic regression [glm()], and Tukey or Games-Howell test [posthocTGH() in ‘userfriendlyscience’ package]. Users are able to set the styles of lines and points. This package contains the function to calculate odds ratios and their confidence intervals from the result of logistic regression. / GPL-2 noarch
r-cir 2.0.0 Isotonic regression (IR), as well as a great small-sample improvement to IR called CIR, interval estimates for both, and additional utilities. / GPL-2 noarch
r-circmle 0.2.1 A series of wrapper functions to implement the 10 maximum likelihood models of animal orientation described by Schnute and Groot (1992) <DOI:10.1016/S0003-3472(05)80068-5>. The functions also include the ability to use different optimizer methods and calculate various model selection metrics (i.e., AIC, AICc, BIC). / GPL-2 noarch
r-circnntsr 2.2 Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, for plotting and more. / GPL-2 noarch
r-circoutlier 3.2.3 Detection of outliers in circular-circular regression models, modifying its and estimating of models parameters. / GPL-2 noarch
r-circstats 0.2_6 Circular Statistics, from Topics in Circular Statistics (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific. / GPL-2 noarch
r-circular 0.4_93 Circular Statistics, from Topics in circular Statistics (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific. / GPL-2 linux-64, osx-64, win-64
r-circularddm 0.1.0 Circular drift-diffusion model for continuous reports. / GPL-2 linux-64, osx-64, win-64
r-cirt 1.3.0 Jointly model the accuracy of cognitive responses and item choices within a bayesian hierarchical framework as described by Culpepper and Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package contains the datasets used within the analysis of the paper. / GPL-2 linux-64, osx-64, win-64
r-cit 2.2 A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. For example, it could be used to test for mediation of a known causal association between a DNA variant, the ‘instrumental variable’, and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables. / Artistic-2.0 linux-64, osx-64, win-64
r-citbcmst 1.0.4 This package implements the approach to assign tumor gene expression dataset to the 6 CIT Breast Cancer Molecular Subtypes described in Guedj et al 2012. / GPL-2 noarch
r-citccmst 1.0.2 This package implements the approach to assign tumor gene expression dataset to the 6 CIT Colon Cancer Molecular Subtypes described in Marisa et al 2013. / GPL-2 noarch
r-cityplot 2.0 Input: a csv-file for each database table and a controlfile describing relations between tables. Output: An extended ER diagram / LGPL-3 noarch
r-cklrt 0.2.3 Composite Kernel Machine Regression based on Likelihood Ratio Test (CKLRT): in this package, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function and we model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. (N. Zhao, H. Zhang, J. Clark, A. Maity, M. Wu. Composite Kernel Machine Regression based on Likelihood Ratio Test with Application for Combined Genetic and Gene-environment Interaction Effect (Submitted).) / GPL-3 linux-64, osx-64, win-64
r-ckmeans.1d.dp 4.2.2 Fast optimal univariate clustering and segementation by dynamic programming. Three types of problem including univariate k-means, k-median, and k-segments are solved with guaranteed optimality and reproducibility. The core algorithm minimizes the sum of within-cluster distances using respective metrics. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. Weighted k-means and unweighted k-segments algorithms can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. In contrast to heuristic methods, this package provides a powerful set of tools for univariate data analysis with guaranteed optimality. Use four spaces when indenting paragraphs within the Description. / LGPL-3 linux-64, osx-64, win-64
r-cla 0.95_1 Implements ‘Markovitz’ Critical Line Algorithm (‘CLA’) for classical mean-variance portfolio optimization, see Markovitz (1952) <doi:10.2307/2975974>. Care has been taken for correctness in light of previous buggy implementations. / GPL-3 noarch
r-cladorcpp 0.15.1 Various cladogenesis-related calculations that are slow in pure R are implemented in C with Rcpp. These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events. Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state). This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue. C implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations. CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it! Type: citation(package=cladoRcpp) to get the citation information. / GPL-2 linux-64, osx-64, win-64
r-clam 2.3.2 Performs ‘classical’ age-depth modelling of dated sediment deposits - prior to applying more sophisticated techniques such as Bayesian age-depth modelling. Any radiocarbon dated depths are calibrated. Age-depth models are constructed by sampling repeatedly from the dated levels, each time drawing age-depth curves. Model types include linear interpolation, linear or polynomial regression, and a range of splines. See Blaauw (2010). <doi:10.1016/j.quageo.2010.01.002>. / GPL-2 noarch
r-clamr 2.1_1 Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams. / GPL-3 noarch
r-clarifai 0.4.2 Get description of images from Clarifai API. For more information, see <http://clarifai.com>. Clarifai uses a large deep learning cloud to come up with descriptive labels of the things in an image. It also provides how confident it is about each of the labels. / MIT noarch
r-class 7.3_15 Various functions for classification, including k-nearest neighbour, Learning Vector Quantization and Self-Organizing Maps. / GPL-2 | GPL-3 linux-32, linux-64, osx-64, win-32, win-64
r-classifly 0.4 Given $p$-dimensional training data containing $d$ groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps curvilinear or multi-faceted. This package implements methods for understanding the division of space between the groups. / MIT noarch
r-classint 0.3_1 Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
r-cld2 1.2 Bindings to Google’s C library Compact Language Detector 2 (see <https://github.com/cld2owners/cld2#readme> for more information). Probabilistically detects over 80 languages in plain text or HTML. For mixed-language input it returns the top three detected languages and their approximate proportion of the total classified text bytes (e.g. 80% English and 20% French out of 1000 bytes). There is also a ‘cld3’ package on CRAN which uses a neural network model instead. / Apache License 2.0 linux-64, osx-64, win-64
r-cld3 1.2 Google’s Compact Language Detector 3 is a neural network model for language identification and the successor of ‘cld2’ (available from CRAN). The algorithm is still experimental and takes a novel approach to language detection with different properties and outcomes. It can be useful to combine this with the Bayesian classifier results from ‘cld2’. See <https://github.com/google/cld3#readme> for more information. / Apache License 2.0 linux-64, osx-64, win-64
r-cleancall 0.1.0 Wrapper of .Call() that runs exit handlers to clean up C resources. Helps managing C (non-R) resources while using the R API. / MIT linux-64, osx-64, win-64
r-cleandata 0.3.0 Functions to work with data frames to prepare data for further analysis. The functions for imputation, encoding, partitioning, and other manipulation can produce log files to keep track of process. / MIT noarch
r-cleanerr 0.1.1 How to deal with missing data?Based on the concept of almost functional dependencies, a method is proposed to fill missing data, as well as help you see what data is missing. The user can specify a measure of error and how many combinations he wish to test the dependencies against, the closer to the length of the dataset, the more precise. But the higher the number, the more time it will take for the process to finish. If the program cannot predict with the accuracy determined by the user it shall not fill the data, the user then can choose to increase the error or deal with the data another way. / MIT noarch
r-cleanr 1.2.0 Check your R code for some of the most common layout flaws. Many tried to teach us how to write code less dreadful, be it implicitly as B. W. Kernighan and D. M. Ritchie (1988) <ISBN:0-13-110362-8> in ‘The C Programming Language’ did, be it explicitly as R.C. Martin (2008) <ISBN:0-13-235088-2> in ‘Clean Code: A Handbook of Agile Software Craftsmanship’ did. So we should check our code for files too long or wide, functions with too many lines, too wide lines, too many arguments or too many levels of nesting. Note: This is not a static code analyzer like pylint or the like. Checkout <https://cran.r-project.org/package=lintr> instead. / BSD_2_clause noarch
r-cli 1.1.0 A suite of tools designed to build attractive command line interfaces (‘CLIs’). Includes tools for drawing rules, boxes, trees, and ‘Unicode’ symbols with ‘ASCII’ alternatives. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-clickclust 1.1.5 Clustering categorical sequences by means of finite mixtures with Markov model components is the main utility of ClickClust. The package also allows detecting blocks of equivalent states by forward and backward state selection procedures. / GPL-2 linux-64, osx-64, win-64
r-clickclustcont 0.1.7 Provides an expectation maximization (EM) algorithm to fit a mixture of continuous time Markov models for use with clickstream or other sequence type data. Gallaugher, M.P.B and McNicholas, P.D. (2018) <arXiv:1802.04849>. / GPL-2 noarch
r-clikcorr 1.0 A profile likelihood based method of estimation and inference on the correlation coefficient of bivariate data with different types of censoring and missingness. / GPL-2 noarch
r-climatestability 0.1.1 Climate stability measures are not formalized in the literature and tools for generating stability metrics from existing data are nascent. This package provides tools for calculating climate stability from raster data encapsulating climate change as a series of time slices. The methods follow Owens and Guralnick. Submitted, Biodiversity Informatics. / GPL-3 noarch
r-clime 0.4.1 A robust constrained L1 minimization method for estimating a large sparse inverse covariance matrix (aka precision matrix), and recovering its support for building graphical models. The computation uses linear programming. / GPL-2 noarch
r-clinfun 1.0.15 Utilities to make your clinical collaborations easier if not fun. It contains functions for designing studies such as Simon 2-stage and group sequential designs and for data analysis such as Jonckheere-Terpstra test and estimating survival quantiles. / GPL-2 linux-64, osx-64, win-64
r-clinicaltrialsummary 1.1.1 Provides estimates of several summary measures for clinical trials including the average hazard ratio, the weighted average hazard ratio, the restricted superiority probability ratio, the restricted mean survival difference and the ratio of restricted mean times lost, based on the short-term and long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) which accommodates various non-proportional hazards scenarios. The inference procedures and the asymptotic results for the summary measures are discussed in Yang (2018, <doi:10.1002/sim.7676>). / GPL-3 linux-64, osx-64, win-64
r-clinpk 0.9.0 Calculates equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function. / MIT noarch
r-clinsig 1.2 Functions for calculating clinical significance. / GPL-2 noarch
r-clinutidna 1.0 This package provides the estimation of an index measuring the clinical utility of DNA testing in the context of gene-environment interactions on a disease. The corresponding gene-environment interaction effect on the additive scale can also be obtained. The estimation is based on case-control or cohort data. The method was developed by Nguyen et al. 2013. / GPL-3 noarch
r-clipr 0.6.0 Simple utility functions to read from and write to the Windows, OS X, and X11 clipboards. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-clisymbols 1.2.0 A small subset of Unicode symbols, that are useful when building command line applications. They fall back to alternatives on terminals that do not support Unicode. Many symbols were taken from the ‘figures’ ‘npm’ package (see <https://github.com/sindresorhus/figures>). / MIT file LICENSE noarch
r-clogitboost 1.1 A set of functions to fit a boosting conditional logit model. / GPL-2 linux-64, osx-64, win-64
r-clogitl1 1.5 Tools for the fitting and cross validation of exact conditional logistic regression models with lasso and elastic net penalties. Uses cyclic coordinate descent and warm starts to compute the entire path efficiently. / GPL-2 linux-64, osx-64, win-64
r-cloneseeker 1.0.7 Defines the classes and functions used to simulate and to analyze data sets describing copy number variants and, optionally, sequencing mutations in order to detect clonal subsets. See Zucker et al. (2019) <doi:10.1093/bioinformatics/btz057>. / Apache License (== 2.0) noarch
r-cloudml 0.6.1 Interface to the Google Cloud Machine Learning Platform <https://cloud.google.com/ml-engine>, which provides cloud tools for training machine learning models. / Apache License 2.0 noarch
r-cloudutil 0.1.12 Provides means of plots for comparing utilization data of compute systems. / GPL-2 noarch
r-clrdag 0.6.0 Provides MLEdag() for constrained maximum likelihood estimation and likelihood ratio test of a large directed acyclic graph. The algorithms are described in the paper by Li, Shen, and Pan (2019) <doi:10.1080/01621459.2019.1623042>. / GPL-2 linux-64, osx-64, win-64
r-clsocp 1.0 This package provides and implementation of a one step smoothing newton method for the solution of second order cone programming problems, originally described by Xiaoni Chi and Sanyang Liu. / GPL-3 noarch
r-clttools 1.3 Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well. / GPL-2 noarch
r-clubsandwich 0.3.5 Provides several cluster-robust variance estimators (i.e., sandwich estimators) for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator introduced by Bell and McCaffrey (2002) <http://www.statcan.gc.ca/pub/12-001-x/2002002/article/9058-eng.pdf> and developed further by Pustejovsky and Tipton (2017) <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating the variance- covariance matrix and for testing single- and multiple- contrast hypotheses based on Wald test statistics. Tests of single regression coefficients use Satterthwaite or saddle-point corrections. Tests of multiple- contrast hypotheses use an approximation to Hotelling’s T-squared distribution. Methods are provided for a variety of fitted models, including lm() and mlm objects, glm(), ivreg() (from package ‘AER’), plm() (from package ‘plm’), gls() and lme() (from ‘nlme’), robu() (from ‘robumeta’), and rma.uni() and rma.mv() (from ‘metafor’). / GPL-3 noarch
r-clue 0.3_57 CLUster Ensembles. / GPL-2 linux-64, osx-64, win-64
r-cluer 1.4 CLUster Evaluation (CLUE) is a computational method for identifying optimal number of clusters in a given time-course dataset clustered by cmeans or kmeans algorithms and subsequently identify key kinases or pathways from each cluster. Its implementation in R is called ClueR. See README on <https://github.com/PengyiYang/ClueR> for more details. P Yang et al. (2015) <doi:10.1371/journal.pcbi.1004403>. / GPL-3 noarch
r-clues 0.5.9 We developed the clues R package to provide functions for automatically estimating the number of clusters and getting the final cluster partition without any input parameter except the stopping rule for convergence. The package also provides functions to evaluate and compare the performances of partitions of a data set both numerically and graphically. / GPL-2 linux-64, osx-64, win-64
r-cluscov 1.1.0 Clustered covariate regression enables estimation and inference in both linear and non-linear models with linear predictor functions even when the design matrix is column rank deficient. Routines in this package implement algorithms in Soale and Tsyawo (2019) <doi:10.13140/RG.2.2.32355.81441>. / GPL-2 linux-64, osx-64, win-64
r-clusrank 0.6_2 Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data. / GPL-3 linux-64, osx-64, win-64
r-clust.bin.pair 0.1.2 Tests, utilities, and case studies for analyzing significance in clustered binary matched-pair data. The central function clust.bin.pair uses one of several tests to calculate a Chi-square statistic. Implemented are the tests Eliasziw (1991) <doi:10.1002/sim.4780101211>, Obuchowski (1998) <doi:10.1002/(SICI)1097-0258(19980715)17:13%3C1495::AID-SIM863%3E3.0.CO;2-I>, Durkalski (2003) <doi:10.1002/sim.1438>, and Yang (2010) <doi:10.1002/bimj.201000035> with McNemar (1947) <doi:10.1007/BF02295996> included for comparison. The utility functions nested.to.contingency and paired.to.contingency convert data between various useful formats. Thyroids and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989) <doi:10.1016/0165-1781(89)90196-0> respectively. / MIT noarch
r-cluster 2.0.8 Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) Finding Groups in Data. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
r-cluster.datasets 1.0_1 A collection of data sets for teaching cluster analysis. / GPL-2 noarch
r-clusterbootstrap 1.0.0 Provides functionality for the analysis of clustered data using the cluster bootstrap. / GPL-3 noarch
r-clustercrit 1.2.8 Compute clustering validation indices. / GPL-2 linux-64, osx-64, win-64
r-clustergeneration 1.3.4 We developed the clusterGeneration package to provide functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables. / GPL-2 noarch
r-clustergenomics 1.0 The Partitioning Algorithm based on Recursive Thresholding (PART) is used to recursively uncover clusters and subclusters in the data. Functionality is also available for visualization of the clustering. / Artistic-2.0 noarch
r-clusterhap 0.1 One haplotype is a combination of SNP (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci). clusterhap groups together all individuals of a population with the same haplotype. Each group contains individual with the same allele in each SNP, whether or not missing data. Thus, clusterhap groups individuals, that to be imputed, have a non-zero probability of having the same alleles in the entire sequence of SNP’s. Moreover, clusterhap calculates such probability from relative frequencies. / GPL-3 noarch
r-clustering.sc.dp 1.0 A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of Ckmeans.1d.dp to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp and backtracking.sc.dp can be used. / LGPL-3 linux-64, osx-64, win-64
r-clusternomics 0.1.1 Integrative context-dependent clustering for heterogeneous biomedical datasets. Identifies local clustering structures in related datasets, and a global clusters that exist across the datasets. / MIT noarch
r-clusternor 0.0_3 The clustering ‘NUMA’ Optimized Routines package or ‘clusternor’ is a highly optimized package for performing clustering in parallel with accelerations specifically targeting multi-core Non-Uniform Memory Access (‘NUMA’) hardware architectures. Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns (2019) <arXiv:1902.09527>. / Apache License 2.0 linux-64, osx-64, win-64
r-clusterpower 0.6.111 Calculate power for cluster randomized trials (CRTs) that compare two means, two proportions, or two counts using closed-form solutions. In addition, calculate power for cluster randomized crossover trials using Monte Carlo methods. For more information, see Reich et al. (2012) <doi:10.1371/journal.pone.0035564>. / GPL-2 noarch
r-clusterranktest 1.0 Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size. / GPL-2 | GPL-3 noarch
r-clusterrepro 0.9 This is a function for validating microarray clusters via reproducibility, based on the paper referenced below. / GPL-2 noarch
r-clustertend 1.4 Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins’ statistic is implemented. / GPL-2 noarch
r-clusteval 0.1 An R package that provides a suite of tools to evaluate clustering algorithms, clusterings, and individual clusters. / MIT linux-64, osx-64, win-64
r-clustmixtype 0.2_1 Functions to perform k-prototypes partitioning clustering for mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining and Knowledge Discovery 2, 283-304, <DOI:10.1023/A:1009769707641>. / GPL-2 noarch
r-clustmmdd 1.0.4 An implementation of a variable selection procedure in clustering by mixture models for discrete data (clustMMDD). Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem of variable selection and clustering is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated. / GPL-2 linux-64, osx-64, win-64
r-clustsig 1.1 A complimentary package for use with hclust; simprof tests to see which (if any) clusters are statistically different. The null hypothesis is that there is no a priori group structure. See Clarke, K.R., Somerfield, P.J., and Gorley R.N. 2008. Testing of null hypothesis in exploratory community analyses: similarity profiles and biota-environment linkage. J. Exp. Mar. Biol. Ecol. 366, 56-69. / GPL-2 noarch
r-clustvarlv 2.0.0 Functions for the clustering of variables around Latent Variables, for 2-way or 3-way data. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable. External variables measured on the same observations or/and additional information on the variables can be taken into account. A noise cluster or sparse latent variables can also be defined. / GPL-3 linux-64, osx-64, win-64
r-clv 0.3_2.1 Package contains most of the popular internal and external cluster validation methods ready to use for the most of the outputs produced by functions coming from package cluster. Package contains also functions and examples of usage for cluster stability approach that might be applied to algorithms implemented in cluster package as well as user defined clustering algorithms. / GPL-2 linux-64, osx-64, win-64
r-clvalid 0.6_6 Statistical and biological validation of clustering results. / LGPL-3 noarch
r-cmaes 1.0_11 Single objective optimization using a CMA-ES. / GPL-2 noarch
r-cmc 1.0 Calculation and plot of the stepwise Cronbach-Mesbah Curve / GPL-2 noarch
r-cmce 0.1.0 Implements the Bayesian calibration model described in Pratola and Chkrebtii (2018) <DOI:10.5705/ss.202016.0403> for stochastic and deterministic simulators. Additive and multiplicative discrepancy models are currently supported. See <http://www.matthewpratola.com/software> for more information and examples. / AGPL-3 noarch
r-cmf 1.0 Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix. / GPL-2 linux-64, osx-64, win-64
r-cmls 1.0_0 Solves multivariate least squares (MLS) problems subject to constraints on the coefficients, e.g., non-negativity, orthogonality, equality, inequality, monotonicity, unimodality, smoothness, etc. Includes flexible functions for solving MLS problems subject to user-specified equality and/or inequality constraints, as well as a wrapper function that implements 24 common constraint options. Also does k-fold or generalized cross-validation to tune constraint options for MLS problems. See ten Berge (1993, ISBN:9789066950832) for an overview of MLS problems, and see Goldfarb and Idnani (1983) <doi:10.1007/BF02591962> for a discussion of the underlying quadratic programming algorithm. / GPL-2 noarch
r-cmm 0.12 Quite extensive package for maximum likelihood estimation and weighted least squares estimation of categorical marginal models (CMMs; e.g., Bergsma and Rudas, 2002, <http://www.jstor.org/stable/2700006?; Bergsma, Croon and Hagenaars, 2009, <DOI:10.1007/b12532>. / GPL-2 noarch
r-cmna 1.0.2 Provides the source and examples for James P. Howard, II, Computational Methods for Numerical Analysis with R, <http://howardjp.github.io/cmna/>, a book on numerical methods in R. / BSD_2_clause noarch
r-cmocean 0.2 Perceptually uniform palettes for commonly used variables in oceanography as functions taking an integer and producing character vectors of colours. See Thyng, K.M., Greene, C.A., Hetland, R.D., Zimmerle, H.M. and S.F. DiMarco (2016) <doi:10.5670/oceanog.2016.66> for the guidelines adhered to when creating the palettes. / MIT noarch
r-cmpcontrol 1.0 The main purpose of this package is to juxtapose the different control limits obtained by modelling a data set through the COM-Poisson distribution vs. the classical Poisson distribution. Accordingly, this package offers the ability to compute the COM-Poisson parameter estimates and plot associated Shewhart control charts for a given data set. / GPL-2 | GPL-3 noarch
r-cmplot 3.3.3 Manhattan plot, a type of scatter plot, was widely used to display the association results. However, it is usually time-consuming and laborious for a non-specialist user to write scripts and adjust parameters of an elaborate plot. Moreover, the ever-growing traits measured have necessitated the integration of results from different Genome-wide association study researches. Circle Manhattan Plot is the first open R package that can lay out Genome-wide association study P-value results in both traditional rectangular patterns, QQ-plot and novel circular ones. United in only one bull’s eye style plot, association results from multiple traits can be compared interactively, thereby to reveal both similarities and differences between signals. / GPL-2 noarch
r-cmpprocess 1.0 A toolkit for flexible modeling of count processes where data (over- or under-) dispersion exists. Estimations can be obtained under two data constructs where one has: (1) data on number of events in an s-unit time interval, or (2) only wait-time data. This package is supplementary to the work set forth in Zhu et al. (2016) <doi:10.1080/00031305.2016.1234976>. / GPL-3 noarch
r-cmprsk 2.2_8 Estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154 <DOI:10.1214/aos/1176350951>, and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509, <DOI:10.1080/01621459.1999.10474144>. / GPL-2 linux-64, osx-64, win-64
r-cmprskqr 0.9.1 Estimation, testing and regression modeling of subdistribution functions in competing risks using quantile regressions, as described in Peng and Fine (2009) <DOI:10.1198/jasa.2009.tm08228>. / GPL-2 linux-64, osx-64, win-64
r-cmrutils 1.3.1 A collection of useful helper routines developed by students of the Center for Mathematical Research, Stankin, Moscow. / GPL-3 noarch
r-cmvnorm 1.0_6 Various utilities for the complex multivariate Gaussian distribution. / GPL-2 noarch
r-cna 2.2.0 Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2018) <doi:10.1017/psrm.2018.45>. CNA is related to Qualitative Comparative Analysis (QCA), but contrary to the latter, it is custom-built for uncovering causal structures with multiple outcomes and it builds causal models from the bottom up by gradually combining single factors to complex dependency structures until the requested thresholds of model fit are met. The new functionalities provided by this package version include functions for evaluating and benchmarking the correctness of CNA’s output, a function determining whether a solution is an INUS model, a function bringing non-INUS expressions into INUS form, and a function for identifying cyclic models. The package vignette has been updated accordingly. / GPL-2 linux-64, osx-64, win-64
r-cnorm 1.2.0 Conventional methods for producing standard scores in psychometrics or biometrics are often plagued with jumps or gaps (i.e., discontinuities) in norm tables and low confidence for assessing extreme scores. The continuous norming method introduced by A. Lenhard et al. (2016), <doi:10.1177/1073191116656437>, generates continuous test norm scores on the basis of the raw data from standardization samples, without requiring assumptions about the distribution of the raw data: Norm scores are directly established from raw data by modeling the latter ones as a function of both percentile scores and an explanatory variable (e.g., age). The method minimizes bias arising from sampling and measurement error, while handling marked deviations from normality, addressing bottom or ceiling effects and capturing almost all of the variance in the original norm data sample. / AGPL-3 noarch
r-cobiclust 0.1.0 Implementation of a probabilistic method for biclustering adapted to overdispersed count data. It is a Gamma-Poisson Latent Block Model. It also implements two selection criteria in order to select the number of biclusters. / GPL-2 noarch
r-cobra 0.99.4 This package performs prediction for regression-oriented problems, aggregating in a nonlinear scheme any basic regression machines suggested by the context and provided by the user. If the user has no valuable knowledge on the data, four defaults machines wrappers are implemented so as to cover a minimal spectrum of prediction methods. If necessary, the computations may be parallelized. The method is described in Biau, Fischer, Guedj and Malley (2013), COBRA: A Nonlinear Aggregation Strategy. / GPL-2 linux-64, osx-64, win-64
r-cobs 1.3_3 Qualitatively Constrained (Regression) Smoothing Splines via Linear Programming and Sparse Matrices. / GPL-2 linux-64, osx-64, win-64
r-coclust 0.3_2 A copula based clustering algorithm that finds clusters according to the complex multivariate dependence structure of the data generating process. The updated version of the algorithm is described in Di Lascio, F.M.L. and Giannerini, S. (2016). Clustering dependent observations with copula functions. Statistical Papers, p.1-17. <doi:10.1007/s00362-016-0822-3>. / GPL-2 noarch
r-coconut 1.0.2 Allows for pooled analysis of microarray data by batch-correcting control samples, and then applying the derived correction parameters to non-control samples to obtain bias-free, inter-dataset corrected data. / GPL-3 noarch
r-cocor 1.1_3 Statistical tests for the comparison between two correlations based on either independent or dependent groups. Dependent correlations can either be overlapping or nonoverlapping. A web interface is available on the website http://comparingcorrelations.org. A plugin for the R GUI and IDE RKWard is included. Please install RKWard from https://rkward.kde.org to use this feature. The respective R package ‘rkward’ cannot be installed directly from a repository, as it is a part of RKWard. / GPL-3 noarch
r-cocron 1.0_1 Statistical tests for the comparison between two or more alpha coefficients based on either dependent or independent groups of individuals. A web interface is available at http://comparingcronbachalphas.org. A plugin for the R GUI and IDE RKWard is included. Please install RKWard from https:// rkward.kde.org to use this feature. The respective R package ‘rkward’ cannot be installed directly from a repository, as it is a part of RKWard. / GPL-3 noarch
r-coda 0.19_2 Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-coda.base 0.2.1 A minimum set of functions to perform compositional data analysis using the log-ratio approach introduced by John Aitchison (1982) <http://www.jstor.org/stable/2345821>. Main functions have been implemented in c for better performance. / GPL-3 linux-64, osx-64, win-64
r-codadiags 1.0 Markov chain Monte Carlo burn-in based on bridge statistics, in the way of coda::heidel.diag, but including non asymptotic tabulated statistics. / GPL-3 linux-64, osx-64, win-64
r-codatags 1.43 Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) <doi:10.1101/636746>. / GPL-3 noarch
r-code 1.0.0 Generates all necessary C functions allowing the user to work with the compiled-code interface of ode() and bvptwp(). The implementation supports forcings and events. Also provides functions to symbolically compute Jacobians, sensitivity equations and adjoint sensitivities being the basis for sensitivity analysis. / GPL-2 noarch
r-codep 0.9_1 Computation of Multiscale Codependence Analysis and spatial eigenvector maps, as an additional feature. / GPL-3 linux-64, osx-64, win-64
r-codetools 0.2_16 Code analysis tools for R. / GPL linux-32, linux-64, noarch, osx-64, win-32, win-64
r-codina 1.1.1 Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <arXiv:1802.00828>. / GPL-2 noarch
r-coenocliner 0.2_2 Simulate species occurrence and abundances (counts) along gradients. / GPL-2 noarch
r-coenoflex 2.2_0 Simulates the composition of samples of vegetation according to gradient-based vegetation theory. Features a flexible algorithm incorporating competition and complex multi-gradient interaction. / GPL-2 linux-64, osx-64, win-64
r-coexist 1.0 species coexistence modeling under asymmetric dispersal and fluctuating source-sink dynamics;testing the proportion of coexistence scenarios driven by neutral and niche processes / GPL (>= 2.0) noarch
r-cofad 0.1.0 Contrast analysis for factorial designs is an alternative to the classical ANOVA approach with the advantage of testing focused hypothesis. The method is mainly based on Rosenthal, Rosnow and Rubin (2000, ISBN:978-0521659802) and Sedlmeier and Renkewitz (2018, ISBN:978-3868943214). / GPL-2 noarch
r-cofra 0.1002 Calculates complete functional regulation analysis and visualize the results in a single heatmap. The provided example data is for biological data but the methodology can be used for large data sets to compare quantitative entities that can be grouped. For example, a store might divide entities into cloth, food, car products etc and want to see how sales changes in the groups after some event. The theoretical background for the calculations are provided in New insights into functional regulation in MS-based drug profiling, Ana Sofia Carvalho, Henrik Molina & Rune Matthiesen, Scientific Reports <doi:10.1038/srep18826>. / GPL-2 noarch
r-coin 1.3_0 Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
r-coindeskr 0.1.0 Extract real-time Bitcoin price details by accessing ‘CoinDesk’ Bitcoin price Index API <https://www.coindesk.com/api/>. / CC0 noarch
r-cointmonitor 0.1.0 We propose a consistent monitoring procedure to detect a structural change from a cointegrating relationship to a spurious relationship. The procedure is based on residuals from modified least squares estimation, using either Fully Modified, Dynamic or Integrated Modified OLS. It is inspired by Chu et al. (1996) <DOI:10.2307/2171955> in that it is based on parameter estimation on a pre-break calibration period only, rather than being based on sequential estimation over the full sample. See the discussion paper <DOI:10.2139/ssrn.2624657> for further information. This package provides the monitoring procedures for both the cointegration and the stationarity case (while the latter is just a special case of the former one) as well as printing and plotting methods for a clear presentation of the results. / GPL-3 noarch
r-cointreg 0.2.0 Cointegration methods are widely used in empirical macroeconomics and empirical finance. It is well known that in a cointegrating regression the ordinary least squares (OLS) estimator of the parameters is super-consistent, i.e. converges at rate equal to the sample size T. When the regressors are endogenous, the limiting distribution of the OLS estimator is contaminated by so-called second order bias terms, see e.g. Phillips and Hansen (1990) <DOI:10.2307/2297545>. The presence of these bias terms renders inference difficult. Consequently, several modifications to OLS that lead to zero mean Gaussian mixture limiting distributions have been proposed, which in turn make standard asymptotic inference feasible. These methods include the fully modified OLS (FM-OLS) approach of Phillips and Hansen (1990) <DOI:10.2307/2297545>, the dynamic OLS (D-OLS) approach of Phillips and Loretan (1991) <DOI:10.2307/2298004>, Saikkonen (1991) <DOI:10.1017/S0266466600004217> and Stock and Watson (1993) <DOI:10.2307/2951763> and the new estimation approach called integrated modified OLS (IM-OLS) of Vogelsang and Wagner (2014) <DOI:10.1016/j.jeconom.2013.10.015>. The latter is based on an augmented partial sum (integration) transformation of the regression model. IM-OLS is similar in spirit to the FM- and D-OLS approaches, with the key difference that it does not require estimation of long run variance matrices and avoids the need to choose tuning parameters (kernels, bandwidths, lags). However, inference does require that a long run variance be scaled out. This package provides functions for the parameter estimation and inference with all three modified OLS approaches. That includes the automatic bandwidth selection approaches of Andrews (1991) <DOI:10.2307/2938229> and of Newey and West (1994) <DOI:10.2307/2297912> as well as the calculation of the long run variance. / GPL-3 noarch
r-cold 2.0_0 Performs regression analysis for longitudinal count data, allowing for serial dependence among observations from a given individual and two dimensional random effects on the linear predictor. Estimation is via maximization of the exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed; M. Helena Goncalves et al.(2007) <doi:10.1016/j.csda.2007.03.002>. / GPL-2 linux-64, osx-64, win-64
r-collectargs 0.4.0 We often want to take all (or most) of the objects in one environment (such as the parameter values of a function) and pass them to another. This might be calling a second function, or iterating over a list, calling the same function. These functions wrap often repeated code. Current stable version (committed on October 14, 2017). / MIT noarch
r-collections 0.1.6 Provides high performance container data types such as Queue, Stack, Deque, Dict and OrderedDict. Benchmarks <https://randy3k.github.io/collections/articles/benchmark.html> have shown that these containers are asymptotically more efficient than those offered by other packages. / MIT linux-64, osx-64, win-64
r-collesslike 1.0 Computation of Colless-Like, Sackin and cophenetic balance indices of a phylogenetic tree and study of the distribution of these balance indices under the alpha-gamma model. For more details see A. Mir, F. Rossello, L. Rotger (2013) <doi:10.1016/j.mbs.2012.10.005>, M. J. Sackin (1972) <doi:10.1093/sysbio/21.2.225>, D. H. Colless (1982) <doi:10.2307/2413420>. / GPL-2 noarch
r-collutils 1.0.5 Provides some low level functions for processing PLINK input and output files. / GPL-3 linux-64
r-colmozzie 1.1.1 Weekly notified dengue cases and climate variables in Colombo district Sri Lanka from 2008/ week-52 to 2014/ week-21. / CC0 noarch
r-coloredica 1.0.0 It implements colored Independent Component Analysis (Lee et al., 2011) and spatial colored Independent Component Analysis (Shen et al., 2014). They are two algorithms to perform ICA when sources are assumed to be temporal or spatial stochastic processes, respectively. / GPL-2 noarch
r-colorfulvennplot 2.4 Given 2-,3- or 4-dimensional data, plots a Venn diagram, i.e. ‘crossing circles’. The user can specify values, labels for each circle-group and unique colors for each plotted part. Here is what it would look like for a 3-dimensional plot: http://elliotnoma.files.wordpress.com/2011/02/venndiagram.png. To see what the 4-dimensional plot looks like, go to http://elliotnoma.files.wordpress.com/2013/03/4dplot.png. / GPL-2 noarch
r-colorhcplot 1.3.1 Build dendrograms with sample groups highlighted by different colors. Visualize results of hierarchical clustering analyses as dendrograms whose leaves and labels are colored according to sample grouping. Assess whether data point grouping aligns to naturally occurring clusters. / GPL-2 noarch
r-colorpalette 1.0_1 Different methods to generate a color palette based on a specified base color and a number of colors that should be created. / MIT noarch
r-colorr 1.0.0 Color palettes for EPL, MLB, NBA, NHL, and NFL teams. / MIT noarch
r-colorramps 2.3 Builds gradient color maps / GPL-3 noarch
r-colorspace 1.4_1 Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with an interactive palette picker (with either a Tcl/Tk or a shiny GUI). / BSD_3_clause file LICENSE linux-32, linux-64, osx-64, win-32, win-64
r-colortools 0.1.5 R package with handy functions to help users select and play with color schemes in an HSV color model / GPL-3 noarch
r-colourlovers 0.3.5 Provides access to the COLOURlovers <http://www.colourlovers.com/> API, which offers color inspiration and color palettes. / GPL-2 noarch
r-colourvalues 0.2.2 Maps one of the viridis colour palettes, or a user-specified palette to values. Viridis colour maps are created by Stéfan van der Walt and Nathaniel Smith. They were set as the default palette for the ‘Python’ ‘Matplotlib’ library, introduced at SciPy 2015 conference <http://scipy2015.scipy.org/ehome/index.php?eventid=115969&>. Other palettes available in this library have been derived from ‘RColorBrewer’ <https://CRAN.R-project.org/package=RColorBrewer> and ‘colorspace’ <https://CRAN.R-project.org/package=colorspace> packages. / GPL-3 linux-64, osx-64, win-64
r-colr 0.1.900 Powerful functions to select and rename columns in dataframes, lists and numeric types by ‘Perl’ regular expression. Regular expression (‘regex’) are a very powerful grammar to match strings, such as column names. / GPL-2 noarch
r-colt 0.1.1 A collection of command-line color styles based on the ‘crayon’ package. ‘Colt’ styles are defined in themes that can easily be switched, to ensure command line output looks nice on dark as well as light consoles. / MIT noarch
r-comat 0.3.0 Builds co-occurrence matrices based on spatial raster data. It includes creation of weighted co-occurrence matrices (wecoma) and integrated co-occurrence matrices (incoma; Vadivel et al. (2007) <doi:10.1016/j.patrec.2007.01.004>). / MIT linux-64, osx-64, win-64
r-combat 0.0.4 Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. Complementary to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal. Here we proposed COMBined Association Test (COMBAT) to incorporate strengths from multiple existing gene-based tests, including VEGAS, GATES and simpleM. Compared to individual tests, COMBAT shows higher overall performance and robustness across a wide range of genetic models. The algorithm behind this method is described in Wang et al (2017) <doi:10.1534/genetics.117.300257>. / GPL-2 noarch
r-combinat 0.0_8 routines for combinatorics / GPL-2 noarch
r-combineportfolio 0.4 Estimation of optimal portfolio weights as combination of simple portfolio strategies, like the tangency, global minimum variance (GMV) or naive (1/N) portfolio. It is based on a utility maximizing 8-fund rule. Popular special cases like the Kan-Zhou(2007) 2-fund and 3-fund rule or the Tu-Zhou(2011) estimator are nested. / GPL-2 noarch
r-combinepvalue 1.0 We offer two statistical tests to combine p-values: selfcontained.test vs competitive.test. The goal is to test whether a vector of pvalues are jointly significant when we combine them together. / GPL-3 noarch
r-combins 1.1_1 Series of partially balanced incomplete block designs (PBIB) based on the combinatory method (S) introduced in (Imane Rezgui et al, 2014) <doi:10.3844/jmssp.2014.45.48>; and it gives their associated U-type design. / GPL-3 noarch
r-combmsc 1.4.2.1 Functions for computing optimal convex combinations of model selection criteria based on ranks, along with utility functions for constructing model lists, MSCs, and priors on model lists. / GPL-2 noarch
r-comclim 0.9.5 Computes community climate statistics for volume and mismatch using species’ climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage. / GPL-3 noarch
r-comf 0.1.9 Functions to calculate various common and less common thermal comfort indices, convert physical variables, and evaluate the performance of thermal comfort indices. / GPL-2 noarch
r-comics 1.0.4 Provided are Computational methods for Immune Cell-type Subsets, including:(1) DCQ (Digital Cell Quantifier) to infer global dynamic changes in immune cell quantities within a complex tissue; and (2) VoCAL (Variation of Cell-type Abundance Loci) a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune-cell types, and then uses these quantitative traits to uncover the underlying DNA loci. / GPL-2 noarch
r-commandr 1.0.1 An S4 representation of the Command design pattern. The Operation class is a simple implementation using closures and supports forward and reverse (undo) evaluation. The more complicated Protocol framework represents each type of command (or analytical protocol) by a formal S4 class. Commands may be grouped and consecutively executed using the Pipeline class. Example use cases include logging, do/undo, analysis pipelines, GUI actions, parallel processing, etc. / Artistic-2.0 noarch
r-commentr 1.0.4 Functions to produce nicely formatted comments to use in R-scripts (or Latex/HTML/markdown etc). A comment with formatting is printed to the console and can then be copied to a script. / GPL-2 noarch
r-commonjavajars 1.0_6 Useful libraries for building a Java based GUI under R are provided. / GPL-2 noarch
r-commonmark 1.7 The CommonMark specification defines a rationalized version of markdown syntax. This package uses the ‘cmark’ reference implementation for converting markdown text into various formats including html, latex and groff man. In addition it exposes the markdown parse tree in xml format. The latest version of this package also adds support for Github extensions including tables, autolinks and strikethrough text. / BSD_2_clause file LICENSE linux-32, linux-64, osx-64, win-32, win-64
r-commonsmath 1.2.4 Java JAR files for the Apache Commons Mathematics Library for use by users and other packages. / Apache License 2.0 | file LICENSE noarch
r-commontrend 0.7_1 Directly extract and plot stochastic common trends from a cointegration system using different approaches, currently including Kasa (1992) and Gonzalo and Granger (1995). The approach proposed by Gonzalo and Granger, also known as Permanent-Transitory Decomposition, is widely used in macroeconomics and market microstructure literature. Kasa’s approach, on the other hand, has a nice property that it only uses the super consistent estimator: the cointegration vector ‘beta’. This package also provides functions calculate P-value from Johansen Statistics according to the approximation method proposed by Doornik (1998). Update: 0.7-1: Fix bugs in calculation alpha. Add formulas and more explanations. 0.6-1: Rewrite the description file. 0.5-1: Add functions to calculate P-value from Johansen statistic, and vice versa. / GPL-2 noarch
r-comorbidity 0.5.0 Computing comorbidity scores such as the weighted Charlson score (Charlson, 1987 <doi:10.1016/0021-9681(87)90171-8>) and the Elixhauser comorbidity score (Elixhauser, 1998 <doi:10.1097/00005650-199801000-00004>) using ICD-9-CM or ICD-10 codes (Quan, 2005 <doi:10.1097/01.mlr.0000182534.19832.83>). / GPL-3 noarch
r-comp2roc 1.1.4 Comparison of two ROC curves through the methodology proposed by Ana C. Braga. / GPL-2 noarch
r-compactr 0.1 Creates empty plots with compact axis notation to which users can add whatever they like (points, lines, text, etc.) The notation is more compact in the sense that the axis-labels and tick-labels are closer to the axis and the tick-marks are shorter. Also, if the plot appears as part of a matrix, the x-axis notation is suppressed unless the plot appears along the bottom row and the y-axis notation is suppress unless the plot appears along the left column. / GPL-2 noarch
r-compare 0.2_6 Functions to compare a model object to a comparison object. If the objects are not identical, the functions can be instructed to explore various modifications of the objects (e.g., sorting rows, dropping names) to see if the modified versions are identical. / GPL-2 noarch
r-comparec 1.3.1 Proposed by Harrell, the C index or concordance C, is considered an overall measure of discrimination in survival analysis between a survival outcome that is possibly right censored and a predictive-score variable, which can represent a measured biomarker or a composite-score output from an algorithm that combines multiple biomarkers. This package aims to statistically compare two C indices with right-censored survival outcome, which commonly arise from a paired design and thus resulting two correlated C indices. / GPL (>= 2.0) linux-64, osx-64, win-64
r-compareodm 1.2 Input: 2 ODM files (ODM version 1.3) Output: list of identical, matching, similar and differing data items / GPL-3 noarch
r-comparer 0.2.0 Quickly run experiments to compare the run time and output of code blocks. The function mbc() can make fast comparisons of code, and will calculate statistics comparing the resulting outputs. It can be used to compare model fits to the same data or see which function runs faster. The function ffexp() runs a function using all possible combinations of selected inputs. This is useful for comparing the effect of different parameter values. It can also run in parallel and automatically save intermediate results, which is very useful for long computations. / GPL-3 noarch
r-comparetests 1.2 A standard test is observed on all specimens. We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens. Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control. We treat the total sample as stratified two-phase sampling and use inverse probability weighting. We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar’s test for binary tests)). See: Katki HA, Li Y, Edelstein DW, Castle PE. Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) <doi:10.1002/sim.4422>. / GPL-3 noarch
r-compas 0.1 Manipulate and analyze 3-D structural geometry of Protein Data Bank (PDB) files. / GPL-3 linux-64, osx-64, win-64
r-compboost 0.1.0 C implementation of component-wise boosting implementation of component-wise boosting written in C to obtain high runtime performance and full memory control. The main idea is to provide a modular class system which can be extended without editing the source code. Therefore, it is possible to use R functions as well as C functions for custom base-learners, losses, logging mechanisms or stopping criteria. / MIT linux-64, osx-64, win-64
r-compglm 2.0 A function (which uses a similar interface to the `glm’ function) for the fitting of a Conway-Maxwell-Poisson GLM. There are also various methods for analysis of the model fit. The package also contains functions for the Conway-Maxwell-Poisson distribution in a similar interface to functions `dpois’, `ppois’ and `rpois’. The functions are generally quick, since the workhorse functions are written in C (thanks to the Rcpp package). / GPL-2 linux-64, osx-64, win-64
r-comphclust 1.0_3 Performs the complementary hierarchical clustering procedure and returns X’ (the expected residual matrix) and a vector of the relative gene importances. / GPL-2 linux-64, osx-64, win-64
r-complexity 1.1.1 Allows for the easy computation of complexity: the proportion of the parameter space in line with the hypothesis by chance. The package comes with a Shiny application in which the calculations can be conducted as well. / GPL-2 noarch
r-complognormal 3.0 Computes the probability density function, cumulative distribution function, quantile function, random numbers of any composite model based on the lognormal distribution. / GPL-2 noarch
r-compoisson 0.3 Provides routines for density and moments of the Conway-Maxwell-Poisson distribution as well as functions for fitting the COM-Poisson model for over/under-dispersed count data. / BSD noarch
r-compoissonreg 0.6.1 Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models to count data (Sellers & Shmueli, 2010) <doi:10.1214/09-AOAS306>. The package provides functions for model estimation, dispersion testing, and diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016) <doi:10.1016/j.csda.2016.01.007> is also supported. / GPL-2 | GPL-3 linux-64, osx-64, win-64
r-compound.cox 3.18 Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>, statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>, Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al. (2019)<DOI:10.1016/j.cmpb.2018.10.020>. Algorithms for generating correlated gene expressions are also available. / GPL-2 noarch
r-compquadform 1.4.3 Computes the distribution function of quadratic forms in normal variables using Imhof’s method, Davies’s algorithm, Farebrother’s algorithm or Liu et al.’s algorithm. / GPL-2 linux-64, osx-64, win-64
r-compr 1.0 Different tools for describing and analysing paired comparison data are presented. Main methods are estimation of products scores according Bradley Terry Luce model. A segmentation of the individual could be conducted on the basis of a mixture distribution approach. The number of classes can be tested by the use of Monte Carlo simulations. This package deals also with multi-criteria paired comparison data. / GPL-2 noarch
r-comprehenr 0.6.7 Provides ‘Python’-style list comprehensions. List comprehension expressions use usual loops (for(), while() and repeat()) and usual if() as list producers. In many cases it gives more concise notation than standard *apply filter strategy. / GPL-2 noarch
r-compute.es 0.2_4 This package contains several functions for calculating the most widely used effect sizes (ES), along with their variances, confidence intervals and p-values. The output includes ES’s of d (mean difference), g (unbiased estimate of d), r (correlation coefficient), z’ (Fisher’s z), and OR (odds ratio and log odds ratio). In addition, NNT (number needed to treat), U3, CLES (Common Language Effect Size) and Cliff’s Delta are computed. This package uses recommended formulas as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009). / GPL-2 noarch
r-comtradr 0.2.2 Interface with and extract data from the United Nations Comtrade API <https://comtrade.un.org/data/>. Comtrade provides country level shipping data for a variety of commodities, these functions allow for easy API query and data returned as a tidy data frame. / GPL-3 noarch
r-con2aqi 0.1.0 To calculate the AQI (Air Quality Index) from pollutant concentration data. O3, PM2.5, PM10, CO, SO2, and NO2 are available currently. The method can be referenced at Environmental Protection Agency, United States as follows: EPA (2016) <https://www3.epa.gov/airnow/aqi-technical-assistance-document-may2016.pdf>. / GPL-3 noarch
r-conake 1.0 Continuous smoothing of probability density function on a compact or semi-infinite support is performed using four continuous associated kernels: extended beta, gamma, lognormal and reciprocal inverse Gaussian. The cross-validation technique is also implemented for bandwidth selection. / GPL-2 noarch
r-concatenate 1.0.0 Simple functions for joining strings. Construct human-friendly messages whose elements aren’t known in advance, like in stop, warning, or message, from clean code. / GPL (>= 3.2) noarch
r-conclust 1.1 There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output. / GPL-3 noarch
r-conconpiwifun 0.4.6 Continuous convex piecewise linear (ccpl) resp. quadratic (ccpq) functions can be implemented with sorted breakpoints and slopes. This includes functions that are ccpl (resp. ccpq) on a convex set (i.e. an interval or a point) and infinite out of the domain. These functions can be very useful for a large class of optimisation problems. Efficient manipulation (such as log(N) insertion) of such data structure is obtained with map standard template library of C (that hides balanced trees). This package is a wrapper on such a class based on Rcpp modules. / GPL-2 linux-64, osx-64, win-64
r-concor 1.0_0.1 The four functions svdcp (cp for column partitioned), svdbip or svdbip2 (bip for bi-partitioned), and svdbips (s for a simultaneous optimization of one set of r solutions), correspond to a SVD by blocks notion, by supposing each block depending on relative subspaces, rather than on two whole spaces as usual SVD does. The other functions, based on this notion, are relative to two column partitioned data matrices x and y defining two sets of subsets xi and yj of variables and amount to estimate a link between xi and yj for the pair (xi, yj) relatively to the links associated to all the other pairs. / GPL-3 noarch
r-concordance 1.6 A set of utilities for matching products in different classification codes used in international trade research. It supports concordance between HS (Combined), ISIC Rev. 2,3, and SITC1,2,3,4 product classification codes, as well as BEC, NAICS, and SIC classifications. It also provides code nomenclature / descriptions look-up, Rauch classification look-up (via concordance to SITC2) and trade elasticity look-up (via concordance to SITC2/3 or HS3.ss). / GPL-2 noarch
r-concreg 0.6 Implements concordance regression which can be used to estimate generalized odds of concordance. Can be used for non- and semi-parametric survival analysis with non-proportional hazards, for binary and for continuous outcome data. / GPL-3 linux-64, osx-64, win-64
r-conditionz 0.1.0 Provides ability to control how many times in function calls conditions are thrown (shown to the user). Includes control of warnings and messages. / MIT noarch
r-condmvnorm 2015.2_1 Computes conditional multivariate normal probabilities, random deviates and densities. / GPL-2 noarch
r-condreg 0.20 Based on url{http://statistics.stanford.edu/~ckirby/techreports/GEN/2012/2012-10.pdf} / GPL-3 noarch
r-condvis 0.5_1 Exploring fitted models by interactively taking 2-D and 3-D sections in data space. / GPL-2 noarch
r-coneproj 1.14 Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013)<doi:10.1080/03610918.2012.659820> for more details. / GPL-2 linux-64, osx-64, win-64
r-conf.design 2.0.0 This small library contains a series of simple tools for constructing and manipulating confounded and fractional factorial designs. / GPL-2 noarch
r-config 0.3 Manage configuration values across multiple environments (e.g. development, test, production). Read values using a function that determines the current environment and returns the appropriate value. / GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-configparser 1.0.0 Enhances the ‘ini’ package by adding the ability to interpolate variables. The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module) and the keys can be read, where ‘%(….)s’ instances are interpolated by other included options or outside variables. / GPL-3 noarch
r-confinterpret 1.0.0 Produces descriptive interpretations of confidence intervals. Includes (extensible) support for various test types, specified as sets of interpretations dependent on where the lower and upper confidence limits sit. Provides plotting functions for graphical display of interpretations. / AGPL-3 noarch
r-confintvariance 1.0.2 Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) <doi:10.1080/15598608.2016.1158675> and the references therein for an overview of unbiased estimation of variances of U-statistics. / GPL-3 noarch
r-conflicted 1.0.4 R’s default conflict management system gives the most recently loaded package precedence. This can make it hard to detect conflicts, particularly when they arise because a package update creates ambiguity that did not previously exist. ‘conflicted’ takes a different approach, making every conflict an error and forcing you to choose which function to use. / GPL-3 noarch
r-confreq 1.5.4_3 Offers several functions for Configural Frequencies Analysis (CFA), which is a useful statistical tool for the analysis of multiway contingency tables. CFA was introduced by G. A. Lienert as ‘Konfigurations Frequenz Analyse - KFA’. Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115. / GPL-3 noarch
r-confsam 0.2 For multiple testing. Computes estimates and confidence bounds for the False Discovery Proportion (FDP), the fraction of false positives among all rejected hypotheses. The methods in the package use permutations of the data. Doing so, they take into account the dependence structure in the data. / GPL-3 noarch
r-conics 0.3 plot conics (ellipses, hyperbolas, parabolas) / GPL-2 noarch
r-conjointchecks 0.0.9 Implementation of a procedure (Domingue, 2012; see also Karabatsos, 2001 and Kyngdon, 2011) to test the single and double cancellation axioms of conjoint measure in data that is dichotomously coded and measured with error. / GPL-2 noarch
r-connmattools 0.3.3 Collects several different methods for analyzing and working with connectivity data in R. Though primarily oriented towards marine larval dispersal, many of the methods are general and useful for terrestrial systems as well. / GPL-2 noarch
r-conover.test 1.1.5 Computes the Conover-Iman test (1979) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Conover-Iman test may be understood as a test for median difference. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected. / GPL-2 noarch
r-conquestr 0.3.7 Extends ‘ACER ConQuest’ by allowing R users to call ‘ACER ConQuest’ from within R. The user can also access ‘ACER ConQuest’ data objects by reading ‘ACER ConQuest’ System Files (generated by the ‘ACER ConQuest’ command ‘put’). This is of particular use to those who are parsing text file output (e.g., ‘show’ files) as this is not consistent across releases of ‘ACER ConQuest’. Requires ‘ACER ConQuest’ version 4.29.3 or later. A demonstration version can be downloaded from <https://shop.acer.edu.au/acer-conquest-4>. / GPL-3 noarch
r-conspline 1.2 Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape constrained regression spline. Function outputs fits and various types of inference. / GPL-2 | GPL-3 noarch
r-constants 0.0.2 CODATA internationally recommended values of the fundamental physical constants, provided as symbols for direct use within the R language. Optionally, the values with errors and/or the values with units are also provided if the ‘errors’ and/or the ‘units’ packages are installed. The Committee on Data for Science and Technology (CODATA) is an interdisciplinary committee of the International Council for Science which periodically provides the internationally accepted set of values of the fundamental physical constants. This package contains the 2014 CODATA version, published on 25 June 2015: Mohr, P. J., Newell, D. B. and Taylor, B. N. (2016) <DOI:10.1103/RevModPhys.88.035009>, <DOI:10.1063/1.4954402>. / MIT noarch
r-constellation 0.2.0 Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called constellations of signs and symptoms, because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows. / GPL-2 noarch
r-container 0.3.0 Common container data structures deque, set and dict (resembling ‘Python’s dict type) with typical member functions to insert, delete and access container elements. Provides iterators and reference semantics. / GPL-3 noarch
r-contfrac 1.1_12 Various utilities for evaluating continued fractions. / GPL-2 linux-64, osx-64, win-64
r-conting 1.7 Bayesian analysis of complete and incomplete contingency tables. / GPL-2 noarch
r-contourfunctions 0.1.1 Provides functions for making contour plots. The contour plot can be created from grid data, a function, or a data set. If non-grid data is given, then a Gaussian process is fit to the data and used to create the contour plot. / GPL-3 noarch
r-controltest 1.1.0 Nonparametric two-sample procedure for comparing survival quantiles. / GPL-3 noarch
r-convergenceclubs 1.4.3 Functions for clustering regions that form convergence clubs, according to the definition of Phillips and Sul (2009) <doi:10.1002/jae.1080>. / GPL-3 noarch
r-convergenceconcepts 1.2.1 This is a pedagogical package, designed to help students understanding convergence of random variables. It provides a way to investigate interactively various modes of convergence (in probability, almost surely, in law and in mean) of a sequence of i.i.d. random variables. Visualisation of simulated sample paths is possible through interactive plots. The approach is illustrated by examples and exercises through the function ‘investigate’, as described in Lafaye de Micheaux and Liquet (2009) <http://dx.doi.org/10.1198/tas.2009.0032>. The user can study his/her own sequences of random variables. / GPL-2 noarch
r-convertgraph 0.1 Converts graphical file formats (SVG, PNG, JPEG, BMP, GIF, PDF, etc) to one another. The exceptions are the SVG file format that can only be converted to other formats and in contrast, PDF format, which can only be created from others graphical formats. The main purpose of the package was to provide a solution for converting SVG file format to PNG which is often needed for exporting graphical files produced by R widgets. / MIT noarch
r-coop 0.6_2 Fast implementations of the co-operations: covariance, correlation, and cosine similarity. The implementations are fast and memory-efficient and their use is resolved automatically based on the input data, handled by R’s S3 methods. Full descriptions of the algorithms and benchmarks are available in the package vignettes. / BSD_2_clause linux-64, osx-64, win-64
r-coppecosenzar 0.1.3 The program implements the COPPE-Cosenza Fuzzy Hierarchy Model. The model was based on the evaluation of local alternatives, representing regional potentialities, so as to fulfill demands of economic projects. After defining demand profiles in terms of their technological coefficients, the degree of importance of factors is defined so as to represent the productive activity. The method can detect a surplus of supply without the restriction of the distance of classical algebra, defining a hierarchy of location alternatives. In COPPE-Cosenza Model, the distance between factors is measured in terms of the difference between grades of memberships of the same factors belonging to two or more sets under comparison. The required factors are classified under the following linguistic variables: Critical (CR); Conditioning (C); Little Conditioning (LC); and Irrelevant (I). And the alternatives can assume the following linguistic variables: Excellent (Ex), Good (G), Regular (R), Weak (W), Empty (Em), Zero (Z) and Inexistent (In). The model also provides flexibility, allowing different aggregation rules to be performed and defined by the Decision Maker. Such feature is considered in this package, allowing the user to define other aggregation matrices, since it considers the same linguistic variables mentioned. / GPL-2 noarch
r-copula 0.999_19.1 Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function. / GPL (>= 3) | file LICENCE linux-32, linux-64, osx-64, win-32, win-64
r-copula.markov 2.4 Estimation and statistical process control are performed under copula-based time-series models. Available are statistical methods in Long and Emura (2014 JCSA), Emura et al. (2017 Commun Stat-Simul) <DOI:10.1080/03610918.2015.1073303>, Huang and Emura(2019, in revision) and Huang, Chen and Emura (2019-, in revision). / GPL-2 noarch
r-copula.surv 1.0 Perform association analysis of bivariate survival data based on copula models. Two different ways to estimate the association parameter in copula models are implemented. A goodness-of-fit test for a given copula model is implemented. See Emura, Lin and Wang (2010) <doi.org/10.1016/j.csda.2010.03.013> for details. / GPL-2 noarch
r-copuladata 0.0_1 Data sets used for copula modeling in addition to those in the package ‘copula’. These include a random subsample from the US National Education Longitudinal Study (NELS) of 1988 and nursing home data from Wisconsin. / GPL-3 noarch
r-coranking 0.1.4 Calculates the co-ranking matrix to assess the quality of a dimensionality reduction. / GPL-3 linux-64, osx-64, win-64
r-corbin 0.3.1 We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S., Hou, L. and Zhao, H. CorBin: An efficient R package to generate high-dimensional binary data with correlation structures. Submitted to Journal of Statistical Software. / GPL-3 noarch
r-corclass 0.1.1 Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules. / GPL-2 noarch
r-cord 0.1.1 Partition data points (variables) into communities/clusters, similar to clustering algorithms, such as k-means and hierarchical clustering. This package implements a clustering algorithm based on a new metric CORD, defined for high dimensional parametric or semi-parametric distributions. Read http://arxiv.org/abs/1508.01939 for more details. / GPL-3 linux-64, osx-64, win-64
r-core 3.0 given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs. / GPL-2 noarch
r-corelearn 1.53.1 A suite of machine learning algorithms written in C with the R interface contains several learning techniques for classification and regression. Predictive models include e.g., classification and regression trees with optional constructive induction and models in the leaves, random forests, kNN, naive Bayes, and locally weighted regression. All predictions obtained with these models can be explained and visualized with the ‘ExplainPrediction’ package. This package is especially strong in feature evaluation where it contains several variants of Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, and DKM. These methods can be used for feature selection or discretization of numeric attributes. The OrdEval algorithm and its visualization is used for evaluation of data sets with ordinal features and class, enabling analysis according to the Kano model of customer satisfaction. Several algorithms support parallel multithreaded execution via OpenMP. The top-level documentation is reachable through ?CORElearn. / GPL-3 linux-64, osx-64, win-64
r-corenlp 0.4_2 Provides a minimal interface for applying annotators from the ‘Stanford CoreNLP’ java library. Methods are provided for tasks such as tokenisation, part of speech tagging, lemmatisation, named entity recognition, coreference detection and sentiment analysis. / GPL-2 noarch
r-coretdt 1.0 Use to analysis case-parent trio sequencing studies. Test the compound heterozygous and recessive disease models / GPL-3 noarch
r-corlink 1.0.0 A matrix of agreement patterns and counts for record pairs is the input for the procedure. An EM algorithm is used to impute plausible values for missing record pairs. A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilities that each pair is a true match - i.e. constitutes the same individual. / CC0 noarch
r-coroica 1.0.1 Contains an implementation of a confounding robust independent component analysis (ICA) for noisy and grouped data. The main function coroICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding based on user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/coroICA/>. / AGPL-3 noarch
r-corpcor 1.6.9 Implements a James-Stein-type shrinkage estimator for the covariance matrix, with separate shrinkage for variances and correlations. The details of the method are explained in Schafer and Strimmer (2005) <DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007) <DOI:10.2202/1544-6115.1252>. The approach is both computationally as well as statistically very efficient, it is applicable to small n, large p data, and always returns a positive definite and well-conditioned covariance matrix. In addition to inferring the covariance matrix the package also provides shrinkage estimators for partial correlations and partial variances. The inverse of the covariance and correlation matrix can be efficiently computed, as well as any arbitrary power of the shrinkage correlation matrix. Furthermore, functions are available for fast singular value decomposition, for computing the pseudoinverse, and for checking the rank and positive definiteness of a matrix. / GPL-3 noarch
r-corpora 0.5 Utility functions for the statistical analysis of corpus frequency data. This package is a companion to the open-source course Statistical Inference: A Gentle Introduction for Computational Linguists and Similar Creatures (‘SIGIL’). / GPL-3 noarch
r-corporacoco 1.1_0 A set of functions used to compare co-occurrence between two corpora. / GPL-3 noarch
r-corpus 0.10.0 Text corpus data analysis, with full support for international text (Unicode). Functions for reading data from newline-delimited ‘JSON’ files, for normalizing and tokenizing text, for searching for term occurrences, and for computing term occurrence frequencies, including n-grams. / Apache License (== 2.0) | file LICENSE linux-64, osx-64, win-64
r-corrcoverage 1.0.0 Using a computationally efficient method, the package can be used to find the corrected coverage estimate of a credible set of putative causal variants from Bayesian genetic fine-mapping. The package can also be used to obtain a corrected credible set if required; that is, the smallest set of variants required such that the corrected coverage estimate of the resultant credible set is within some user defined accuracy of the desired coverage. Maller et al. (2012) <doi:10.1038/ng.2435>, Wakefield (2009) <doi:10.1002/gepi.20359>, Fortune and Wallace (2018) <doi:10.1093/bioinformatics/bty898>. / MIT linux-64, osx-64
r-corrdna 1.0.1 Can be useful for finding associations among different positions in a position-wise aligned sequence dataset. The approach adopted for finding associations among positions is based on the latent multivariate normal distribution. / GPL-2 noarch
r-correctedfdr 1.0 There are many estimators of false discovery rate. In this package we compute the Nonlocal False Discovery Rate (NFDR) and the estimators of local false discovery rate: Corrected False discovery Rate (CFDR), Re-ranked False Discovery rate (RFDR) and the blended estimator. Bickel, D. R. (2016) <http://hdl.handle.net/10393/34277>. / LGPL-3 noarch
r-correctoverloadedpeaks 1.2.17 Analyzes and modifies metabolomics raw data (generated using GC-APCI-MS, Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in xcmsRaw format are accepted as input and mzXML files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or Isotopic-Ratio approach, QC plots are generated and corrected data are stored within the original xcmsRaw or mzXML respectively to allow further processing. / GPL-3 noarch
r-correlplot 1.0_2 Correlplot contains diverse routines for the construction of different plots for representing correlation matrices. / GPL-2 noarch
r-corrmixed 0.1_13 In clinical practice and research settings in medicine and the behavioral sciences, it is often of interest to quantify the correlation of a continuous endpoint that was repeatedly measured (e.g., test-retest correlations, ICC, etc.). This package allows for estimating these correlations based on mixed-effects models. Part of this software has been developed using funding provided from the European Union’s 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552. / GPL-2 noarch
r-corrplot 0.84 A graphical display of a correlation matrix or general matrix. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc. / GPL-3 noarch
r-corrsieve 1.6_8 Statistical summary of Structure output. / GPL-3 noarch
r-corset 0.1_4 Set of methods to constrain numerical series and time series within arbitrary boundaries. / GPL-3 noarch
r-cortest 0.9.8 There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. U are proportion to different types of correlation in 6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4(). We highly recommend ST5() according to the passage New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs published in Scientific Reports. / GPL-2 noarch
r-cortools 1.0 Designed for analysis of the results of a Genome Wide Association Study. Includes tools to pull lists of Chromosome number and SNP position below a certain significance threshold, refine gene networks (including data I/O for Cytoscape), and check SNP base pair changes. / Artistic-2.0 noarch
r-cosmofns 1.0_1 Package encapsulates standard expressions for distances, times, luminosities, and other quantities useful in observational cosmology, including molecular line observations. Currently coded for a flat universe only. / GPL-2 noarch
r-cost 0.1.0 Parameter estimation, one-step ahead forecast and new location prediction methods for spatio-temporal data. / GPL-3 noarch
r-costsensitive 0.1.2.10 Reduction-based techniques for cost-sensitive multi-class classification, in which each observation has a different cost for classifying it into one class, and the goal is to predict the class with the minimum expected cost for each new observation. Implements Weighted All-Pairs (Beygelzimer, A., Langford, J., & Zadrozny, B., 2008, <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B., 2005, <https://dl.acm.org/citation.cfm?id=1102358>) and Regression One-Vs-Rest. Works with arbitrary classifiers taking observation weights, or with regressors. Also implements cost-proportionate rejection sampling for working with classifiers that don’t accept observation weights. / BSD_2_clause linux-64, osx-64, win-64
r-cotrend 1.0.1 Implements cointegration/cotrending rank selection algorithm in Guo and Shintani (2013) Consistant cotrending rank selection when both stochastic and nonlinear deterministic trends are present. The Econometrics Journal 16: 473-483 <doi:10.1111/j.1368-423X.2012.00392.x>. Numbered examples correspond to Feb 2011 preprint <http://www.fas.nus.edu.sg/ecs/events/seminar/seminar-papers/05Apr11.pdf>. / GPL-3 noarch
r-couchdb 1.4.1 Interface to the couchDB document database <http://couchdb.apache.org>. / AGPL-3 noarch
r-countgmifs 0.0.1 Provides a function for fitting Poisson and negative binomial regression models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method. / GPL-2 noarch
r-counthmm 0.1.0 Provides tools for penalized estimation of flexible hidden Markov models for time series of counts w/o the need to specify a (parametric) family of distributions. These include functions for model fitting, model checking, and state decoding. For details, see Adam, T., Langrock, R., and Weiß, C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. <arXiv:1901.03275>. / GPL-3 noarch
r-countrycode 1.1.0 Standardize country names, convert them into one of eleven coding schemes, convert between coding schemes, and assign region descriptors. / GPL-3 noarch
r-covafillr 0.4.3 Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from ‘C’ based model builder tools such as ‘Rcpp’/’inline’, ‘TMB’, or ‘JAGS’. / BSD_2_clause linux-64, osx-64, win-64
r-covbm 0.1.0 Allows Brownian motion, fractional Brownian motion, and integrated Ornstein-Uhlenbeck process components to be added to linear and non-linear mixed effects models using the structures and methods of the ‘nlme’ package. / GPL-3 linux-64, osx-64, win-64
r-covfefe 0.1.0 Converts any word, sentence or speech into Trump’s infamous covfefe format. Reference: <https://www.nytimes.com/2017/05/31/us/politics/covfefe-trump-twitter.html>. Inspiration thanks to: <https://codegolf.stackexchange.com/questions/123685/covfefify-a-string>. / GPL-3 noarch
r-covr 3.3.1 Track and report code coverage for your package and (optionally) upload the results to a coverage service like ‘Codecov’ <http://codecov.io> or ‘Coveralls’ <http://coveralls.io>. Code coverage is a measure of the amount of code being exercised by a set of tests. It is an indirect measure of test quality and completeness. This package is compatible with any testing methodology or framework and tracks coverage of both R code and compiled C/C/FORTRAN code. / GPL-3 linux-64, osx-64, win-64
r-covreg 1.0 This package fits a simultaneous regression model for the mean vectors and covariance matrices of multivariate response variables, as described in Hoff and Niu (2012). The explanatory variables can be continuous or discrete. The current version of the package provides the Bayesian estimates. / GPL-2 noarch
r-covrobust 1.1_3 The cov.nnve() function implements robust covariance estimation by the nearest neighbor variance estimation (NNVE) method of Wang and Raftery (2002) <DOI:10.1198/016214502388618780>. / GPL-2 noarch
r-covsep 1.1.0 Functions for testing if the covariance structure of 2-dimensional data (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) = C_1 x C_2. A complete descriptions of the implemented tests can be found in the paper Aston, John A. D.; Pigoli, Davide; Tavakoli, Shahin. Tests for separability in nonparametric covariance operators of random surfaces. Ann. Statist. 45 (2017), no. 4, 1431–1461. <doi:10.1214/16-AOS1495> <https://projecteuclid.org/euclid.aos/1498636862> <arXiv:1505.02023>. / GPL-2 noarch
r-covtestr 0.1.4 Testing functions for Covariance Matrices. These tests include high-dimension homogeneity of covariance matrix testing described by Schott (2007) <doi:10.1016/j.csda.2007.03.004> and high-dimensional one-sample tests of covariance matrix structure described by Fisher, et al. (2010) <doi:10.1016/j.jmva.2010.07.004>. Covariance matrix tests use C to speed performance and allow larger data sets. / GPL-2 linux-64, osx-64, win-64
r-coxboost 1.4 This package provides routines for fitting Cox models by likelihood based boosting for a single endpoint or in presence of competing risks / GPL-2 linux-64, osx-64, win-64
r-coxme 2.2_14 This package fits Cox proportional hazards models containing both fixed and random effects. The random effects can have a general form, of which familial interactions (a kinship matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a frailty model. The approach is based on Rippatti and Palgren, Biometrics 2002. / LGPL-2 linux-64, osx-64, win-64
r-coxphf 1.13 Implements Firth’s penalized maximum likelihood bias reduction method for Cox regression which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function). The program fits profile penalized likelihood confidence intervals which were proved to outperform Wald confidence intervals. / GPL-3 linux-64, osx-64, win-64
r-coxphlb 1.2.0 Performs analysis of right-censored length-biased data using Cox model. It contains model fitting and checking, and the stationarity assumption test. The model fitting and checking methods are described in Qin and Shen (2010) <doi:10.1111/j.1541-0420.2009.01287.x> and Lee, Ning, and Shen (2018) <doi:10.1007/s10985-018-9422-y>. / GPL-2 noarch
r-coxphmic 0.1.0 Sparse estimation for Cox PH models is done via Minimum approximated Information Criterion (MIC) by Su, Wijayasinghe, Fan, and Zhang (2016) <DOI:10.1111/biom.12484>. MIC mimics the best subset selection using a penalized likelihood approach yet with no need of a tuning parameter. The problem is further reformulated with a re-parameterization step so that it reduces to one unconstrained non-convex yet smooth programming problem, which can be solved efficiently. Furthermore, the re-parameterization tactic yields an additional advantage in terms of circumventing post-selection inference. / GPL-2 noarch
r-coxphsgd 0.2.1 Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data. / GPL-2 noarch
r-coxphw 4.0.1 Implements weighted estimation in Cox regression as proposed by Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, <doi:10.1002/sim.3623>) and as described in Dunkler, Ploner, Schemper and Heinze (Journal of Statistical Software, 2018, <doi:10.18637/jss.v084.i02>). Weighted Cox regression provides unbiased average hazard ratio estimates also in case of non-proportional hazards. Approximated generalized concordance probability an effect size measure for clear-cut decisions can be obtained. The package provides options to estimate time-dependent effects conveniently by including interactions of covariates with arbitrary functions of time, with or without making use of the weighting option. / GPL-2 linux-64, osx-64, win-64
r-coxplus 1.1.1 A high performance package estimating Cox Model when an even has more than one causes. It also supports random and fixed effects, tied events, and time-varying variables. / GPL-3 linux-64, osx-64, win-64
r-coxridge 0.9.2 A package for fitting Cox models with penalized ridge-type partial likelihood. The package includes functions for fitting simple Cox models with all covariates controlled by a ridge penalty. The weight of the penalty is optimised by using a REML type-algorithm. Models with time varying effects of the covariates can also be fitted. Some of the covariates may be allowed to be fixed and thus not controlled by the penalty. There are three different penalty functions, ridge, dynamic and weighted dynamic. Time varying effects can be fitted without the need of an expanded dataset. / GPL-2 noarch
r-coxrobust 1.0 Fit robustly proportional hazards regression model / GPL-2 linux-64, osx-64, win-64
r-coxsei 0.1 It fits a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data. / GPL-2 linux-64, osx-64, win-64
r-cp 1.6 Functions for calculating the conditional power for different models in survival time analysis within randomized clinical trials with two different treatments to be compared and survival as an endpoint. / GPL-3 noarch
r-cpa 1.0 The package includes functions to test and compare causal models. / GPL-2 noarch
r-cpca 0.1.2 This package contains methods to perform Common Principal Component Analysis (CPCA). The stepwise method by Trendafilov is published in the current version. Please see Trendafilov (2010). Stepwise estimation of common principal components. Computational Statistics & Data Analysis, 54(12), 3446-3457. doi:10.1016/j.csda.2010.03.010 / GPL-3 noarch
r-cpcg 1.0 Solves system of linear equations using (preconditioned) conjugate gradient algorithm, with improved efficiency using Armadillo templated ‘C’ linear algebra library, and flexibility for user-specified preconditioning method. Please check <https://github.com/styvon/cPCG> for latest updates. / GPL-2 linux-64, osx-64, win-64
r-cpgassoc 2.60 Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites. / GPL-2 noarch
r-cpgfilter 1.1 Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design. / GPL-3 noarch
r-cpk 1.3_1 The package cpk provides simplified clinical pharmacokinetic functions for dose regimen design and modification at the point-of-care. Currently, the following functions are available: (1) ttc.fn for target therapeutic concentration, (2) dr.fn for dose rate, (3) di.fn for dosing interval, (4) dm.fn for maintenance dose, (5) bc.ttc.fn for back calculation, (6) ar.fn for accumulation ratio, (7) dpo.fn for orally administered dose, (8) cmax.fn for peak concentration, (9) css.fn for steady-state concentration, (10) cmin.fn for trough,(11) ct.fn for concentration-time predictions, (12) dlcmax.fn for calculating loading dose based on drug’s maximum concentration, (13) dlar.fn for calculating loading dose based on drug’s accumulation ratio, and (14) R0.fn for calculating drug infusion rate. Reference: Linares O, Linares A. Computational opioid prescribing: A novel application of clinical pharmacokinetics. J Pain Palliat Care Pharmacother 2011;25:125-135. / GPL-2 noarch
r-cplots 0.4_0 Provides functions to produce some circular plots for circular data, in a height- or area-proportional manner. They include barplots, smooth density plots, stacked dot plots, histograms, multi-class stacked smooth density plots, and multi-class stacked histograms. The new methodology for general area-proportional circular visualization is described in an article submitted (after revision) to Journal of Computational and Graphical Statistics. / GPL-2 noarch
r-cpm 2.2 Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points. / GPL-3 linux-64, osx-64, win-64
r-cpmcglm 1.2 We propose to determine the correction of the significance level after multiple coding of an explanatory variable in Generalized Linear Model. The different methods of correction of the p-value are the Single step Bonferroni procedure, and resampling based methods developed by P.H.Westfall in 1993. Resampling methods are based on the permutation and the parametric bootstrap procedure. If some continuous, and dichotomous transformations are performed this package offers an exact correction of the p-value developed by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available. / GPL (> 2) noarch
r-cpprouting 1.1 Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm. Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) <doi:10.1007/BF01386390>), bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) <https://pdfs.semanticscholar.org/0761/18dfbe1d5a220f6ac59b4de4ad07b50283ac.pdf>), A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) <doi:10.1109/TSSC.1968.300136>), new bidirectional A* (Pijls & Post (2009) <http://repub.eur.nl/pub/16100/ei2009-10.pdf>). / GPL-2 linux-64, osx-64, win-64
r-cprr 0.2.0 Calculate date of birth, age, and gender, and generate anonymous sequence numbers from CPR numbers. <https://en.wikipedia.org/wiki/Personal_identification_number_(Denmark)>. / GPL-3 noarch
r-cpt 1.0.2 Non-parametric test for equality of multivariate distributions. Trains a classifier to classify (multivariate) observations as coming from one of several distributions. If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the distributions are equal is rejected. / GPL-3 noarch
r-cptcity 1.0.4 Incorporates colour gradients from the ‘cpt-city’ web archive available at <http://soliton.vm.bytemark.co.uk/pub/cpt-city/>. / GPL-3 noarch
r-cptec 0.1.0 Allows to retrieve data from the ‘CPTEC/INPE’ weather forecast API. ‘CPTEC’ stands for ‘Centro de Previsão de Tempo e Estudos Climáticos’ and ‘INPE’ for ‘Instituto Nacional de Pesquisas Espaciais’. ‘CPTEC’ is the most advanced numerical weather and climate forecasting center in Latin America, with high-precision short and medium-term weather forecasting since the beginning of 1995. See <http://www.cptec.inpe.br/> for more information. / GPL-3 noarch
r-cqrreg 1.2 Estimate quantile regression(QR) and composite quantile regression (cqr) and with adaptive lasso penalty using interior point (IP), majorize and minimize(MM), coordinate descent (CD), and alternating direction method of multipliers algorithms(ADMM). / GPL-2 linux-64, osx-64, win-64
r-cr 1.0 This package contains R-functions to perform power calculation in a group sequential clinical trial with censored survival data and possibly unequal patient allocation between treatment and control groups. The fuctions can also be used to determine the study duration in a clinical trial with censored survival data as the sum of the accrual duration, which determines the sample size in a traditional sense, and the follow-up duration, which more or less controls the number of events to be observed. This package also contains R functions and methods to display the computed results. / GPL-2 noarch
r-crac 1.0 R functions for cosmological research. The main functions are similar to the python library, cosmolopy. / GPL-2 noarch
r-cramer 0.9_3 Provides R routine for the so called two-sample Cramer-Test. This nonparametric two-sample-test on equality of the underlying distributions can be applied to multivariate data as well as univariate data. It offers two possibilities to approximate the critical value both of which are included in this package. / GPL-2 noarch
r-crank 1.1_2 Functions for completing and recalculating rankings and sorting. / GPL-2 noarch
r-cranlogs 2.1.1 ‘API’ to the database of ‘CRAN’ package downloads from the ‘RStudio’ ‘CRAN mirror’. The database itself is at <http://cranlogs.r-pkg.org>, see <https://github.com/r-hub/cranlogs.app> for the raw ‘API’. / MIT noarch
r-crantastic 0.1 Various R tools for http://crantastic.org/ / GPL-2 noarch
r-crayon 1.3.4 Colored terminal output on terminals that support ‘ANSI’ color and highlight codes. It also works in ‘Emacs’ ‘ESS’. ‘ANSI’ color support is automatically detected. Colors and highlighting can be combined and nested. New styles can also be created easily. This package was inspired by the ‘chalk’ ‘JavaScript’ project. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-crblocks 1.0_0 Implements a statistical test for comparing bar plots or histograms of categorical data derived from a randomized block repeated measures layout. / GPL-3 noarch
r-cream 1.1.1 Provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). ‘CREAM’ uses genome-wide maps of genomic regions in the tissue or cell type of interest, such as those generated from chromatin-based assays including DNaseI, ATAC or ChIP-Seq. ‘CREAM’ considers proximity of the elements within chromosomes of a given sample to identify COREs in the following steps: 1) It identifies window size or the maximum allowed distance between the elements within each CORE, 2) It identifies number of elements which should be clustered as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which does not pass the threshold considered in the approach. / GPL-3 noarch
r-credentials 1.1 Setup and retrieve HTTPS and SSH credentials for use with ‘git’ and other services. For HTTPS remotes the package interfaces the ‘git-credential’ utility which ‘git’ uses to store HTTP usernames and passwords. For SSH remotes we provide convenient functions to find or generate appropriate SSH keys. The package both helps the user to setup a local git installation, and also provides a back-end for git/ssh client libraries to authenticate with existing user credentials. / MIT noarch
r-creditmetrics 0.0_2 A set of functions for computing the CreditMetrics risk model / Unlimited noarch
r-credsubs 1.0.1 Functions for constructing simultaneous credible bands and identifying subsets via the credible subsets (also called credible subgroups) method. / GPL-3 noarch
r-credule 0.1.3 It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS. / MIT linux-64, osx-64, win-64
r-crf 0.4_2 Implements modeling and computational tools for conditional random fields (CRF) model as well as other probabilistic undirected graphical models of discrete data with pairwise and unary potentials. / GPL-2 linux-64, osx-64, win-64
r-crfsuite 0.3 Wraps the ‘CRFsuite’ library <https://github.com/chokkan/crfsuite> allowing users to fit a Conditional Random Field model and to apply it on existing data. The focus of the implementation is in the area of Natural Language Processing where this R package allows you to easily build and apply models for named entity recognition, text chunking, part of speech tagging, intent recognition or classification of any category you have in mind. Next to training, a small web application is included in the package to allow you to easily construct training data. / BSD_3_clause linux-64, osx-64, win-64
r-crimcv 0.9.6 A finite mixture of Zero-Inflated Poisson (ZIP) models for analyzing criminal trajectories. / GPL-2 linux-64, osx-64, win-64
r-crisp 1.0.0 Implements convex regression with interpretable sharp partitions (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 <http://jmlr.org/papers/volume17/15-344/15-344.pdf>. / GPL-2 noarch
r-crm 1.2.4 Functions for phase I clinical trials using the continual reassessment method. / GPL-2 linux-64, osx-64, win-64
r-crn 1.1 The crn package provides the core functions required to download and format data from the Climate Reference Network. Both daily and hourly data are downloaded from the ftp, a consolidated file of all stations is created, station metadata is extracted. In addition functions for selecting individual variables and creating R friendly datasets for them is provided. / GPL-2 noarch
r-crochet 2.2.0 Functions to help implement the extraction / subsetting / indexing function ‘[‘ and replacement function ‘[<-‘ of custom matrix-like types (based on S3, S4, etc.), modeled as closely to the base matrix class as possible (with tests to prove it). / MIT noarch
r-crone 0.1.1 Functions to carry out the most important crystallographic calculations for crystal structures made of 1d Gaussian-shaped atoms, especially useful for methods development. Main reference: E. Smith, G. Evans, J. Foadi (2017) <doi:10.1088/1361-6404/aa8188>. / GPL-2 noarch
r-cronr 0.4.0 Create, edit, and remove ‘cron’ jobs on your unix-alike system. The package provides a set of easy-to-use wrappers to ‘crontab’. It also provides an RStudio add-in to easily launch and schedule your scripts. / MIT noarch
r-crop 0.0_2 A device closing function which is able to crop graphics (e.g., PDF, PNG files) on Unix-like operating systems with the required underlying command-line tools installed. / GPL-2 | GPL-3 noarch
r-cropdatape 1.0.0 Provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes 6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially, in excel files which has been transformed and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell. The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014. / MIT noarch
r-crossdes 1.1_1 Contains functions for the construction of carryover balanced crossover designs. In addition contains functions to check given designs for balance. / GPL-2 noarch
r-crossreg 1.0 This package provides functions to calculate confidence intervals for crossover points of two simple linear regression lines using the non-linear regression, the delta method, the Fieller method, and the bootstrap methods. / GPL-2 noarch
r-crosstalk 1.0.0 Provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e. static .html files). Currently supports linked brushing and filtering. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-crossva 0.9.9 Enables transformation of Verbal Autopsy data collected with the WHO 2016 questionnaire (versions 1.4.1 & 1.5.1) or the WHO 2014 questionnaire for automated coding of Cause of Death using the InSilicoVA (data.type = WHO2016) and InterVA5 algorithms. Previous versions of this package supported user-supplied mappings (via the map_records function), but this functionality has been removed. This package is made available by WHO and the Bloomberg Data for Health Initiative. / GPL-3 noarch
r-crossval 1.0.3 Contains generic functions for performing cross validation and for computing diagnostic errors. / GPL-3 noarch
r-crov 0.1.3 Fits a constrained regression model for an ordinal response with ordinal predictors and possibly others, Espinosa and Hennig (2018) <arXiv:1804.08715>. The parameter estimates associated with an ordinal predictor are constrained to be monotonic. If a monotonicity direction (isotonic or antitonic) is not specified for an ordinal predictor by the user, then the monotonicity direction classification procedure establishes it. A monotonicity test is also available to test the null hypothesis of monotonicity over a set of parameters associated with an ordinal predictor. / GPL-2 noarch
r-crp.csfp 2.0.2 Modelling credit risks based on the concept of CreditRisk, First Boston Financial Products, 1997 and CreditRisk in the Banking Industry, Gundlach & Lehrbass, Springer, 2003. / GPL-2 noarch
r-crrp 1.0 In competing risks regression, the proportional subdistribution hazards(PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for penalized variable selection for the PSH model. Penalties include LASSO, SCAD, MCP, and their group versions. / GPL-2 linux-64, osx-64, win-64
r-crrsc 1.1 Extension of cmprsk to Stratified and Clustered data. Goodness of fit test for Fine-Gray model. / GPL-2 linux-64, osx-64, win-64
r-crrstep 2015_2.1 Performs forward and backwards stepwise regression for the Proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events. / GPL-2 noarch
r-crseeventstudy 1.2 Based on Dutta et al. (2018) <doi:10.1016/j.jempfin.2018.02.004>, this package provides their standardized test for abnormal returns in long-horizon event studies. The methods used improve the major weaknesses of size, power, and robustness of long-run statistical tests described in Kothari/Warner (2007) <doi:10.1016/B978-0-444-53265-7.50015-9>. Abnormal returns are weighted by their statistical precision (i.e., standard deviation), resulting in abnormal standardized returns. This procedure efficiently captures the heteroskedasticity problem. Clustering techniques following Cameron et al. (2011) <10.1198/jbes.2010.07136> are adopted for computing cross-sectional correlation robust standard errors. The statistical tests in this package therefore accounts for potential biases arising from returns’ cross-sectional correlation, autocorrelation, and volatility clustering without power loss. / BSD_3_clause noarch
r-crskdiag 1.0.1 Provides the implementation of analytical and graphical approaches for checking the assumptions of the Fine and Gray model. / GPL-2 linux-64, osx-64, win-64
r-crsnls 0.2 Functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm. Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition. / GPL-2 noarch
r-crso 0.1.1 An algorithm for identifying candidate driver combinations in cancer. CRSO is based on a theoretical model of cancer in which a cancer rule is defined to be a collection of two or more events (i.e., alterations) that are minimally sufficient to cause cancer. A cancer rule set is a set of cancer rules that collectively are assumed to account for all of ways to cause cancer in the population. In CRSO every event is designated explicitly as a passenger or driver within each patient. Each event is associated with a patient-specific, event-specific passenger penalty, reflecting how unlikely the event would have happened by chance, i.e., as a passenger. CRSO evaluates each rule set by assigning all samples to a rule in the rule set, or to the null rule, and then calculating the total statistical penalty from all unassigned event. CRSO uses a three phase procedure find the best rule set of fixed size K for a range of Ks. A core rule set is then identified from among the best rule sets of size K as the rule set that best balances rule set size and statistical penalty. Users should consult the ‘crso’ vignette for an example walk through of a full CRSO run. The full description, of the CRSO algorithm is presented in: Klein MI, Cannataro V, Townsend J, Stern DF and Zhao H. Identifying combinations of cancer driver in individual patients. BioRxiv 674234 [Preprint]. June 19, 2019. <doi:10.1101/674234>. Please cite this article if you use ‘crso’. / GPL-2 noarch
r-crtsize 1.0 Sample size estimation in cluster (group) randomized trials. Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012). / GPL-2 noarch
r-crwrm 0.0.1 To re-calculate the coefficients and the standard deviation when changing the reference group. / GPL-3 noarch
r-cseqpat 0.1.2 Mines contiguous sequential patterns in text. / MIT noarch
r-cshapes 0.6 Package for CShapes, a GIS dataset of country boundaries (1946-today). Includes functions for data extraction and the computation of distance matrices and -lists. / GPL-2 noarch
r-csn 1.1.3 Provides functions for computing the density and the log-likelihood function of closed-skew normal variates, and for generating random vectors sampled from this distribution. See Gonzalez-Farias, G., Dominguez-Molina, J., and Gupta, A. (2004). The closed skew normal distribution, Skew-elliptical distributions and their applications: a journey beyond normality, Chapman and Hall/CRC, Boca Raton, FL, pp. 25-42. / GPL-2 noarch
r-csp 0.1.0 Provides the Correlates of State Policy data set for easy use in R. / CC0 noarch
r-csrplus 1.03_0 Includes two functions to evaluate the hypothesis of complete spatial randomness (csr) in point processes. The function ‘mwin’ calculates quadrat counts to estimate the intensity of a spatial point process through the moving window approach proposed by Bailey and Gatrell (1995). Event counts are computed within a window of a set size over a fine lattice of points within the region of observation. The function ‘pielou’ uses the nearest neighbor test statistic and asymptotic distribution proposed by Pielou (1959) to compare the observed point process to one generated under csr. The value can be compared to that given by the more widely used test proposed by Clark and Evans (1954). / GPL-2 noarch
r-cssam 1.2.4 Cell-type specific differential expression of a microarray experiment of heterogeneous tissue samples, using SAM. / LGPL-3 noarch
r-cstar 1.0 Functions that allow a researcher to examine the robustness of the substantive significance of their findings. Implements ideas set out in Esarey and Danneman (2014). / GPL-2 noarch
r-csv 0.5.3 Reads and writes CSV with selected conventions. Uses the same generic function for reading and writing to promote consistent formats. / GPL-3 noarch
r-csvread 1.2.1 Functions for loading large (10M lines) CSV and other delimited files, similar to read.csv, but typically faster and using less memory than the standard R loader. While not entirely general, it covers many common use cases when the types of columns in the CSV file are known in advance. In addition, the package provides a class ‘int64’, which represents 64-bit integers exactly when reading from a file. The latter is useful when working with 64-bit integer identifiers exported from databases. The CSV file loader supports common column types including ‘integer’, ‘double’, ‘string’, and ‘int64’, leaving further type transformations to the user. / Apache License (== 2.0) linux-64, osx-64, win-64
r-csvy 0.3.0 Support for import from and export to the CSVY file format. CSVY is a file format that combines the simplicity of CSV (comma-separated values) with the metadata of other plain text and binary formats (JSON, XML, Stata, etc.) by placing a YAML header on top of a regular CSV. / GPL-2 noarch
r-cthresher 1.1.0 Estimation and inference methods for the continuous threshold expectile regression. It can fit the continuous threshold expectile regression and test the existence of change point, for the paper, Feipeng Zhang and Qunhua Li (2016). A continuous threshold expectile regression, submitted. / GPL (>= 2.0) noarch
r-ctrdata 0.18.2 Provides functions for querying, retrieving and analysing protocol- and results-related information on clinical trials from two public registers, the European Union Clinical Trials Register (EUCTR, <https://www.clinicaltrialsregister.eu/>) and ClinicalTrials.gov (CTGOV, <https://clinicaltrials.gov/>). The information is transformed and then stored in a database (mongo). Functions are provided for accessing and analysing the locally stored information on the clinical trials, as well as for identifying duplicate records. The package is motivated by the need for aggregating and trend-analysing the design, conduct and outcomes across clinical trials. / MIT noarch
r-ctrlgene 1.0.1 A simple way to assess the stability of candidate housekeeping genes is implemented in this package. / GPL-2 noarch
r-cts 1.0_22 Functions to fit continuous time autoregressive models with the Kalman filter (Wang (2013) <doi:10.18637/jss.v053.i05>). / GPL-2 linux-64, osx-64, win-64
r-ctt 2.3.3 A collection of common test and item analyses from a classical test theory (CTT) framework. Analyses can be applied to both dichotomous and polytomous data. Functions provide reliability analyses (alpha), item statistics, disctractor analyses, disattenuated correlations, scoring routines, and empirical ICCs. / GPL-2 noarch
r-cttinshiny 0.1.0 A Shiny interface developed in close coordination with the CTT package, providing a GUI that guides the user through CTT analyses. / GPL-2 noarch
r-ctv 0.8_5 Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated packages (client-side tools), generating HTML pages and storing task view information in the repository (server-side tools). / GPL-2 | GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-cub 1.1.3 For ordinal rating data, estimate and test models within the family of CUB models and their extensions (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions). Simulation routines, plotting facilities and fitting measures are also provided. / GPL-2 | GPL-3 noarch
r-cubature 2.0.3 R wrappers around the cubature C library of Steven G. Johnson for adaptive multivariate integration over hypercubes and the Cuba C library of Thomas Hahn for deterministic and Monte Carlo integration. Scalar and vector interfaces for cubature and Cuba routines are provided; the vector interfaces are highly recommended as demonstrated in the package vignette. / GPL-3 linux-64, osx-64, win-64
r-cubeview 0.1.0 Creates a 3D data cube view of a RasterStack/Brick, typically a collection/array of RasterLayers (along z-axis) with the same geographical extent (x and y dimensions) and resolution, provided by package ‘raster’. Slices through each dimension (x/y/z), freely adjustable in location, are mapped to the visible sides of the cube. The cube can be freely rotated. Zooming and panning can be used to focus on different areas of the cube. / MIT noarch
r-cubfits 0.1_3 Estimating mutation and selection coefficients on synonymous codon bias usage based on models of ribosome overhead cost (ROC). Multinomial logistic regression and Markov Chain Monte Carlo are used to estimate and predict protein production rates with/without the presence of expressions and measurement errors. Work flows with examples for simulation, estimation and prediction processes are also provided with parallelization speedup. The whole framework is tested with yeast genome and gene expression data of Yassour, et al. (2009) <doi:10.1073/pnas.0812841106>. / Mozilla Public License 2.0 linux-64, osx-64, win-64
r-cubist 0.2.2 Regression modeling using rules with added instance-based corrections. / GPL-3 linux-64, osx-64, win-64
r-cump 2.0 Combining Univariate Association Test Results of Multiple Phenotypes for Detecting Pleiotropy. / GPL-2 noarch
r-cumplyr 0.1_1 Extends ddply to allow calculation of cumulative quantities. / MIT noarch
r-cumseg 1.2 Estimation of number and location of change points in mean-shift (piecewise constant) models. Particularly useful to model genomic sequences of continuous measurements. / GPL-3 noarch
r-cumstats 1.0 Cumulative descriptive statistics for (arithmetic, geometric, harmonic) mean, median, mode, variance, skewness and kurtosis. / GPL-3 noarch
r-curl 3.3 The curl() and curl_download() functions provide highly configurable drop-in replacements for base url() and download.file() with better performance, support for encryption (https, ftps), gzip compression, authentication, and other ‘libcurl’ goodies. The core of the package implements a framework for performing fully customized requests where data can be processed either in memory, on disk, or streaming via the callback or connection interfaces. Some knowledge of ‘libcurl’ is recommended; for a more-user-friendly web client see the ‘httr’ package which builds on this package with http specific tools and logic. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
r-currentsurvival 1.0 The currentSurvival package contains functions for the estimation of the current cumulative incidence (CCI) and the current leukaemia-free survival (CLFS). The CCI is the probability that a patient is alive and in any disease remission (e.g. complete cytogenetic remission in chronic myeloid leukaemia) after initiating his or her therapy (e.g. tyrosine kinase therapy for chronic myeloid leukaemia). The CLFS is the probability that a patient is alive and in any disease remission after achieving the first disease remission. / GPL-2 noarch
r-curry 0.1.1 Partial application is the process of reducing the arity of a function by fixing one or more arguments, thus creating a new function lacking the fixed arguments. The curry package provides three different ways of performing partial function application by fixing arguments from either end of the argument list (currying and tail currying) or by fixing multiple named arguments (partial application). This package provides this functionality through the %<%, %-<%, and %><% operators which allows for a programming style comparable to modern functional languages. Compared to other implementations such a purrr::partial() the operators in curry composes functions with named arguments, aiding in autocomplete etc. / GPL-2 noarch
r-curstatci 0.1.1 Computes the maximum likelihood estimator, the smoothed maximum likelihood estimator and pointwise bootstrap confidence intervals for the distribution function under current status data. Groeneboom and Hendrickx (2017) <doi:10.1214/17-EJS1345>. / GPL-3 linux-64, osx-64, win-64
r-curvecomp 0.1.0 Performs multiple comparison procedures on curve observations among different treatment groups. The methods are applicable in a variety of situations (such as independent groups with equal or unequal sample sizes, or repeated measures) by using parametric bootstrap. References to these procedures can be found at Konietschke, Gel, and Brunner (2014) <doi:10.1090/conm/622/12431> and Westfall (2011) <doi:10.1080/10543406.2011.607751>. / GPL-2 noarch
r-cusp 2.3.3 Cobb’s maximum likelihood method for cusp-catastrophe modeling (Grasman, van der Maas, & Wagenmakers, 2009, JSS, 32:8; Cobb, L, 1981, Behavioral Science, 26:1, 75–78). Includes a cusp() function for model fitting, and several utility functions for plotting, and for comparing the model to linear regression and logistic curve models. / GPL-2 linux-64, osx-64, win-64
r-customerscoringmetrics 1.0.0 Functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978–0–387–72578–9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions. / GPL-2 noarch
r-cusum 0.4.0 Provides functions for constructing and evaluating CUSUM charts and RA-CUSUM charts with focus on false signal probability. / GPL-2 linux-64, osx-64, win-64
r-cusumdesign 1.1.3 Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts. / GPL-2 linux-64, osx-64, win-64
r-cutpointsoehr 0.1.2 Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shaped relationship with survival outcomes based on Cox regression model. The optimal equal-HR method estimates two optimal cut-points that have approximately the same log hazard value based on Cox regression model and divides individuals into different groups according to their HR values. / GPL-3 noarch
r-cuttlefish.model 1.0 This package can be used to standardize abundance indices using the delta-GLM method and to model the English Channel cuttlefish stock using a two-stage biomass model / GPL-3 noarch
r-cvauc 1.1.0 This package contains various tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators. The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively. One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods. The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package. / Apache License (== 2.0) noarch
r-cvcalibration 1.0_1 Statistical inferences for estimating the calibration equation with error-in observations / GPL-2 noarch
r-cvd 1.0.2 Methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation. / GPL-3 noarch
r-cvequality 0.2.0 Contains functions for testing for significant differences between multiple coefficients of variation. Includes Feltz and Miller’s (1996) <DOI:10.1002/(SICI)1097-0258(19960330)15:6%3C647::AID-SIM184%3E3.0.CO;2-P> asymptotic test and Krishnamoorthy and Lee’s (2014) <DOI:10.1007/s00180-013-0445-2> modified signed-likelihood ratio test. See the vignette for more, including full details of citations. / MIT noarch
r-cvgee 0.3_0 Calculates predictions from generalized estimating equations and internally cross-validates them using the logarithmic, quadratic and spherical proper scoring rules; Kung-Yee Liang and Scott L. Zeger (1986) <doi:10.1093/biomet/73.1.13>. / GPL-3 noarch
r-cvmgof 1.0.0 It is devoted to Cramer-von Mises goodness-of-fit tests. It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model. / CeCILL noarch
r-cvq2 1.2.0 The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient. This value can be calculated with an external data set or by cross validation. / GPL-3 noarch
r-cvst 0.2_2 The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran’s Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts. / GPL (>= 2.0) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-cvtools 0.3.2 Tools that allow developers to write functions for cross-validation with minimal programming effort and assist users with model selection. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-cvtuningcov 1.0 This is a package for selecting tuning parameters based on cross-validation (CV) in regularized estimators of large covariance matrices. Four regularized methods are implemented: banding, tapering, hard-thresholding and soft-thresholding. Two types of matrix norms are applied: Frobenius norm and operator norm. Two types of CV are considered: K-fold CV and random CV. Usually K-fold CV use K-1 folds to train a model and the rest one fold to validate the model. The reverse version trains a model with 1 fold and validates with the rest with K-1 folds. Random CV randomly splits the data set to two parts, a training set and a validation set with user-specified sizes. / GPL-2 noarch
r-cvxbiclustr 0.0.1 An iterative algorithm for solving a convex formulation of the biclustering problem. / MIT linux-64, osx-64, win-64
r-cvxclustr 1.1.1 Alternating Minimization Algorithm (AMA) and Alternating Direction Method of Multipliers (ADMM) splitting methods for convex clustering. / CC BY-NC-SA 4.0 linux-64, osx-64, win-64
r-cwhmisc 6.6 Miscellaneous useful or interesting functions. / GPL-2 noarch
r-cxhull 0.2.0 Computes the convex hull in arbitrary dimension, based on the Qhull library (<http://www.qhull.org>). The package provides a complete description of the convex hull: edges, ridges, facets, adjacencies. Triangulation is optional. / GPL-3 linux-64, osx-64, win-64
r-cxxfunplus 1.0 extend cxxfunction by saving the dynamic shared objects for reusing across R sessions / GPL-3 noarch
r-cyclertools 1.1.1 A suite of functions for analysing cycling data. / MIT linux-64, osx-64, win-64
r-cyclocomp 1.1.0 Cyclomatic complexity is a software metric (measurement), used to indicate the complexity of a program. It is a quantitative measure of the number of linearly independent paths through a program’s source code. It was developed by Thomas J. McCabe, Sr. in 1976. / MIT noarch
r-cycloids 1.0 Tools for calculating coordinate representations of hypocycloids, epicyloids, hypotrochoids, and epitrochoids (altogether called ‘cycloids’ here) with different scaling and positioning options. The cycloids can be visualised with any appropriate graphics function in R. / GPL-3 noarch
r-cytobankapi 1.3.0 Tools to interface with Cytobank’s API via R, organized by various endpoints that represent various areas of Cytobank functionality. Learn more about Cytobank at <https://www.cytobank.org>. / Artistic-2.0 noarch
r-cytobankbridger 1.0.0 A collection of tools that leverage the CytobankAPI R package <https://cran.r-project.org/web/packages/CytobankAPI/vignettes/cytobank-quickstart.html> to complete more complex workflows, and add/extend various Cytobank features. / Artistic-2.0 noarch
r-d3network 0.5.2.1 This packages is intended to make it easy to create D3 JavaScript network, tree, dendrogram, and Sankey graphs from R using data frames. !!! NOTE: Active development has moved to the networkD3 package. !!! / GPL-3 noarch
r-d3plus 0.1.0 Provides functions that offer seamless ‘D3Plus’ integration. The examples provided here are taken from the official ‘D3Plus’ website <http://d3plus.org>. / MIT noarch
r-daag 1.22.1 Various data sets used in examples and exercises in the book Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) Data Analysis and Graphics Using R. / GPL-3 noarch
r-daagxtras 0.8_4 various data sets used in additional exercises for the book Maindonald, J.H. and Braun, W.J. (3rd edn 2010) Data Analysis and Graphics Using R, and for a ‘Data Mining’ course. Note that a number of datasets that were in earlier versions of this package have been transferred to the DAAG package. / Unlimited noarch
r-daarem 0.3 Implements the DAAREM method for accelerating the convergence of slow, monotone sequences from smooth, fixed-point iterations such as the EM algorithm. For further details about the DAAREM method. see Henderson, N.C. and Varadhan, R. (2018) <arXiv:1803.06673>. / GPL-2 noarch
r-dacf 1.0.0 An implementation of data analytic methods in R for analyses for data with ceiling/floor effects. The package currently includes functions for mean/variance estimation and mean comparison tests. Implemented methods are from Aitkin (1964) <doi:10.1007/BF02289723> and Liu & Wang (in prep). / GPL-2 noarch
r-dagr 1.1.3 Functions to draw, manipulate, evaluate directed acyclic graphs and simulate corresponding data. / GPL-2 noarch
r-daime 1.1 Reverse and model the effects of changing deposition rates on geological data and rates. Based on Hohmann (2018) <doi:10.13140/RG.2.2.23372.51841> . / CC BY 4.0 noarch
r-dalex2 0.9 Machine Learning models are widely used and have various applications in classification or regression tasks. Due to increasing computational power, availability of new data sources and new methods, ML models are more and more complex. Models created with techniques like boosting, bagging of neural networks are true black boxes. It is hard to trace the link between input variables and model outcomes. They are used because of high performance, but lack of interpretability is one of their weakest sides. In many applications we need to know, understand or prove how input variables are used in the model and what impact do they have on final model prediction. DALEX2 is a collection of tools that help to understand how complex predictive models are working. DALEX2 is a part of DrWhy universe for tools for Explanation, Exploration and Visualisation for Predictive Models. / GPL-3 noarch
r-daly 1.5.0 The DALY Calculator is a free, open-source Graphical User Interface (GUI) for stochastic disability-adjusted life year (DALY) calculation. / GPL-2 linux-64, osx-64, win-64
r-dam 0.0.1 A collection of functions which aim to assist common computational workflow for analysis of matabolomic data.. / CC0 noarch
r-dams 0.2 The single largest source of dams in the United States is the National Inventory of Dams (NID) <http://nid.usace.army.mil> from the US Army Corps of Engineers. Entire data from the NID cannot be obtained all at once and NID’s website limits extraction of more than a couple of thousand records at a time. Moreover, selected data from the NID’s user interface cannot not be saved to a file. In order to make the analysis of this data easier, all the data from NID was extracted manually. Subsequently, the raw data was checked for potential errors and cleaned. This package provides sample cleaned data from the NID and provides functionality to access the entire cleaned NID data. / GPL-2 noarch
r-dang 0.0.10 A collection of utility functions. / GPL-2 noarch
r-dap 1.0 An implementation of Discriminant Analysis via Projections (DAP) method for high-dimensional binary classification in the case of unequal covariance matrices. See Irina Gaynanova and Tianying Wang (2018) <arXiv:1711.04817v2>. / GPL-2 linux-64, osx-64, win-64
r-dapr 0.0.3 An easy-to-use, dependency-free set of functions for iterating over elements of various input objects. Functions are wrappers around base apply()/lapply()/vapply() functions but designed to have similar functionality to the mapping functions in the ‘purrr’ package <https://purrr.tidyverse.org/>. Specifically, function names more explicitly communicate the expected class of the output and functions also allow for the convenient shortcut of ‘~ .x’ instead of the more verbose ‘function(.x) .x’. / GPL-3 noarch
r-dark 0.9.8 The recovery of visual sensitivity in a dark environment is known as dark adaptation. In a clinical or research setting the recovery is typically measured after a dazzling flash of light and can be described by the Mahroo, Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take dark adaptation data and use nonlinear regression to find the parameters of the model that ‘best’ describe the data. They do this by firstly, generating rapid initial objective estimates of data adaptation parameters, then a multi-start algorithm is used to reduce the possibility of a local minimum. There is also a bootstrap method to calculate parameter confidence intervals. The functions rely upon a ‘dark’ list or object. This object is created as the first step in the workflow and parts of the object are updated as it is processed. / GPL-3 noarch
r-darts 1.0 Are you aiming at the right spot in darts? Maybe not! Use this package to find your optimal aiming location. For a better explanation, go to http://www-stat.stanford.edu/~ryantibs/darts/ or see the paper A Statistician Plays Darts. / GPL-3 linux-64, osx-64, win-64
r-dashboard 0.1.0 The dashboard package allows users to create web pages which display interactive data visualizations working in a standard modern browser. It displays them locally using the Rook server. Nor knowledge about web technologies nor Internet connection are required. D3.js is a JavaScript library for manipulating documents based on data. D3 helps the dashboard package bring data to life using HTML, SVG and CSS. / GPL-2 noarch
r-dasst 0.3.3 Provides methods for reading, displaying, processing and writing files originally arranged for the ‘DSSAT-CSM’ fixed width format. The ‘DSSAT-CSM’ cropping system model is described at J.W. Jones, G. Hoogenboomb, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, J.T. Ritchie (2003) <doi:10.1016/S1161-0301(02)00107-7>. / GPL-2 noarch
r-data.table 1.12.2 Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, a fast friendly file reader and parallel file writer. Offers a natural and flexible syntax, for faster development. / MPL-2.0 | file LICENSE linux-32, linux-64, osx-64, win-32, win-64
r-databaseconnectorjars 1.1.0 Provides external JAR dependencies for the ‘DatabaseConnector’ package. / Apache License noarch
r-dataclean 1.0 Includes functions that researchers or practitioners may use to clean raw data, transferring html, xlsx, txt data file into other formats. And it also can be used to manipulate text variables, extract numeric variables from text variables and other variable cleaning processes. It is originated from a author’s project which focuses on creative performance in online education environment. The resulting paper of that study will be published soon. / GPL-3 noarch
r-dataframes2xls 0.4.7 Writes data frames to xls files. It supports multiple sheets and basic formatting. / BSD_3_clause noarch
r-datagraph 1.0.1 Functions to save either ‘.dtable’ or ‘.dtbin’ files that can be read by DataGraph, a graphing and analysis application for macOS. Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see <https://www.visualdatatools.com/DataGraph/Help/DataFiles/R/index.html>. / GPL-2 linux-64, osx-64, win-64
r-datamap 0.1_1 datamap utilizes variable bindings and objects of class UserDefinedDatabase to provide a simple mapping system to foreign objects. Maps can be used as environments or attached to the search path, and changes to either are persistent. Mapped foreign objects are fetched in real-time and are never cached by the mapping system. / GPL-2 linux-64, osx-64, win-64
r-datamaps 0.0.3 Easily create interactive choropleth maps then add bubbles and arcs by coordinates or region name. These maps can be used directly from the console, from ‘RStudio’, in ‘Shiny’ apps and ‘R Markdown’ documents. ‘Shiny’ proxies allow to interactively add arcs and bubbles, change choropleth values, or change labels. / MIT noarch
r-dataqualityr 1.0 The package performs variable level data quality checks including missing values, unique values, frequency tables, and generates summary statistics / MIT noarch
r-datarium 0.1.0 Contains data organized by topics: categorical data, regression model, means comparisons, independent and repeated measures ANOVA, mixed ANOVA and ANCOVA. / GPL-2 noarch
r-datarobot 2.14.2 For working with the ‘DataRobot’ predictive modeling platform’s API <https://www.datarobot.com/>. / MIT noarch
r-datasaurus 0.1.4 The Datasaurus Dozen is a set of datasets with the same summary statistics. They retain the same summary statistics despite having radically different distributions. The datasets represent a larger and quirkier object lesson that is typically taught via Anscombe’s Quartet (available in the ‘datasets’ package). Anscombe’s Quartet contains four very different distributions with the same summary statistics and as such highlights the value of visualisation in understanding data, over and above summary statistics. As well as being an engaging variant on the Quartet, the data is generated in a novel way. The simulated annealing process used to derive datasets from the original Datasaurus is detailed in Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing <doi:10.1145/3025453.3025912>. / MIT noarch
r-dataseries 0.2.0 Download and import time series from <http://www.dataseries.org>, a comprehensive and up-to-date collection of open data from Switzerland. / GPL-3 noarch
r-datassim 1.0 For estimation of a variable of interest using Kalman filter by incorporating results from previous assessments, i.e. through development weighted estimates where weights are assigned inversely proportional to the variance of existing and new estimates. For reference see Ehlers et al. (2017) <doi:10.20944/preprints201710.0098.v1>. / GPL-2 linux-64, osx-64, win-64
r-datastructures 0.2.8 Implementation of advanced data structures such as hashmaps, heaps, or queues. Advanced data structures are essential in many computer science and statistics problems, for example graph algorithms or string analysis. The package uses ‘Boost’ and ‘STL’ data types and extends these to R with ‘Rcpp’ modules. / GPL-3 linux-64, osx-64, win-64
r-datautils 0.1.5 Contains facilities such as getting the current timestamp in decimal seconds, computing interval w.r.t. a reference timestamp, and custom plotting with error bars. / LGPL-3 linux-64, osx-64, win-64
r-dataverse 0.2.0 Provides access to Dataverse version 4 APIs <https://dataverse.org/>, enabling data search, retrieval, and deposit. For Dataverse versions <= 4.0, use the deprecated ‘dvn’ package <https://cran.r-project.org/package=dvn>. / GPL-2 noarch
r-date 1.2_38 Functions for handling dates. / GPL-2 linux-64, osx-64, win-64
r-datetime 0.1.4 Provides methods for working with nominal dates, times, and durations. Base R has sophisticated facilities for handling time, but these can give unexpected results if, for example, timezone is not handled properly. This package provides a more casual approach to support cases which do not require rigorous treatment. It systematically deconstructs the concepts origin and timezone, and de-emphasizes the display of seconds. It also converts among nominal durations such as seconds, hours, days, and weeks. See ‘?datetime’ and ‘?duration’ for examples. Adapted from ‘metrumrg’ <http://r-forge.r-project.org/R/?group_id=1215>. / GPL-3 noarch
r-datetimeutils 0.3_0 Utilities for handling dates and times, such as selecting particular days of the week or month, formatting timestamps as required by RSS feeds, or converting timestamp representations of other software (such as ‘MATLAB’ and ‘Excel’) to R. The package is lightweight (no dependencies, pure R implementations) and relies only on R’s standard classes to represent dates and times (‘Date’ and ‘POSIXt’); it aims to provide efficient implementations, through vectorisation and the use of R’s native numeric representations of timestamps where possible. / GPL-3 noarch
r-datr 0.1.0 Interface with the ‘Dat’ p2p network protocol <https://datproject.org>. Clone archives from the network, share your own files, and install packages from the network. / GPL-3 noarch
r-davies 1.1_9 Various utilities for the Davies distribution. / GPL-2 noarch
r-dbemplikegof 1.2.4 Goodness-of-fit and two sample comparison tests using sample entropy / GPL-2 noarch
r-dbemplikenorm 1.0.0 Test for joint assessment of normality / GPL-2 noarch
r-dbest 1.8 A program for analyzing vegetation time series, with two algorithms: 1) change detection algorithm that detects trend changes, determines their type (abrupt or non-abrupt), and estimates their timing, magnitude, number, and direction; 2) generalization algorithm that simplifies the temporal trend into main features. The user can set the number of major breakpoints or magnitude of greatest changes of interest for detection, and can control the generalization process by setting an additional parameter of generalization-percentage. / GPL-2 noarch
r-dbhydror 0.2_7 Client for programmatic access to the South Florida Water Management District’s ‘DBHYDRO’ database at <https://www.sfwmd.gov/science-data/dbhydro>, with functions for accessing hydrologic and water quality data. / GPL-3 noarch
r-dbi 1.0.0 A database interface definition for communication between R and relational database management systems. All classes in this package are virtual and need to be extended by the various R/DBMS implementations. / LGPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-dblcens 1.1.7 Use EM algorithm to compute the NPMLE of CDF and also the two censoring distributions. For doubly censored data (as described in Chang and Yang (1987) Ann. Stat. 1536-47). You can also specify a constraint, it will return the constrained NPMLE and the -2 log empirical likelihood ratio. This can be used to test the hypothesis about the constraint and find confidence intervals for probability or quantile via empirical likelihood ratio theorem. Influence function of hat F may also be calculated (but may be slow). / GPL-2 linux-64, osx-64, win-64
r-dbplyr 1.4.0 A ‘dplyr’ back end for databases that allows you to work with remote database tables as if they are in-memory data frames. Basic features works with any database that has a ‘DBI’ back end; more advanced features require ‘SQL’ translation to be provided by the package author. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-dbscan 1.1_4 A fast reimplementation of several density-based algorithms of the DBSCAN family for spatial data. Includes the DBSCAN (density-based spatial clustering of applications with noise) and OPTICS (ordering points to identify the clustering structure) clustering algorithms HDBSCAN (hierarchical DBSCAN) and the LOF (local outlier factor) algorithm. The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided. / GPL-2 linux-64, osx-64, win-64
r-dbstats 1.0.5 Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors. / GPL-2 noarch
r-dbx 0.2.5 Provides select, insert, update, upsert, and delete database operations. Supports ‘PostgreSQL’, ‘MySQL’, ‘SQLite’, and more, and plays nicely with the ‘DBI’ package. / MIT noarch
r-dcg 0.9.3 Data cloud geometry (DCG) applies random walks in finding community structures for social networks. Fushing, VanderWaal, McCowan, & Koehl (2013) (<doi:10.1371/journal.pone.0056259>). / GPL-2 noarch
r-dchipio 0.1.5 Functions for reading DCP and CDF.bin files generated by the dChip software. / LGPL-2.1 noarch
r-dcl 0.1.0 Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012). / GPL-2 noarch
r-dcovts 1.1 Computing and plotting the distance covariance and correlation function of a univariate or a multivariate time series. Both versions of biased and unbiased estimators of distance covariance and correlation are provided. Test statistics for testing pairwise independence are also implemented. Some data sets are also included. / GPL-2 noarch
r-dcurver 0.9.1 A Davidian curve defines a seminonparametric density, whose shape and flexibility can be tuned by easy to estimate parameters. Since a special case of a Davidian curve is the standard normal density, Davidian curves can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001) <doi:10.1111/j.0006-341X.2001.00795.x>. This package provides the density function, the gradient of the loglikelihood and a random generator for Davidian curves. / GPL-3 linux-64, osx-64, win-64
r-dcv 0.1.1 This package performs several conventional Cross-validation statistical methods for climate-growth model in the climate reconstruction from tree rings, including Sign Test statistic, Reduction of Error statistic, Product Mean Test, Durbin-Watson statistic etc. This package is at its primary stage, the functions have not been tested exhaustively and more functions would be added in the comming days. / GPL-2 noarch
r-ddalpha 1.3.9 Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included. / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
r-ddm 1.0_0 A set of three two-census methods to the estimate the degree of death registration coverage for a population. Implemented methods include the Generalized Growth Balance method (GGB), the Synthetic Extinct Generation method (SEG), and a hybrid of the two, GGB-SEG. Each method offers automatic estimation, but users may also specify exact parameters or use a graphical interface to guess parameters in the traditional way if desired. / GPL-2 noarch
r-ddrtree 0.1.5 Provides an implementation of the framework of reversed graph embedding (RGE) which projects data into a reduced dimensional space while constructs a principal tree which passes through the middle of the data simultaneously. DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of the single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as bifurcation structure of any datatype. / Artistic-2.0 linux-64, osx-64, win-64
r-deadband 0.1.0 Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,<doi:10.3390/s6010049>). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms. / GPL-2 noarch
r-deal 1.2_39 Bayesian networks with continuous and/or discrete variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>. / GPL-2 linux-64, osx-64, win-64
r-deamer 1.0 deamer provides deconvolution algorithms for the non-parametric estimation of the density f of an error-prone variable x with additive noise e. The model is y = x e where the noisy variable y is observed, while x is unobserved. Estimation may be performed for i) a known density of the error ii) with an auxiliary sample of pure noise and iii) with an auxiliary sample of replicate (repeated) measurements. Estimation is performed using adaptive model selection and penalized contrasts. / GPL-3 noarch
r-debugme 1.1.0 Specify debug messages as special string constants, and control debugging of packages via environment variables. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-debugr 0.0.1 Tool to print out the value of R objects/expressions while running an R script. Outputs can be made dependent on user-defined conditions/criteria. Debug messages only appear when a global option for debugging is set. This way, ‘debugr’ code can even remain in the debugged code for later use without any negative effects during normal runtime. / GPL-3 noarch
r-decide 1.2 Calculates various estimates for measures of educational differentials, the relative importance of primary and secondary effects in the creation of such differentials and compares the estimates obtained from two datasets. / GPL-2 noarch
r-decido 0.2.0 Provides constrained triangulation of polygons. Ear cutting (or ear clipping) applies constrained triangulation by successively ‘cutting’ triangles from a polygon defined by path/s. Holes are supported by introducing a bridge segment between polygon paths. This package wraps the ‘header-only’ library ‘earcut.hpp’ <https://github.com/mapbox/earcut.hpp.git> which includes a reference to the method used by Held, M. (2001) <doi:10.1007/s00453-001-0028-4>. / MIT linux-64, osx-64, win-64
r-decision 0.1.0 Contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making. / GPL-2 noarch
r-decode 1.2 Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm. / GPL-3 noarch
r-decoder 1.1.13 Main function decode is used to decode coded key values to plain text. Function code can be used to code plain text to code if there is a 1:1 relation between the two. The concept relies on ‘keyvalue’ objects used for translation. There are several ‘keyvalue’ objects included in the areas of geographical regional codes, administrative health care unit codes, diagnosis codes and more. It is also easy to extend the use by arbitrary code sets. / GPL-2 noarch
r-decompr 4.5.0 Two global-value-chain decompositions are implemented. Firstly, the Wang-Wei-Zhu (Wang, Wei, and Zhu, 2013) algorithm splits bilateral gross exports into 16 value-added components. Secondly, the Leontief decomposition (default) derives the value added origin of exports by country and industry, which is also based on Wang, Wei, and Zhu (Wang, Z., S.-J. Wei, and K. Zhu. 2013. Quantifying International Production Sharing at the Bilateral and Sector Levels.). / GPL-3 noarch
r-decon 1.2_4 This package contains a collection of functions to deal with nonparametric measurement error problems using deconvolution kernel methods. We focus two measurement error models in the package: (1) an additive measurement error model, where the goal is to estimate the density or distribution function from contaminated data; (2) nonparametric regression model with errors-in-variables. The R functions allow the measurement errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the Fast Fourier Transform (FFT) algorithm for density estimation with error-free data to the deconvolution kernel estimation. Several methods for the selection of the data-driven smoothing parameter are also provided in the package. See details in: Wang, X.F. and Wang, B. (2011). Deconvolution estimation in measurement error models: The R package decon. Journal of Statistical Software, 39(10), 1-24. / GPL-3 linux-64, osx-64, win-64
r-deconvolver 1.1 Empirical Bayes methods for learning prior distributions from data. An unknown prior distribution (g) has yielded (unobservable) parameters, each of which produces a data point from a parametric exponential family (f). The goal is to estimate the unknown prior (g-modeling) by deconvolution and Empirical Bayes methods. / GPL-2 noarch
r-deepboost 0.1.6 Provides deep boosting models training, evaluation, predicting and hyper parameter optimising using grid search and cross validation. Based on Google’s Deep Boosting algorithm, and Google’s C implementation. Cortes, C., Mohri, M., & Syed, U. (2014) <http://machinelearning.wustl.edu/mlpapers/papers/icml2014c2_cortesb14>. / Apache License (== 2.0) linux-64, osx-64, win-64
r-deepgmm 0.1.56 Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) <doi:10.1007/s11222-017-9793-z> provide a generalization of classical Gaussian mixtures to multiple layers. Each layer contains a set of latent variables that follow a mixture of Gaussian distributions. To avoid overparameterized solutions, dimension reduction is applied at each layer by way of factor models. / GPL-3 noarch
r-deepnet 0.2 Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on. / GPL-3 noarch
r-deepnn 0.3 Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning. / GPL-3 noarch
r-deepr 0.1 Tests for, and describe differences in event count profiles in groups of reconstructed cophylogenies / GPL-2 noarch
r-default 1.0.0 A simple syntax to change the default values for function arguments, whether they are in packages or defined locally. / MIT noarch
r-deformula 0.1.1 Numerical quadrature of functions of one variable over a finite or infinite interval with double exponential formulas. / GPL-2 linux-64, osx-64, win-64
r-deisotoper 0.0.7 Provides a low-level interface for a deisotoper container implemented in the ‘Java’ programming language and means of S3 helper functions for plotting and debugging isotopes of mass spectrometric data. The deisotoper algorithm detects and aggregates peaks which belong to the same isotopic cluster of a given mass spectrum. / GPL-3 linux-64
r-delaporte 7.0.2 Provides probability mass, distribution, quantile, random-variate generation, and method-of-moments parameter-estimation functions for the Delaporte distribution. The Delaporte is a discrete probability distribution which can be considered the convolution of a negative binomial distribution with a Poisson distribution. Alternatively, it can be considered a counting distribution with both Poisson and negative binomial components. It has been studied in actuarial science as a frequency distribution which has more variability than the Poisson, but less than the negative binomial. / BSD_2_clause linux-64, osx-64, win-64
r-deldir 0.1_23 Calculates the Delaunay triangulation and the Dirichlet or Voronoi tessellation (with respect to the entire plane) of a planar point set. Plots triangulations and tessellations in various ways. Clips tessellations to sub-windows. Calculates perimeters of tessellations. Summarises information about the tiles of the tessellation. / GPL-2 linux-64, osx-64, win-64
r-delt 0.8.2 We implement methods for estimating multivariate densities. We include a discretized kernel estimator, an adaptive histogram (a greedy histogram and a CART-histogram), stagewise minimization, and bootstrap aggregation. / GPL-2 linux-64, osx-64, win-64
r-delta 0.2.0.2 Measure of agreement delta was originally by Martín & Femia (2004) <DOI:10.1348/000711004849268>. Since then has been considered as agreement measure for different fields, since their behavior is usually better than the usual kappa index by Cohen (1960) <DOI:10.1177/001316446002000104>. The main issue with delta is that can not be computed by hand contrary to kappa. The current algorithm is based on the Version 5 of the delta windows program that can be found on <https://www.ugr.es/~bioest/software/delta/cmd.php?seccion=downloads>. / GPL-3 noarch
r-deltaplotr 1.6 The deltaPlotR package implements Angoff’s Delta Plot method to detect dichotomous DIF. Several detection thresholds are included, either from multivariate normality assumption or by prior determination. Item purification is supported (Magis and Facon (2014) <doi:10.18637/jss.v059.c01>). / GPL-2 noarch
r-deltd 0.1.0 It plots densities by using asymmetrical kernels which belong to life time distributions and calculate its related MSE. For details see Chen (2000), Jin and Kawczak (2003) and Salha et al. (2014) <doi:10.12988/pms.2014.4616>. / GPL-2 noarch
r-demetics 0.8_7 This package allows to calculate the fixation index Gst (Nei, 1973) and the differentiation index D (Jost, 2008) pairwise between or averaged over several populations. P-values, stating the significance of differentiation, and 95 percent confidence intervals can be estimated using bootstrap resamplings. In the case that more than two populations are compared pairwise, the p-values are adjusted by bonferroni correction and in several other ways due to the multiple comparison from one data set. / GPL-2 noarch
r-deming 1.4 Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions. / LGPL-2 noarch
r-demogr 0.6.0 Construction and analysis of matrix population models in R. / GPL-2 noarch
r-demokde 0.9_4 Demonstration code showing how (univariate) kernel density estimates are computed, at least conceptually, and allowing users to experiment with different kernels, should they so wish. NOTE: the density function in the stats package should be used for computational efficiency. / GPL-2 noarch
r-demova 1.0 Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets. / GPL-2 noarch
r-dendroextras 0.2.3 Provides extra functions to manipulate dendrograms that build on the base functions provided by the ‘stats’ package. The main functionality it is designed to add is the ability to colour all the edges in an object of class ‘dendrogram’ according to cluster membership i.e. each subtree is coloured, not just the terminal leaves. In addition it provides some utility functions to cut ‘dendrogram’ and ‘hclust’ objects and to set/get labels. / GPL-2 noarch
r-dendsort 0.3.3 An implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization. / GPL-2 | GPL-3 noarch
r-denoiseq 0.1.1 Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of a bottom-up model for PCR amplification. This model is developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution. / GPL-2 noarch
r-denpro 0.9.2 We provide tools to (1) visualize multivariate density functions and density estimates with level set trees, (2) visualize level sets with shape trees, (3) visualize multivariate data with tail trees, (4) visualize scales of multivariate density estimates with mode graphs and branching maps, and (5) visualize anisotropic spread with 2D volume functions and 2D probability content functions. Level set trees visualize mode structure, shape trees visualize shapes of level sets of unimodal densities, and tail trees visualize connected data sets. The kernel estimator is implemented but the package may also be applied for visualizing other density estimates. / GPL-2 linux-64, osx-64, win-64
r-denseflmm 0.1.2 Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis. / GPL-2 noarch
r-densparcorr 1.1 Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation. / GPL-2 noarch
r-densratio 0.2.1 Density ratio estimation. The estimated density ratio function can be used in many applications such as anomaly detection, change-point detection, covariate shift adaptation. The implemented methods are uLSIF (Hido et al. (2011) <doi:10.1007/s10115-010-0283-2>), RuLSIF (Yamada et al. (2011) <doi:10.1162/NECO_a_00442>), and KLIEP (Sugiyama et al. (2007) <doi:10.1007/s10463-008-0197-x>). / MIT noarch
r-denstrip 1.5.4 Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips. / GPL-2 noarch
r-denvax 0.1.1 Provides the mathematical model described by Serostatus Testing & Dengue Vaccine Cost-Benefit Thresholds in <doi:10.1098/rsif.2019.0234>. Using the functions in the package, that analysis can be repeated using sample life histories, either synthesized from local seroprevalence data using other functions in this package (as in the manuscript) or from some other source. The package provides a vignette which walks through the analysis in the publication, as well as a function to generate a project skeleton for such an analysis. / MIT noarch
r-deoptim 2.2_4 Implements the differential evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector. / GPL-2 linux-64, osx-64, win-64
r-deoptimr 1.0_8 Differential Evolution (DE) stochastic algorithms for global optimization of problems with and without constraints. The aim is to curate a collection of its state-of-the-art variants that (1) do not sacrifice simplicity of design, (2) are essentially tuning-free, and (3) can be efficiently implemented directly in the R language. Currently, it only provides an implementation of the ‘jDE’ algorithm by Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-depend.truncation 3.0 Estimation and testing methods for dependently truncated data. Semi-parametric methods are based on Emura et al. (2011)<Stat Sinica 21:349-67>, Emura & Wang (2012)<doi:10.1016/j.jmva.2012.03.012>, and Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>. Parametric approaches are based on Emura & Konno (2012)<doi:10.1007/s00362-014-0626-2> and Emura & Pan (2017)<doi:10.1007/s00362-017-0947-z>. A regression approach is based on Emura & Wang (2016)<doi:10.1007/s10463-015-0526-9>. Quasi-independence tests are based on Emura & Wang (2010)<doi:10.1016/j.jmva.2009.07.006>. Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>. / GPL-2 noarch
r-deplogo 1.0 Plots dependency logos from a set of input sequences. / GPL-3 noarch
r-deployrrserve 9.0.0 Rserve acts as a socket server (TCP/IP or local sockets) which allows binary requests to be sent to R. Every connection has a separate workspace and working directory. Client-side implementations are available for popular languages such as C/C and Java, allowing any application to use facilities of R without the need of linking to R code. Rserve supports remote connection, user authentication and file transfer. A simple R client is included in this package as well. / GPL version 2 linux-64, win-64
r-depmix 0.9.15 Fits (multigroup) mixtures of latent or hidden Markov models on mixed categorical and continuous (timeseries) data. The Rdonlp2 package can optionally be used for optimization of the log-likelihood and is available from R-forge. / GPL-3 linux-64, osx-64, win-64
r-depth.plot 0.1 Could be used to obtain spatial depths, spatial ranks and outliers of multivariate random variables. Could also be used to visualize DD-plots (a multivariate generalization of QQ-plots). / GPL-2 noarch
r-depthtools 0.4 depthTools is a package that implements different statistical tools for the description and analysis of gene expression data based on the concept of data depth, namely, the scale curves for visualizing the dispersion of one or various groups of samples (e.g. types of tumors), a rank test to decide whether two groups of samples come from a single distribution and two methods of supervised classification techniques, the DS and TAD methods. All these techniques are based on the Modified Band Depth, which is a recent notion of depth with a low computational cost, what renders it very appropriate for high dimensional data such as gene expression data. / GPL-2 noarch
r-dequer 2.0_1 Queues, stacks, and ‘deques’ are list-like, abstract data types. These are meant to be very cheap to grow, or insert new objects into. A typical use case involves storing data in a list in a streaming fashion, when you do not necessarily know how may elements need to be stored. Unlike R’s lists, the new data structures provided here are not necessarily stored contiguously, making insertions and deletions at the front/end of the structure much faster. The underlying implementation is new and uses a head/tail doubly linked list; thus, we do not rely on R’s environments or hashing. To avoid unnecessary data copying, most operations on these data structures are performed via side-effects. / BSD_2_clause linux-64, osx-64, win-64
r-derezende.ferreira 0.1.0 Modeling the zero coupon yield curve using the dynamic De Rezende and Ferreira (2011) <doi:10.1002/for.1256> five factor model with variable or fixed decaying parameters. For explanatory purposes, the package also includes various short datasets of interest rates for the BRICS countries. / GPL-2 noarch
r-deriv 3.8.5 R-based solution for symbolic differentiation. It admits user-defined function as well as function substitution in arguments of functions to be differentiated. Some symbolic simplification is part of the work. / GPL-3 noarch
r-derivmkts 0.2.4 A set of pricing and expository functions that should be useful in teaching a course on financial derivatives. / MIT noarch
r-des 1.0.0 Discrete event simulation (DES) involves modeling of systems having discrete, i.e. abrupt, state changes. For instance, when a job arrives to a queue, the queue length abruptly increases by 1. This package is an R implementation of the event-oriented approach to DES; see the tutorial in Matloff (2008) <http://heather.cs.ucdavis.edu/~matloff/156/PLN/DESimIntro.pdf>. / MIT noarch
r-desc 1.2.0 Tools to read, write, create, and manipulate DESCRIPTION files. It is intended for packages that create or manipulate other packages. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-descomponer 1.5 Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain. / GPL-2 noarch
r-descr 1.1.4 Weighted frequency and contingency tables of categorical variables and of the comparison of the mean value of a numerical variable by the levels of a factor, and methods to produce xtable objects of the tables and to plot them. There are also functions to facilitate the character encoding conversion of objects, to quickly convert fixed width files into csv ones, and to export a data.frame to a text file with the necessary R and SPSS codes to reread the data. / GPL-2 linux-64, osx-64, win-64
r-describer 0.2.0 Allows users to quickly and easily describe data using common descriptive statistics. / MIT noarch
r-designgg 1.1 The package provides R scripts for designing genetical genomics experiments. / GPL-3 noarch
r-designglmm 0.1.0 Use simulated annealing to find optimal designs for Poisson regression models with blocks. / GPL-3 noarch
r-desir 1.2.1 Functions for (1) ranking, selecting, and prioritising genes, proteins, and metabolites from high dimensional biology experiments, (2) multivariate hit calling in high content screens, and (3) combining data from diverse sources. / GPL-3 noarch
r-desirability 2.1 S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980). / GPL-2 noarch
r-desnowball 1.0 This package implements a statistical data mining method to compare whole genome gene expression profiles, with respect to the presence of a recurrent genetic disturbance event, to identify the affected target genes. / GPL-3 noarch
r-desolve 1.24 Functions that solve initial value problems of a system of first-order ordinary differential equations (‘ODE’), of partial differential equations (‘PDE’), of differential algebraic equations (‘DAE’), and of delay differential equations. The functions provide an interface to the FORTRAN functions ‘lsoda’, ‘lsodar’, ‘lsode’, ‘lsodes’ of the ‘ODEPACK’ collection, to the FORTRAN functions ‘dvode’, ‘zvode’ and ‘daspk’ and a C-implementation of solvers of the ‘Runge-Kutta’ family with fixed or variable time steps. The package contains routines designed for solving ‘ODEs’ resulting from 1-D, 2-D and 3-D partial differential equations (‘PDE’) that have been converted to ‘ODEs’ by numerical differencing. / GPL-2 linux-64, osx-64, win-64
r-det 2.0.1 Builds both ROC (Receiver Operating Characteristic) and DET (Detection Error Tradeoff) curves from a set of predictors, which are the results of a binary classification system. The curves give a general vision of the performance of the classifier, and are useful for comparing performance of different systems. / GPL-2 noarch
r-detector 0.1.0 Allows users to quickly and easily detect data containing Personally Identifiable Information (PII) through convenience functions. / MIT noarch
r-detestset 1.1.6 Solvers and test set for stiff and non-stiff differential equations, and differential algebraic equations. ‘Mazzia, F., Cash, J.R. and K. Soetaert, 2012. DOI: 10.1016/j.cam.2012.03.014’. / GPL-3 linux-64, osx-64, win-64
r-detmcd 0.0.5 Implementation of DetMCD, a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, Rousseeuw and Verdonck (2012) <doi:10.1080/10618600.2012.672100>. / GPL-2 linux-64, osx-64, win-64
r-detpack 1.1.3 Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function ‘det.construct’ translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function ‘det.query’ is available. The functions ‘det1’ and ‘det2’ provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by ‘det.rnd’. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632> or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) <doi:10.1080/10618600.2018.1482768>. / GPL-2 noarch
r-detr 0.0.5 DetLTS, DetMM (and DetS) Algorithms for Deterministic, Robust Linear Regression. / GPL-2 linux-64, osx-64, win-64
r-detsel 1.0.3 In the new era of population genomics, surveys of genetic polymorphism (genome scans) offer the opportunity to distinguish locus-specific from genome wide effects at many loci. Identifying presumably neutral regions of the genome that are assumed to be influenced by genome-wide effects only, and excluding presumably selected regions, is therefore critical to infer population demography and phylogenetic history reliably. Conversely, detecting locus-specific effects may help identify those genes that have been, or still are, targeted by natural selection. The software package DetSel has been developed to identify markers that show deviation from neutral expectation in pairwise comparisons of diverging populations. / GPL-2 linux-64, osx-64, win-64
r-devemf 3.6_3 Output graphics to EMF/EMF. / GPL-3 linux-64, osx-64, win-64
r-devfunc 0.1 A concise check of the format of one or multiple input arguments (data type, length or value) is provided. Since multiple input arguments can be tested simultaneously, a lengthly list of checks at the beginning of your function can be avoided, hereby enhancing the readability and maintainability of your code. / GPL-3 noarch
r-devoid 0.1.0 Provides a non-drawing graphic device for benchmarking purpose. In order to properly benchmark graphic drawing code it is necessary to factor out the device implementation itself so that results are not related to the specific graphics device used during benchmarking. The ‘devoid’ package implements a graphic device that accepts all the required calls from R’s graphic engine but performs no action. Apart from benchmarking it is unlikely that this device has any practical use. / MIT linux-64, osx-64, win-64
r-devore7 0.7.6 Data sets and sample analyses from Jay L. Devore (2008), Probability and Statistics for Engineering and the Sciences (7th ed), Thomson. / GPL-2 noarch
r-devrate 0.1.10 A set of functions to quantify the relationship between development rate and temperature and to build phenological models. The package comprises a set of models and estimated parameters borrowed from a literature review in ectotherms. The methods and literature review are described in Rebaudo et al. (2018) <doi:10.1111/2041-210X.12935> and Rebaudo and Rabhi (2018) <doi:10.1111/eea.12693>. An example can be found in Rebaudo et al. (2017) <doi:10.1007/s13355-017-0480-5>. / GPL-2 noarch
r-devtools 2.0.2 Collection of package development tools. / GPL (>= 2) linux-32, linux-64, noarch, osx-64, win-32, win-64
r-df2json 0.0.2 It handles numerics, characters, factors, and logicals. / GPL-3 noarch
r-dfadjust 1.0.0 Computes small-sample degrees of freedom adjustment for heteroskedasticity robust standard errors, and for clustered standard errors in linear regression. See Imbens and Kolesár (1994) <doi:10.1162/REST_a_00552> for a discussion of these adjustments. / MIT noarch
r-dfcomb 3.0_0 Phase I/II adaptive dose-finding design for combination studies where toxicity rates are supposed to increase with both agents. / GPL-3 linux-64, osx-64, win-64
r-dfcompare 1.0.0 Compares two dataframes with a common key and returns the delta records. The package will return three dataframes that contain the added, changed, and deleted records. / GPL-3 noarch
r-dfcrm 0.2_2.1 Provides functions to run the CRM and TITE-CRM in phase I trials and calibration tools for trial planning purposes. / GPL-2 noarch
r-dfmta 1.7_0 Phase I/II adaptive dose-finding design for single-agent Molecularly Targeted Agent (MTA), according to the paper Phase I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau Determination using Adaptive Randomization, Riviere Marie-Karelle et al. (2016) <doi:10.1177/0962280216631763>. / GPL-3 linux-64, osx-64, win-64
r-dfoptim 2018.2_1 Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems. / GPL-2 noarch
r-dfphase1 1.1.1 Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed without making any parametric assumptions on the stable (in-control) distribution. / LGPL-2 linux-64, osx-64, win-64
r-dga 1.2 Performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size. / GPL-2 noarch
r-dglars 2.1.2 Differential geometric least angle regression method for fitting sparse generalized linear models. In this version of the package, the user can fit models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. Furthermore, several link functions can be used to model the relationship between the conditional expected value of the response variable and the linear predictor. The solution curve can be computed using an efficient predictor-corrector or a cyclic coordinate descent algorithm, as described in the paper linked to via the URL below. / GPL-2 linux-64, osx-64, win-64
r-dgmb 1.2 A set of functions have been implemented to generate random data to perform Monte Carlo simulations on structural models with formative constructs and interaction and nonlinear effects (Two-Step PLS Mode B structural models). The setup of the true model considers a simple structure with three formative exogenous constructs related to one formative endogenous construct. The routines take into account the interaction and nonlinear effects of the exogenous constructs on the endogenous construct. / GPL-2 noarch
r-dgodata 0.0.2 Provides data used by package ‘dgo’ in examples and vignettes. / GPL-3 noarch
r-dgof 1.2 This package contains a proposed revision to the stats::ks.test() function and the associated ks.test.Rd help page. With one minor exception, it does not change the existing behavior of ks.test(), and it adds features necessary for doing one-sample tests with hypothesized discrete distributions. The package also contains cvm.test(), for doing one-sample Cramer-von Mises goodness-of-fit tests. / GPL (>= 2.0) linux-64, osx-64, win-64
r-dhh 0.0.1 The density, cumulative distribution, quantiles, and i.i.d random variables of a heavy-headed distribution. For more information, please see the vignette. / GPL-2 noarch
r-dhsic 2.1 Contains an implementation of the d-variable Hilbert Schmidt independence criterion and several hypothesis tests based on it, as described in Pfister et al. (2017) <doi:10.1111/rssb.12235>. / GPL-3 linux-64, osx-64, win-64
r-di 1.1.4 A set of utilities for calculating the Deficit (frailty) Index (DI) in gerontological studies. The deficit index was first proposed by Arnold Mitnitski and Kenneth Rockwood and represents a proxy measure of aging and also can be served as a sensitive predictor of survival. For more information, see (i)Accumulation of Deficits as a Proxy Measure of Aging by Arnold B. Mitnitski et al. (2001), The Scientific World Journal 1, <DOI:10.1100/tsw.2001.58>; (ii) Frailty, fitness and late-life mortality in relation to chronological and biological age by Arnold B Mitnitski et al. (2001), BMC Geriatrics2002 2(1), <DOI:10.1186/1471-2318-2-1>. / GPL-3 noarch
r-diagonals 0.4.0 Several tools for handling block-matrix diagonals and similar constructs are implemented. Block-diagonal matrices can be extracted or removed using two small functions implemented here. In addition, non-square matrices are supported. Block diagonal matrices occur when two dimensions of a data set are combined along one edge of a matrix. For example, trade-flow data in the ‘decompr’ and ‘gvc’ packages have each country-industry combination occur along both edges of the matrix. / GPL-3 noarch
r-diagrammer 1.0.1 Build graph/network structures using functions for stepwise addition and deletion of nodes and edges. Work with data available in tables for bulk addition of nodes, edges, and associated metadata. Use graph selections and traversals to apply changes to specific nodes or edges. A wide selection of graph algorithms allow for the analysis of graphs. Visualize the graphs and take advantage of any aesthetic properties assigned to nodes and edges. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-dialr 0.3.0 Parse, format, and validate international phone numbers using Google’s ‘libphonenumber’ java library, <https://github.com/googlei18n/libphonenumber>. / GPL-3 noarch
r-dialrjars 8.10.14 Collects ‘libphonenumber’ jars required for the ‘dialr’ package. / GPL-3 noarch
r-diaplt 1.3.0 Visualize one-factor data frame. Beads plot consists of diamonds of each factor of each data series. A diamond indicates average and range. Look over a data frame with many numeric columns and a factor column. / MIT (FOSS) noarch
r-dice 1.2 This package provides utilities to calculate the probabilities of various dice-rolling events, such as the probability of rolling a four-sided die six times and getting a 4, a 3, and either a 1 or 2 among the six rolls (in any order); the probability of rolling two six-sided dice three times and getting a 10 on the first roll, followed by a 4 on the second roll, followed by anything but a 7 on the third roll; or the probabilities of each possible sum of rolling five six-sided dice, dropping the lowest two rolls, and summing the remaining dice. / GPL-2 noarch
r-dicedesign 1.8_1 Space-Filling Designs and Uniformity Criteria. / GPL-3 linux-64, osx-64, win-64
r-diceeval 1.4 Estimation, validation and prediction of models of different types : linear models, additive models, MARS,PolyMARS and Kriging. / GPL-3 noarch
r-dicekriging 1.5.6 Estimation, validation and prediction of kriging models. Important functions : km, print.km, plot.km, predict.km. / GPL-2 | GPL-3 linux-64, osx-64, win-64
r-dichromat 2.0_0 Collapse red-green or green-blue distinctions to simulate the effects of different types of color-blindness. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-dicionariosibge 1.6 This package contains the dictionaries for reading microdata from IBGE (Brazilian Institute of Geography and Statistics) surveys PNAD, PME and POF. / GPL-2 noarch
r-didacticboost 0.1.1 A basic, clear implementation of tree-based gradient boosting designed to illustrate the core operation of boosting models. Tuning parameters (such as stochastic subsampling, modified learning rate, or regularization) are not implemented. The only adjustable parameter is the number of training rounds. If you are looking for a high performance boosting implementation with tuning parameters, consider the ‘xgboost’ package. / GPL-3 noarch
r-dielectric 0.2.3 Physical constants. Gold, silver and glass permittivities, together with spline interpolation functions. / GPL-3 noarch
r-diezeit 0.1_0 A wrapper for the ZEIT ONLINE Content API, available at <http://developer.zeit.de>. ‘diezeit’ gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage. / MIT noarch
r-difconet 1.0_4 Estimation of DIFferential COexpressed NETworks using diverse and user metrics. This package is basically used for three functions related to the estimation of differential coexpression. First, to estimate differential coexpression where the coexpression is estimated, by default, by Spearman correlation. For this, a metric to compare two correlation distributions is needed. The package includes 6 metrics. Some of them needs a threshold. A new metric can also be specified as a user function with specific parameters (see difconet.run). The significance is be estimated by permutations. Second, to generate datasets with controlled differential correlation data. This is done by either adding noise, or adding specific correlation structure. Third, to show the results of differential correlation analyses. Please see <http://bioinformatica.mty.itesm.mx/difconet> for further information. / GPL-2 noarch
r-diffee 1.1.0 This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>. / GPL-2 noarch
r-diffmeshgp 0.1.0 This R function implements the nonstationary Kriging model proposed by Tuo, Wu and Yu (2014) <DOI:10.1080/00401706.2013.842935> for analyzing multi-fidelity computer outputs. This function computes the maximum likelihood estimates for the model parameters as well as the predictive means and variances of the exact solution (i.e., the conceptually highest fidelity). / GPL-2 noarch
r-diffobj 0.2.3 Generate a colorized diff of two R objects for an intuitive visualization of their differences. / GPL-2 linux-64, osx-64, win-64
r-diffpriv 0.4.2 An implementation of major general-purpose mechanisms for privatizing statistics, models, and machine learners, within the framework of differential privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms include the Laplace mechanism for releasing numeric aggregates, and the exponential mechanism for releasing set elements. A sensitivity sampler (Rubinstein & Alda, 2017) <arXiv:1706.02562> permits sampling target non-private function sensitivity; combined with the generic mechanisms, it permits turn-key privatization of arbitrary programs. / MIT noarch
r-diffr 0.1 An R interface to the ‘codediff’ JavaScript library (a copy of which is included in the package, see <https://github.com/danvk/codediff.js> for information). Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser. / GPL-2 noarch
r-diffusr 0.1.4 Implementation of network diffusion algorithms such as heat diffusion or Markov random walks. Network diffusion algorithms generally spread information in the form of node weights along the edges of a graph to other nodes. These weights can for example be interpreted as temperature, an initial amount of water, the activation of neurons in the brain, or the location of a random surfer in the internet. The information (node weights) is iteratively propagated to other nodes until a equilibrium state or stop criterion occurs. / GPL-3 linux-64, osx-64, win-64
r-digest 0.6.18 Implementation of a function ‘digest()’ for the creation of hash digests of arbitrary R objects (using the ‘md5’, ‘sha-1’, ‘sha-256’, ‘crc32’, ‘xxhash’ and ‘murmurhash’ algorithms) permitting easy comparison of R language objects, as well as a function ‘hmac()’ to create hash-based message authentication code. Please note that this package is not meant to be deployed for cryptographic purposes for which more comprehensive (and widely tested) libraries such as ‘OpenSSL’ should be used. / GPL (>= 2) linux-32, linux-64, osx-64, win-32, win-64
r-digitalpcr 1.1.0 The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute. / GPL-2 noarch
r-dils 0.8.1 Combine multiple-relationship networks into a single weighted network. The approach is similar to factor analysis in the that contribution from each constituent network varies so as to maximize the information gleaned from the multiple-relationship networks. This implementation uses Principal Component Analysis calculated using ‘prcomp’ with bootstrap subsampling. Missing links are imputed using the method of Chen et al. (2012). / MIT linux-64, osx-64, win-64
r-dime 1.2 A robust differential identification method that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) to analyze ChIP-seq (high-throughput genomic)data comparing two samples allowing for flexible modeling of data. / GPL-2 linux-64, osx-64, win-64
r-dimred 0.2.2 A collection of dimensionality reduction techniques from R packages and provides a common interface for calling the methods. / GPL-3 | file LICENSE linux-32, linux-64, osx-64, win-32, win-64
r-dinamic 1.0 This function implements the DiNAMIC procedure for assessing the statistical significance of recurrent DNA copy number aberrations (Bioinformatics (2011) 27(5) 678 - 685). / GPL-2 noarch
r-dint 2.1.0 S3 classes and methods to create and work with year-quarter, year-month and year-isoweek vectors. Basic arithmetic operations (such as adding and subtracting) are supported, as well as formatting and converting to and from standard R date types. / MIT noarch
r-diphiseq 0.2.0 Implements the algorithm described in Jun Li and Alicia T. Lamere, DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes (Unpublished). Detects not only genes that show different average expressions (differential expression, DE), but also genes that show different diversities of expressions in different groups (differentially dispersed, DD). DD genes can be important clinical markers. ‘DiPhiSeq’ uses a redescending penalty on the quasi-likelihood function, and thus has superior robustness against outliers and other noise. Updates from version 0.1.0: (1) Added the option of using adaptive initial value for phi. (2) Added a function for estimating the proportion of outliers in the data. (3) Modified the input parameter names for clarity, and modified the output format for the main function. / GPL-3 noarch
r-diptest 0.75_7 Compute Hartigan’s dip test statistic for unimodality / multimodality and provide a test with simulation based p-values, where the original public code has been corrected. / GPL-2 linux-64, osx-64, win-64
r-direct 1.0.1 A Bayesian clustering method for replicated time series or replicated measurements from multiple experimental conditions, e.g., time-course gene expression data. It estimates the number of clusters directly from the data using a Dirichlet-process prior. See Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361. <doi:10.1214/13-AOAS650>. / GPL-2 linux-64, osx-64, win-64
r-directedclustering 0.1.1 Allows the computation of clustering coefficients for directed and weighted networks by using different approaches. It allows to compute clustering coefficients that are not present in ‘igraph’ package. A description of clustering coefficients can be found in Directed clustering in weighted networks: a new perspective, Clemente, G.P., Grassi, R. (2017), <doi:10.1016/j.chaos.2017.12.007>. / GPL-3 noarch
r-directeffects 0.2 A set of functions to estimate the controlled direct effect of treatment fixing a potential mediator to a specific value. Implements the sequential g-estimation estimator described in Vansteelandt (2009) <doi:10.1097/EDE.0b013e3181b6f4c9> and Acharya, Blackwell, and Sen (2016) <doi:10.1017/S0003055416000216>. / GPL-2 noarch
r-directlabels 2018.05.22 An extensible framework for automatically placing direct labels onto multicolor ‘lattice’ or ‘ggplot2’ plots. Label positions are described using Positioning Methods which can be re-used across several different plots. There are heuristics for examining trellis and ggplot objects and inferring an appropriate Positioning Method. / GPL-3 noarch
r-directstandardisation 1.2 Calculate adjusted means and proportions of a variable by groups defined by another variable by direct standardisation, standardised to the structure of the dataset. / GPL-2 noarch
r-dirmult 0.1.3_4 Estimate parameters in Dirichlet-Multinomial and compute profile log-likelihoods. / GPL-2 noarch
r-discfrail 0.1 Functions for fitting Cox proportional hazards models for grouped time-to-event data, where the shared group-specific frailties have a discrete nonparametric distribution. The methods proposed in the package is described by Gasperoni, F., Ieva, F., Paganoni, A. M., Jackson, C. H., Sharples, L. (2018) <doi:10.1093/biostatistics/kxy071>. There are also functions for simulating from these models, with a nonparametric or a parametric baseline hazard function. / GPL-3 noarch
r-disclap 1.5 Discrete Laplace exponential family for models such as a generalized linear model / GPL-2 noarch
r-disclapmix 1.7.3 Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section). / GPL-2 linux-64, osx-64, win-64
r-discretefdr 1.3_1 Multiple testing procedures described in the paper Döhler, Durand and Roquain (2018) New FDR bounds for discrete and heterogeneous tests <doi:10.1214/18-EJS1441>. The main procedures of the paper (HSU and HSD), their adaptive counterparts (AHSU and AHSD), and the HBR variant are available and are coded to take as input a set of observed p-values and their discrete support under the null. A function to compute such p-values and supports for Fisher’s exact tests is also provided, along with a wrapper allowing to apply discrete procedures directly from contingency tables. / MIT linux-64, osx-64, win-64
r-discretelaplace 1.1.1 Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions. / GPL-3 noarch
r-discretemtp 0.1_2 Multiple testing procedures for discrete test statistics, that use the known discrete null distribution of the p-values for simultaneous inference. / GPL-2 noarch
r-discreterv 1.2.2 Create, manipulate, transform, and simulate from discrete random variables. The syntax is modeled after that which is used in mathematical statistics and probability courses, but with powerful support for more advanced probability calculations. This includes the creation of joint random variables, and the derivation and manipulation of their conditional and marginal distributions. / GPL-3 noarch
r-discretization 1.0_1 This package is a collection of supervised discretization algorithms. It can also be grouped in terms of top-down or bottom-up, implementing the discretization algorithms. / GPL-3 noarch
r-discriminer 0.1_29 Functions for Discriminant Analysis and Classification purposes covering various methods such as descriptive, geometric, linear, quadratic, PLS, as well as qualitative discriminant analyses / GPL-3 noarch
r-discsurv 1.4.0 Provides data transformations, estimation utilities, predictive evaluation measures and simulation functions for discrete time survival analysis. / GPL-3 noarch
r-diseasemapping 1.4.6 Formatting of population and case data, calculation of Standardized Incidence Ratios, and fitting the BYM model using INLA. / GPL-3 noarch
r-dishet 1.0.0 Model cell type heterogeneity of bulk renal cell carcinoma. The observed gene expression in bulk tumor sample is modeled by a log-normal distribution with the location parameter structured as a linear combination of the component-specific gene expressions. / GPL-2 noarch
r-dismo 1.1_4 Functions for species distribution modeling, that is, predicting entire geographic distributions form occurrences at a number of sites and the environment at these sites. / GPL-3 linux-64, osx-64, win-64
r-disparityfilter 2.2.3 The disparity filter algorithm is a network reduction technique to identify the ‘backbone’ structure of a weighted network without destroying its multi-scale nature. The algorithm is documented in M. Angeles Serrano, Marian Boguna and Alessandro Vespignani in Extracting the multiscale backbone of complex weighted networks, Proceedings of the National Academy of Sciences 106 (16), 2009. This implementation of the algorithm supports both directed and undirected networks. / GPL-2 noarch
r-displayhts 1.0 A package containing R functions for displaying data and results from high-throughput screening experiments. / GPL-2 noarch
r-dispmod 1.2 Functions for estimating Gaussian dispersion regression models (Aitkin, 1987 <doi:10.2307/2347792>), overdispersed binomial logit models (Williams, 1987 <doi:10.2307/2347977>), and overdispersed Poisson log-linear models (Breslow, 1984 <doi:10.2307/2347661>), using a quasi-likelihood approach. / GPL-2 noarch
r-disposables 1.0.3 Create disposable R packages for testing. You can create, install and load multiple R packages with a single function call, and then unload, uninstall and destroy them with another function call. This is handy when testing how some R code or an R package behaves with respect to other packages. / MIT noarch
r-dissutils 1.0 This package has extensible C code for computing dissimilarities between vectors. It also has a number of C functions for assembling collections of dissimilarities. In particular, it lets you find a matrix of dissimilarities between the rows of two input matrices. There are also functions for finding the nearest neighbors of each row of a matrix, either within the matrix itself or within another matrix. / GPL-2 linux-64, osx-64, win-64
r-distance.sample.size 0.0 Calculates the study size (either number of detections, or proportion of region that should be covered) to achieve a target precision for the estimated abundance. The calculation allows for the penalty due to unknown detection function, and for overdispersion. The user must specify a guess at the true detection function. / GPL-2 | GPL-3 noarch
r-distances 0.1.7.2 Provides tools for constructing, manipulating and using distance metrics. / GPL-3 linux-64, osx-64, win-64
r-distcomp 1.0_1 Implementing algorithms and fitting models when sites (possibly remote) share computation summaries rather than actual data over HTTP with a master R process (using ‘opencpu’, for example). A stratified Cox model and a singular value decomposition are provided. The former makes direct use of code from the R ‘survival’ package. (That is, the underlying Cox model code is derived from that in the R ‘survival’ package.) Sites may provide data via several means: CSV files, Redcap API, etc. An extensible design allows for new methods to be added in the future. Web applications are provided (via ‘shiny’) for the implemented methods to help in designing and deploying the computations. / LGPL-2 linux-64, osx-64, win-64
r-distcrete 1.0.3 Creates discretised versions of continuous distribution functions by mapping continuous values to an underlying discrete grid, based on a (uniform) frequency of discretisation, a valid discretisation point, and an integration range. For a review of discretisation methods, see Chakraborty (2015) <doi:10.1186/s40488-015-0028-6>. / MIT noarch
r-distdrawr 0.1.3 Download data from the FlorKart database of the floristic field mapping in Germany in a convenient way. The database incorporates distribution data for plants in Germany on the basis of quadrants on a topographical map with a resolution of 1 : 25000 (TK 25). The data is owned and provided by the German Federal Agency for Nature Conservation (BfN) and the Network Phytodiversity in Germany (NetPhyD). For further information please visit <http://www.floraweb.de/pflanzenarten/hintergrundtexte_florkart_organisation.html>. The author of this package is in no way associated with the BfN or NetPhyD. / GPL-2 noarch
r-distfree.cr 1.5.1 Constructs confidence regions without the need to know the sampling distribution of bivariate data. The method was proposed by Zhiqiu Hu & Rong-cai Yang (2013) <doi:10.1371/journal.pone.0081179.g001>. / GPL-2 noarch
r-distillery 1.0_6 Some very simple method functions for confidence interval calculation, bootstrap resampling, and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx. / GPL-2 noarch
r-distory 1.4.3 Geodesic distance between phylogenetic trees and associated functions. / BSD linux-64, osx-64, win-64
r-distrib 1.0 A different way for calculating pdf/pmf, cdf, quantile and random data such that the user is able to consider the name of related distribution as an argument and so easily can changed by a changing argument by user. It must be mentioned that the core and computation base of package ‘DISTRIB’ is package ‘stats’. Although similar functions are introduced previously in package ‘stats’, but the package ‘DISTRIB’ has some special applications in some special computational programs. / LGPL-3 noarch
r-distributions3 0.1.1 Tools to create and manipulate probability distributions using S3. Generics random(), pdf(), cdf() and quantile() provide replacements for base R’s r/d/p/q style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes. / MIT noarch
r-distributiontest 1.0 Provides new types of omnibus tests which are generally much more powerful than traditional tests (including the Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling tests),see Zhang (2002) <doi:10.1111/1467-9868.00337>. / GPL-3 noarch
r-distributionutils 0.6_0 Utilities are provided which are of use in the packages I have developed for dealing with distributions. Currently these packages are GeneralizedHyperbolic, VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of these packages requires DistributionUtils. Functionality includes sample skewness and kurtosis, log-histogram, tail plots, moments by integration, changing the point about which a moment is calculated, functions for testing distributions using inversion tests and the Massart inequality. Also includes an implementation of the incomplete Bessel K function. / GPL-2 linux-64, osx-64, win-64
r-disttools 0.1.7 Provides convenient methods for accessing the data in ‘dist’ objects with minimal memory and computational overhead. ‘disttools’ can be used to extract the distance between any pair or combination of points encoded by a ‘dist’ object using only the indices of those points. This is an improvement over existing functionality, which requires either coercing a ‘dist’ object into a matrix or calculating the one dimensional index corresponding to a pair of observations. Coercion to a matrix is undesirable because doing so doubles the amount of memory required for storage. In contrast, there is no inherent downside to the latter solution. However, in part due to several edge cases, correctly and efficiently implementing such a solution can be challenging. ‘disttools’ abstracts away these challenges and provides a simple interface to access the data in a ‘dist’ object using the latter approach. / MIT noarch
r-divdyn 0.8.0 Functions to describe sampling and diversity dynamics of fossil occurrence datasets (e.g. from the Paleobiology Database). The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) <doi:10.1101/423780>. / CC BY 4.0 linux-64, osx-64, win-64
r-divmelt 1.0.3 This package has tools for analyzing DNA melting data to generate HRM scores, the DNA diversity measure output of the HRM Diversity Assay. For additional documentation visit http://code.google.com/p/divmelt/. / GPL-2 noarch
r-divo 1.0.0 A set of tools for empirical analysis of diversity (a number and frequency of different types in population) and similarity (a number and frequency of shared types in two populations) in biological or ecological systems. / GPL-3 linux-64, osx-64, win-64
r-dixontest 1.0.0 For outlier detection in small and normally distributed samples the ratio test of Dixon (Q-test) can be used. Density, distribution function, quantile function and random generation for Dixon’s ratio statistics are provided as wrapper functions. The core applies McBane’s Fortran functions <doi:10.18637/jss.v016.i03> that use Gaussian quadrature for a numerical solution. / GPL-3 linux-64, osx-64, win-64
r-dkdna 0.1.1 Compute diffusion kernels on DNA polymorphisms, including SNP and bi-allelic genotypes. / GPL-2 linux-64, osx-64, win-64
r-dlasso 2.0.2 An implementation of the differentiable lasso (dlasso) and SCAD (dSCAD) using iterative ridge algorithm. This package allows selecting the tuning parameter by AIC, BIC, GIC and GIC. / GPL-2 noarch
r-dlib 1.0.3 Interface for ‘Rcpp’ users to ‘dlib’ <http://dlib.net> which is a ‘C’ toolkit containing machine learning algorithms and computer vision tools. It is used in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. This package allows R users to use ‘dlib’ through ‘Rcpp’. / BSL-1.0 linux-64, osx-64, win-64
r-dlm 1.1_5 Provides routines for Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models. / GPL-2 linux-64, osx-64, win-64
r-dma 1.4_0 Dynamic model averaging for binary and continuous outcomes. / GPL-2 noarch
r-dmrnet 0.2.0 Model selection algorithms for regression and classification, where the predictors can be numerical and categorical and the number of regressors exceeds the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka (2015) <doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk (2015) <http://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>. / GPL-2 noarch
r-dmt 0.8.20 Probabilistic dependency modeling toolkit. / BSD_2_clause noarch
r-dmwr 0.4.1 This package includes functions and data accompanying the book Data Mining with R, learning with case studies by Luis Torgo, CRC Press 2010. / GPL-2 noarch
r-dnaseqtest 1.0 Generates DNA sequences based on Markov model techniques for matched sequences. This can be generalized to several sequences. The sequences (taxa) are then arranged in an evolutionary tree (phylogenetic tree) depicting how taxa diverge from their common ancestors. This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and often impose further restrictions on the parameters. / GPL-2 noarch
r-dne 2.1.0 The DnE package involves functions to analyse the distribution of a set of given data. The basic idea of the analysis is chi-squared test. Functions which have the form as is.xxdistribution are used to analyse whether the data obeys the xxdistrbution. If you do not know which distribution to judge, use function is.dt(). / GPL-2 noarch
r-dng 0.2.1 Provides density, distribution function, quantile function and random generation for the split normal and split-t distributions, and computes their mean, variance, skewness and kurtosis for the two distributions (Li, F, Villani, M. and Kohn, R. (2010) <doi:10.1016/j.jspi.2010.04.031>). / GPL-2 linux-64, osx-64, win-64
r-dnmf 1.3 Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification. Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds. Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech. Cognitive neurodynamics 6.6 (2012): 525-535. / GPL-2 noarch
r-do 1.0.0.0 Flexibly convert data between long and wide format using just two functions: reshape_toLong() and reshape_toWide(). / GPL-3 noarch
r-dobad 1.0.6 Provides Frequentist (EM) and Bayesian (MCMC) Methods for Inference of Birth-Death-Immigration Markov Chains. / GPL-2 noarch
r-dobson 0.4 Example datasets from the book An Introduction to Generalised Linear Models (Year: 2018, ISBN:9781138741515) by Dobson and Barnett. / GPL-2 noarch
r-dockerfiler 0.1.3 Build a Dockerfile straight from your R session. ‘dockerfiler’ allows you to create step by step a Dockerfile, and provide convenient tools to wrap R code inside this Dockerfile. / MIT noarch
r-docopt 0.6.1 Define a command-line interface by just giving it a description in the specific format. / MIT noarch
r-docopulae 0.4.0 A direct approach to optimal designs for copula models based on the Fisher information. Provides flexible functions for building joint PDFs, evaluating the Fisher information and finding optimal designs. It includes an extensible solution to summation and integration called ‘nint’, functions for transforming, plotting and comparing designs, as well as a set of tools for common low-level tasks. / MIT linux-64, osx-64, win-64
r-docstring 1.0.0 Provides the ability to display something analogous to Python’s docstrings within R. By allowing the user to document their functions as comments at the beginning of their function without requiring putting the function into a package we allow more users to easily provide documentation for their functions. The documentation can be viewed just like any other help files for functions provided by packages as well. / GPL-2 noarch
r-documair 0.6_0 Production of R packages from tagged comments introduced within the code and a minimum of additional documentation files. / GPL (>= 2.15) linux-64, osx-64, win-64
r-document 3.1.0 Have you ever been tempted to create ‘roxygen2’-style documentation comments for one of your functions that was not part of one of your packages (yet)? This is exactly what this package is about: running ‘roxygen2’ on (chunks of) a single code file. / BSD_2_clause noarch
r-docusignr 0.0.3 Connect to the ‘DocuSign’ Rest API <https://www.docusign.com/p/RESTAPIGuide/RESTAPIGuide.htm>, which supports embedded signing, and sending of documents. / GPL-3 noarch
r-dodge 0.9_2 A variety of sampling plans are able to be compared using evaluations of their operating characteristics (OC), average outgoing quality (OQ), average total inspection (ATI) etc. / GPL-3 noarch
r-doestrare 0.2 Rare variant association test integrating variant position information. It aims to identify the presence of clusters of disease-risk variants in specific gene regions. For more details, please read the publication from Persyn et al. (2017) <doi:10.1371/journal.pone.0179364>. / GPL-2 linux-64, osx-64, win-64
r-doex 1.1 Contains several one-way heteroscedastic ANOVA tests such as Alexander-Govern test by Alexandern and Govern (1994) <doi:10.2307/1165140>, Alvandi et al. Generalized F test by Alvandi et al. (2012) <doi:10.1080/03610926.2011.573160>, Approximate F test by Asiribo and Gurland (1990) <doi:10.1080/03610929008830427>, Box F test by Box (1954) <doi:10.1214/aoms/1177728786>, Brown-Forsythe test by Brown and Forsythe (1974) <do:10.2307/1267501>, B2 test by Ozdemir and Kurt (2006) <http://sjam.selcuk.edu.tr/sjam/article/view/174>, Cochran F test by Cochran (1937) <https://www.jstor.org/stable/pdf/2984123.pdf>, Fiducial Approach test by Li et al. (2011) <doi:10.1016/j.csda.2010.12.009>, Generalized F test by Weerahandi (1995) <doi:10.2307/2532947>, Johansen F test by Johansen (1980) <doi:10.1093/biomet/67.1.85>, Modified Brown-Forsythe test by Mehrotra (1997) <doi:10.1080/03610919708813431>, Modified Welch test by Hartung et al.(2002) <doi:10.1007/s00362-002-0097-8>, One-Stage test by Chen and Chen (1998) <doi:10.1080/03610919808813501>, One-Stage Range test by Chen and Chen (2000) <doi:10.1080/01966324.2000.10737505>, Parametric Bootstrap test by Krishnamoorhty et al.(2007) <doi:10.1016/j.csda.2006.09.039>, Permutation F test by Berry and Mielke (2002) <doi:10.2466/pr0.2002.90.2.495>, Scott-Smith test by Scott and Smith (1971) <doi:10.2307/2346757>, Welch test by Welch(1951) <doi:10.2307/2332579>, and Welch-Aspin test by Aspin (1948) <doi:10.1093/biomet/35.1-2.88>. These tests are used to test the equality of group means under unequal variance. Furthermore, a modified version of Generalized F-test is improved to test the equality of non-normal group means under unequal variances and a revised version of Generalized F-test is given to test the equality of non-normal group means caused by skewness. / GPL-2 noarch
r-domc 1.3.5 Provides a parallel backend for the %dopar% function using the multicore functionality of the parallel package. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-dominanceanalysis 1.0.0 Dominance analysis is a method that allows to compare the relative importance of predictors in multiple regression models: ordinary least squares, generalized linear models and hierarchical linear models. The main principles and methods of dominance analysis are described in Budescu, D. V. (1993) <doi:10.1037/0033-2909.114.3.542> and Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129> for ordinary least squares regression. Subsequently, the extensions for multivariate regression, logistic regression and hierarchical linear models were described in Azen, R., & Budescu, D. V. (2006) <doi:10.3102/10769986031002157>, Azen, R., & Traxel, N. (2009) <doi:10.3102/1076998609332754> and Luo, W., & Azen, R. (2013) <doi:10.3102/1076998612458319>, respectively. / GPL-2 noarch
r-domino 0.3.1 A wrapper on top of the ‘Domino Command-Line Client’. It lets you run ‘Domino’ commands (e.g., run, upload, download) directly from your R environment. Under the hood, it uses R’s system function to run the ‘Domino’ executable, which must be installed as a prerequisite. ‘Domino’ is a service that makes it easy to run your code on scalable hardware, with integrated version control and collaboration features designed for analytical workflows (see <http://www.dominodatalab.com> for more information). / MIT noarch
r-doparallel 1.0.14 Provides a parallel backend for the %dopar% function using the parallel package. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-dorng 1.7.1 Provides functions to perform reproducible parallel foreach loops, using independent random streams as generated by L’Ecuyer’s combined multiple-recursive generator [L’Ecuyer (1999), <DOI:10.1287/opre.47.1.159>]. It enables to easily convert standard %dopar% loops into fully reproducible loops, independently of the number of workers, the task scheduling strategy, or the chosen parallel environment and associated foreach backend. / GPL-2 noarch
r-dos 1.0.0 Contains data sets, examples and software from the book Design of Observational Studies by Paul R. Rosenbaum, New York: Springer, <doi:10.1007/978-1-4419-1213-8>, ISBN 978-1-4419-1212-1. / GPL-2 noarch
r-dosearch 1.0.2 Identification of causal effects from arbitrary observational and experimental probability distributions via do-calculus and standard probability manipulations using a search-based algorithm. Allows for the presence of mechanisms related to selection bias (Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>), transportability (Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>), missing data (Mohan, K. and Pearl, J. and Tian., J. (2013) <http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf>) and arbitrary combinations of these. / GPL-2 linux-64, osx-64, win-64
r-dosefinding 0.9_16 The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology. / GPL-3 linux-64, osx-64, win-64
r-dostats 1.3.2 A small package containing helper utilities for creating function for computing statistics. / GPL-3 noarch
r-dotcall64 1.0_0 Provides .C64(), which is an enhanced version of .C() and .Fortran() from the foreign function interface. .C64() supports long vectors, arguments of type 64-bit integer, and provides a mechanism to avoid unnecessary copies of read-only and write-only arguments. This makes it a convenient and fast interface to C/C and Fortran code. / GPL-2 linux-64, osx-64, win-64
r-dotdot 0.1.0 Use ‘..’ on the right hand side of the ‘:=’ operator as a shorthand for the left hand side, so that ‘var := f(..) ..’ is equivalent to ‘var <- f(var) var’. This permits the user to be explicit about growing an object or overwriting it using its previous value, avoids repeating a variable name, and saves keystrokes, time, visual space and cognitive load. / GPL-3 noarch
r-dotenv 1.0.2 Load configuration from a ‘.env’ file, that is in the current working directory, into environment variables. / MIT noarch
r-doubcens 1.1 Contains the discrete nonparametric survivor function estimation algorithm of De Gruttola and Lagakos for doubly interval-censored failure time data and the discrete nonparametric survivor function estimation algorithm of Sun for doubly interval-censored left-truncated failure time data [Victor De Gruttola & Stephen W. Lagakos (1989) <doi:10.2307/2532030>] [Jianguo Sun (1995) <doi:10.2307/2533008>]. / GPL-2 linux-64, osx-64, win-64
r-double.truncation 1.4 Likelihood-based inference methods with doubly-truncated data are developed under various models. Nonparametric models are based on Efron and Petrosian (1999) <doi:10.1080/01621459.1999.10474187> and Emura, Konno, and Michimae (2015) <doi:10.1007/s10985-014-9297-5>. Parametric models from the special exponential family (SEF) are based on Hu and Emura (2015) <doi:10.1007/s00180-015-0564-z> and Emura, Hu and Konno (2017) <doi:10.1007/s00362-015-0730-y>. / GPL-2 noarch
r-doublecone 1.1 Performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones. See Bodhisattva Sen and Mary C Meyer (2016) <doi:10.1111/rssb.12178> for more details. / GPL-2 | GPL-3 noarch
r-doubleexpseq 1.1 Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample. / GPL-3 noarch
r-dovalidation 1.1.0 Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation. / GPL-2 noarch
r-downloader 0.4 Provides a wrapper for the download.file function, making it possible to download files over HTTPS on Windows, Mac OS X, and other Unix-like platforms. The ‘RCurl’ package provides this functionality (and much more) but can be difficult to install because it must be compiled with external dependencies. This package has no external dependencies, so it is much easier to install. / GPL-2 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-downsize 0.2.2 Toggles the test and production versions of a large workflow. / GPL-3 noarch
r-dparser 0.1.8 A Scannerless GLR parser/parser generator. Note that GLR standing for generalized LR, where L stands for left-to-right and R stands for rightmost (derivation). For more information see <https://en.wikipedia.org/wiki/GLR_parser>. This parser is based on the Tomita (1987) algorithm. (Paper can be found at <http://acl-arc.comp.nus.edu.sg/archives/acl-arc-090501d3/data/pdf/anthology-PDF/J/J87/J87-1004.pdf>). The original ‘dparser’ package documentation can be found at <http://dparser.sourceforge.net/>. This allows you to add mini-languages to R (like RxODE’s ODE mini-language Wang, Hallow, and James 2015 <DOI:10.1002/psp4.12052>) or to parse other languages like ‘NONMEM’ to automatically translate them to R code. To use this in your code, add a LinkingTo dparser in your DESCRIPTION file and instead of using #include <dparse.h> use #include <dparser.h>. This also provides a R-based port of the make_dparser <http://dparser.sourceforge.net/d/make_dparser.cat> command called mkdparser(). Additionally you can parse an arbitrary grammar within R using the dparse() function, which works on most OSes and is mainly for grammar testing. The fastest parsing, of course, occurs at the C level, and is suggested. / BSD_3_clause linux-64, osx-64, win-64
r-dpcid 1.0 Differential partial correlation identification with the ridge and the fusion penalties. / GPL-2 linux-64, osx-64, win-64
r-dpglasso 1.0 fits the primal graphical lasso, via one-at-a-time block-coordinate descent. / GPL-2 linux-64, osx-64, win-64
r-dplrcon 1.0 The concordance method is a non-parametric method based on bootstrapping that is used to test the hypothesis that two subsets of time series are similar in terms of mean, variance or both. This method was developed to address a concern within dendroclimatology that young trees may produce a differing climate response to older more established trees. Details of this method are available in Pirie, M. (2013). The Climate of New Zealand reconstructed from kauri tree rings: Enhancement through the use of novel statistical methodology. PhD. Dissertation, School of Environment and Department of Statistics, University of Auckland, New Zealand. This package also produces a figure with 3 panels, each panel is for a different climate variable. An example of this figure in included in On the influence of tree size on the climate - growth relationship of New Zealand kauri (Agathis australis): insights from annual, monthly and daily growth patterns. J Wunder, AM Fowler, ER Cook, M Pirie, SPJ McCloskey. Trees 27 (4), 937-948. For further R functions for loading your own dendroclimatology datasets and performing dendrochronology analysis refer to the R package dplR: Dendrochronology Program Library in R. The concordance procedure is intended to add to the standard dendrochronology techniques provided in dplR. / GPL-3 noarch
r-dplyr 0.8.0.1 A fast, consistent tool for working with data frame like objects, both in memory and out of memory. / MIT file LICENSE linux-32, linux-64, osx-64, win-32, win-64
r-dpp 0.1.2 This MCMC method takes a data numeric vector (Y) and assigns the elements of Y to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred. Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data. / MIT linux-64, osx-64, win-64
r-dprint 0.0.4 Provides a generalized method for printing tabular data within the R environment in order to make the process of presenting high quality tabular output seamless for the user. Output is directed to the R graphics device so that tables can be exported to any file format supported by the graphics device. Utilizes a formula interface to specify the contents of tables often found in manuscripts or business reports. In addition, formula interface provides inline formatting of the numeric cells of a table and renaming column labels. / GPL-2 noarch
r-dqshiny 0.0.3 Provides highly customizable modules to enhance your shiny apps. Includes layout independent collapsible boxes and value boxes, a very fast autocomplete input, rhandsontable extensions for filtering and paging and much more. / GPL-3 noarch
r-dr 3.0.10 Functions, methods, and datasets for fitting dimension reduction regression, using slicing (methods SAVE and SIR), Principal Hessian Directions (phd, using residuals and the response), and an iterative IRE. Partial methods, that condition on categorical predictors are also available. A variety of tests, and stepwise deletion of predictors, is also included. Also included is code for computing permutation tests of dimension. Adding additional methods of estimating dimension is straightforward. For documentation, see the vignette in the package. With version 3.0.4, the arguments for dr.step have been modified. / GPL-2 noarch
r-dragonking 0.1.0 Statistical tests and test statistics to identify events in a dataset that are dragon kings (DKs). The statistical methods in this package were reviewed in Wheatley & Sornette (2015) <doi:10.2139/ssrn.2645709>. / GPL-3 noarch
r-dragular 0.3.1 Move elements between containers in ‘Shiny’ without explicitly using ‘JavaScript’. It can be used to build custom inputs or to change the positions of user interface elements like plots or tables. / GPL-2 noarch
r-drat 0.1.5 Creation and use of R Repositories via helper functions to insert packages into a repository, and to add repository information to the current R session. Two primary types of repositories are support: gh-pages at GitHub, as well as local repositories on either the same machine or a local network. Drat is a recursive acronym: Drat R Archive Template. / GPL-2 noarch
r-draw 1.0.0 A set of user-friendly wrapper functions for creating consistent graphics and diagrams with lines, common shapes, text, and page settings. Compatible with and based on the R ‘grid’ package. / MIT noarch
r-drawr 1.0.1 We present DRaWR, a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types, preserving more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only the relevant properties. We then rerank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. / GPL-2 noarch
r-driftbursthypothesis 0.1.3 Calculates the T-Statistic for the drift burst hypothesis from the working paper Christensen, Oomen and Reno (2018) <DOI:10.2139/ssrn.2842535>. The authors’ MATLAB code is available upon request, see: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2842535>. / GPL-3 linux-64, osx-64, win-64
r-drillr 0.1 Provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill. / GPL-3 noarch
r-drimpute 1.0 R codes for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events (‘PCAreduce’, ‘SC3’, ‘PCA’, ‘t-SNE’, ‘Monocle’, ‘TSCAN’, etc). ‘DrImpute’ can improve the performance of such software by imputing dropout events. / GPL-3 linux-64, osx-64, win-64
r-drmdel 1.3.1 Dual empirical likelihood (DEL) inference under semiparametric density ratio models (DRM) in the presence of multiple samples, including population cumulative distribution function estimation, quantile estimation and comparison, density estimation, composite hypothesis testing for DRM parameters which encompasses testing for changes in population distribution functions as a special case, etc. / GPL-3 linux-64, osx-64, win-64
r-dropr 0.1 Drop out analysis for psychologists in a R based web application. Shiny is used to visualize and analyze drop outs tailored to the methods of online survey methodology. Concept and app presented at the SCIP Conference in Long Beach, California. / GPL-2 noarch
r-droptest 0.1.3 Generates simulated data representing the LOX drop testing process (also known as impact testing). A simulated process allows for accelerated study of test behavior. Functions are provided to simulate trials, test series, and groups of test series. Functions for creating plots specific to this process are also included. Test attributes and criteria can be set arbitrarily. This work is not endorsed by or affiliated with NASA. See ASTM G86-17, Standard Test Method for Determining Ignition Sensitivity of Materials to Mechanical Impact in Ambient Liquid Oxygen and Pressurized Liquid and Gaseous Oxygen Environments <doi:10.1520/G0086-17>. / MIT noarch
r-drr 0.0.3 An Implementation of Dimensionality Reduction via Regression using Kernel Ridge Regression. / GPL-3 | file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-ds 4.0 Performs various analyzes of descriptive statistics, including correlations, graphics and tables. / GPL-2 noarch
r-dsample 0.91.2.2 Two discretization-based Monte Carlo algorithms, namely the Fu-Wang algorithm and the Wang-Lee algorithm, are provided for random sample generation from a high dimensional distribution of complex structure. The normalizing constant of the target distribution needs not to be known. / GPL-2 noarch
r-dsbayes 1.1 Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials. / GPL-2 noarch
r-dsl 0.1_6.1 An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class. / GPL-3 linux-64, osx-64, win-64
r-dst 1.4.0 Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions. / GPL-2 noarch
r-dstat 1.0.4 A d-statistic tests the null hypothesis of no treatment effect in a matched, nonrandomized study of the effects caused by treatments. A d-statistic focuses on subsets of matched pairs that demonstrate insensitivity to unmeasured bias in such an observational study, correcting for double-use of the data by conditional inference. This conditional inference can, in favorable circumstances, substantially increase the power of a sensitivity analysis (Rosenbaum (2010) <doi:10.1007/978-1-4419-1213-8_14>). There are two examples, one concerning unemployment from Lalive et al. (2006) <doi:10.1111/j.1467-937X.2006.00406.x>, the other concerning smoking and periodontal disease from Rosenbaum (2017) <doi:10.1214/17-STS621>. / GPL-2 noarch
r-dt 0.5 Data objects in R can be rendered as HTML tables using the JavaScript library ‘DataTables’ (typically via R Markdown or Shiny). The ‘DataTables’ library has been included in this R package. The package name ‘DT’ is an abbreviation of ‘DataTables’. / GPL-3 | file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-dtables 0.2.0 Towards automation of descriptive frequencies and statistics tables. / GPL-3 noarch
r-dtangle 0.3.1 Deconvolving cell types from high-throughput gene profiling data. / GPL-3 noarch
r-dtaxg 0.1.0 To calculate the sensitivity and specificity in the absence of gold standard using the Bayesian method. The Bayesian method can be referenced at Haiyan Gu and Qiguang Chen (1999) <doi:10.3969/j.issn.1002-3674.1999.04.004>. / GPL-3 noarch
r-dtd 0.2.1 Provides fast methods to work with Merton’s distance to default model introduced in Merton (1974) <doi:10.1111/j.1540-6261.1974.tb03058.x>. The methods includes simulation and estimation of the parameters. / GPL-2 linux-64, osx-64, win-64
r-dtda 2.1_1 This package implements different algorithms for analyzing randomly truncated data, one-sided and two-sided (i.e. doubly) truncated data. Two real data sets are included. / GPL-2 noarch
r-dtda.cif 1.0 Nonparametric estimator of the cumulative incidences of competing risks under double truncation. The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) <doi:10.2307/2669997>. / GPL-2 linux-64, osx-64, win-64
r-dtda.ni 1.0 Non-iterative estimator for the cumulative distribution of a doubly truncated variable. de Uña-Álvarez J. (2018) <doi:10.1007/978-3-319-73848-2_37>. / GPL-2 noarch
r-dtk 3.5 This package was created to analyze multi-level one-way experimental designs. It is designed to handle vectorized observation and factor data where there are unequal sample sizes and population variance homogeneity can not be assumed. To conduct the Dunnett modified Tukey-Kramer test (a.k.a. the T3 Procedure), create two vectors: one for your observations and one for the factor level of each observation. The function, gl.unequal, provides a means to more conveniently produce a factor vector with unequal sample sizes. Next, use the DTK.test function to conduct the test and save the output as an object to input into the DTK.plot function, which produces a confidence interval plot for each of the pairwise comparisons. Lastly, the function TK.test conducts the original Tukey-Kramer test. / GPL-2 noarch
r-dtmcpack 0.1_2 A series of functions which aid in both simulating and determining the properties of finite, discrete-time, discrete state markov chains. Two functions (DTMC, MultDTMC) produce n iterations of a Markov Chain(s) based on transition probabilities and an initial distribution. The function FPTime determines the first passage time into each state. The function statdistr determines the stationary distribution of a Markov Chain. / GPL-2 noarch
r-dtp 0.1.0 Compute the dynamic threshold panel model suggested by (Stephanie Kremer, Alexander Bick and Dieter Nautz (2013) <doi:10.1007/s00181-012-0553-9>) in which they extended the (Hansen (1999) <doi: 10.1016/S0304-4076(99)00025-1>) original static panel threshold estimation and the Caner and (Hansen (2004) <doi:10.1017/S0266466604205011>) cross-sectional instrumental variable threshold model, where generalized methods of moments type estimators are used. / GPL-3 noarch
r-dtrlearn2 1.0 We provide a comprehensive software to estimate general K-stage DTRs from SMARTs with Q-learning and a variety of outcome-weighted learning methods. Penalizations are allowed for variable selection and model regularization. With the outcome-weighted learning scheme, different loss functions - SVM hinge loss, SVM ramp loss, binomial deviance loss, and L2 loss - are adopted to solve the weighted classification problem at each stage; augmentation in the outcomes is allowed to improve efficiency. The estimated DTR can be easily applied to a new sample for individualized treatment recommendations or DTR evaluation. / GPL-2 noarch
r-dtrreg 1.4 Dynamic treatment regime estimation and inference via G-estimation, dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference via bootstrap and (for G-estimation) recursive sandwich estimation. Estimation and inference for survival outcomes via Dynamic Weighted Survival Modeling (DWSurv). / GPL-2 noarch
r-dtsg 0.1.3 Basic time series functionalities such as listing of missing values, application of arbitrary aggregation as well as rolling window functions and automatic detection of periodicity. As it is mainly based on ‘data.table’, it is fast and - in combination with the ‘R6’ package - offers reference semantics. In addition to its native R6 interface, it provides an S3 interface inclusive an S3 wrapper method generator for those who prefer the latter. / MIT noarch
r-dtt 0.1_2 This package provides functions for 1D and 2D Discrete Cosine Transform (DCT), Discrete Sine Transform (DST) and Discrete Hartley Transform (DHT). / GPL-2 noarch
r-dub 0.2.0 Provides an operator for assigning nested components of a list to names via a concise pattern matching syntax. This is especially convenient for assigning individual names to the multiple values that a function may return in the form of a list, and for extracting deeply nested list components. / MIT noarch
r-dummies 1.5.6 Expands factors, characters and other eligible classes into dummy/indicator variables. / GPL-2 noarch
r-dummy 0.1.3 Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set). / GPL-2 noarch
r-dunn.test 1.3.5 Computes Dunn’s test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. ‘dunn.test’ makes k(k-1)/2 multiple pairwise comparisons based on Dunn’s z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn’s test may be understood as a test for median difference. ‘dunn.test’ accounts for tied ranks. / GPL-2 noarch
r-dunnetttests 2.0 For the implementation of the step-down or step-up Dunnett testing procedures, the package includes R functions to calculate critical constants and R functions to calculate adjusted P-values of the test statistics. In addition, the package also contains functions to evaluate testing powers and hence the necessary sample sizes specially for the classical problem of comparisons of several treatments with a control. / GPL-2 noarch
r-durmod 1.1_2 Estimation of piecewise constant mixed proportional hazard competing risk model with NPMLE. The model is described in S. Gaure et al. (2007) <doi:10.1016/j.jeconom.2007.01.015>, J. Heckman and B. Singer (1984) <doi:10.2307/1911491>, and B.G. Lindsay (1983) <doi:10.1214/aos/1176346059>. / Artistic-2.0 linux-64, osx-64, win-64
r-dwdlarger 0.1_0 Solving large scale distance weighted discrimination. The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) <arXiv:1604.05473> for more details. / GPL-2 noarch
r-dwlm 0.1.0 This linear model solution is useful when both predictor and response have associated uncertainty. The doubly weights linear model solution is invariant on which quantity is used as predictor or response. Based on the results by Reed(1989) <doi:10.1119/1.15963> and Ripley & Thompson(1987) <doi:10.1039/AN9871200377>. / GPL-2 noarch
r-dyads 1.1.2 Contains functions for the MCMC simulation of dyadic network models j2 (Zijlstra, 2017, <doi:10.1080/0022250X.2017.1387858>) and p2 (Van Duijn, Snijders & Zijlstra, 2004, <doi: 10.1046/j.0039-0402.2003.00258.x>) as described in Zijlstra, Van Duijn & Snijders (2009) <doi: 10.1348/000711007X255336>. / GPL-2 noarch
r-dydea 0.1.0 Finds regular and chaotic intervals in the data using the 0-1 test for chaos proposed by Gottwald and Melbourne (2004) <DOI:10.1137/080718851>. / GPL-3 noarch
r-dygraphs 1.1.1.6 An R interface to the ‘dygraphs’ JavaScript charting library (a copy of which is included in the package). Provides rich facilities for charting time-series data in R, including highly configurable series- and axis-display and interactive features like zoom/pan and series/point highlighting. / MIT file LICENSE linux-32, linux-64, noarch, osx-64, win-32, win-64
r-dykstra 1.0_0 Solves quadratic programming problems using Richard L. Dykstra’s cyclic projection algorithm. Routine allows for a combination of equality and inequality constraints. See Dykstra (1983) <doi:10.1080/01621459.1983.10477029> for details. / GPL-2 noarch
r-dym 0.2 Add a Did You Mean feature to the R interactive. With this package, error messages for misspelled input of variable names or package names suggest what you really want to do in addition to notification of the mistake. / BSD_3_clause noarch
r-dyn 0.2_9.6 Time series regression. The dyn class interfaces ts, irts(), zoo() and zooreg() time series classes to lm(), glm(), loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(), quantreg::rq(), randomForest::randomForest() and other regression functions allowing those functions to be used with time series including specifications that may contain lags, diffs and missing values. / GPL-3 noarch
r-dynamac 0.1.8 While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>). / GPL-2 noarch
r-dynamicdistribution 1.1 The package is aimed at dynamically visualizing probability distributions and their moments and all the commonly used distributions are included. / GPL-2 noarch
r-dynamichazard 0.6.5 Contains functions that lets you fit dynamic hazard models using state space models. The first implemented model is described in Fahrmeir (1992) <doi:10.1080/01621459.1992.10475232> and Fahrmeir (1994) <doi:10.1093/biomet/81.2.317>. Extensions hereof are available where the Extended Kalman filter is replaced by an unscented Kalman filter and other options including particle filters. The implemented particle filters support more general state space models. / GPL-2 linux-64, win-64
r-dynamictreecut 1.63_1 Contains methods for detection of clusters in hierarchical clustering dendrograms. / GPL-2 noarch
r-dynatree 1.2_10 Inference by sequential Monte Carlo for dynamic tree regression and classification models with hooks provided for sequential design and optimization, fully online learning with drift, variable selection, and sensitivity analysis of inputs. Illustrative examples from the original dynamic trees paper are facilitated by demos in the package; see demo(package=dynaTree). / LGPL-3 linux-64, osx-64, win-64
r-dynclust 3.13 DynClust is a two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus. / MIT noarch
r-dyncomp 0.0.2_1 While there are many well-established measures for identifying critical fluctuations and phase transitions, these measures only work with many points of measurement and thus are unreliable when studying short and coarse-grained time series. This package provides a measure for complexity in a time series that does not rely on long time series (Kaiser (2017), <doi:10.17605/OSF.IO/GWTKX>). / MIT noarch
r-dynetnlaresistance 0.1.0 An anonymization algorithm to resist neighbor label attack in a dynamic network. / MIT noarch
r-dynia 0.2 Fit dynamic intervention model using the arima() function. / GPL-2 noarch
r-dynpanel 0.1.0 Computes the first stage GMM estimate of a dynamic linear model with p lags of the dependent variables. / GPL-3 noarch
r-dynpred 0.1.2 The dynpred package contains functions for dynamic prediction in survival analysis. / GPL-2 linux-64, osx-64, win-64
r-dynprog 0.1.0 A domain-specific language for specifying translating recursions into dynamic-programming algorithms. See <https://en.wikipedia.org/wiki/Dynamic_programming> for a description of dynamic programming. / GPL-3 noarch
r-dzexpm 2.0 MCMC method to estimate and predict skewed spatial processes. A real data set is included. Reference: Wang, Yang and Majumdar (2018) <doi.org/10.1007/s00180-017-0741-3>. / GPL-2 noarch
r-e1071 1.7_1 Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, … / GPL-2 linux-32, linux-64, osx-64, win-32, win-64
r-eaf 1.8 Plots of the empirical attainment function for two objectives. / GPL-2 linux-64, osx-64, win-64
r-easyahp 0.1.1 Given the scores from decision makers, the analytic hierarchy process can be conducted easily. / GPL-3 noarch
r-easyanova 7.0 Perform analysis of variance and other important complementary analyses. The functions are easy to use. Performs analysis in various designs, with balanced and unbalanced data. / GPL-2 noarch
r-easycsv 1.0.8 Allows users to easily read multiple comma separated tables and create a data frame under the same name. Is able to read multiple comma separated tables from a local directory, a zip file or a zip file on a remote directory. / GPL-2 noarch
r-easynls 5.0 Fit and plot some nonlinear models. / GPL-2 noarch
r-easypubmed 2.13 Query NCBI Entrez and retrieve PubMed records in XML or text format. Process PubMed records by extracting and aggregating data from selected fields. A large number of records can be easily downloaded via this simple-to-use interface to the NCBI PubMed API. / GPL-2 noarch
r-easyreg 3.0 Performs analysis of regression in simple designs with quantitative treatments, including mixed models and non linear models. / GPL-2 noarch
r-easysvg 0.1.0 This SVG elements generator can easily generate SVG elements such as rect, line, circle, ellipse, polygon, polyline, text and group. Also, it can combine and output SVG elements into a SVG file. / MIT noarch
r-ebal 0.1_6 Package implements entropy balancing, a data preprocessing procedure that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population. / GPL-2 noarch
r-ebass 0.1 We propose a new sample size calculation method for trial-based cost-effectiveness analyses. Our strategy is based on the value of perfect information that would remain after the completion of the study. / GPL-3 noarch
r-ebdbnet 1.2.5 Infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks. / GPL-3 linux-64, osx-64, win-64
r-eben 4.6 Provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models. As a special case of the ‘EBglmnet’ package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects. / GPL-3 linux-64, osx-64, win-64
r-ebgenotyping 2.0.1 Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method. / GPL-2 noarch
r-ebglmnet 4.1 Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects. / GPL-3 linux-64, osx-64, win-64
r-ebprs 1.1.5 EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy. No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for each individual in testing data, and evaluate the PRS according to AUC and predictive r2. / GPL-3 noarch
r-ebrank 1.0.0 Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors. A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given. / CC0 noarch
r-ebsnp 1.0 Genotyping and SNP calling tool for single-sample next generation sequencing data analysis using an empirical Bayes method. / GPL-2 noarch
r-ecctmc 0.2.5 Draw sample paths for endpoint-conditioned continuous time Markov chains via modified rejection sampling or uniformization. / GPL-3 linux-64, osx-64, win-64
r-ecfsup 0.1_2 Testing the equality of several covariance functions of functional data. Four different methods are implemented: L2-norm with W-S naive, L2-norm with W-S bias-reduced, L2-norm (Zhang 2013) <ISBN:9781439862735>, and sup-norm with resampling (Guo et al. 2017) <arXiv:1609.04232>. / LGPL-3 linux-64, osx-64, win-64
r-echarts2shiny 0.2.13 Embed interactive charts to their Shiny applications. These charts will be generated by ECharts library developed by Baidu (<http://echarts.baidu.com/>). Current version supports line chart, bar chart, pie chart, scatter plot, gauge, word cloud, radar chart, tree map, and heat map. / GPL-2 noarch
r-ecipex 1.0 Provides a function that quickly computes the fine structure isotope patterns of a set of chemical formulas to a given degree of accuracy (up to the limit set by errors in floating point arithmetic). A data-set comprising the masses and isotopic abundances of individual elements is also provided. / GPL-2 noarch
r-eco 4.0_1 Implements the Bayesian and likelihood methods proposed in Imai, Lu, and Strauss (2008 <DOI: 10.1093/pan/mpm017>) and (2011 <DOI:10.18637/jss.v042.i05>) for ecological inference in 2 by 2 tables as well as the method of bounds introduced by Duncan and Davis (1953). The package fits both parametric and nonparametric models using either the Expectation-Maximization algorithms (for likelihood models) or the Markov chain Monte Carlo algorithms (for Bayesian models). For all models, the individual-level data can be directly incorporated into the estimation whenever such data are available. Along with in-sample and out-of-sample predictions, the package also provides a functionality which allows one to quantify the effect of data aggregation on parameter estimation and hypothesis testing under the parametric likelihood models. / GPL-2 linux-64, osx-64, win-64
r-ecodist 2.0.1 Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data. / GPL-2 linux-64, osx-64, win-64
r-ecohydmod 1.0.0 Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) <doi:10.1002/hyp.10953>. / GPL-2 noarch
r-econdemand 1.0 Tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR. / GNU General Public License version 2 noarch
r-ecoreg 0.2.2 Estimating individual-level covariate-outcome associations using aggregate data (ecological inference) or a combination of aggregate and individual-level data (hierarchical related regression). / GPL-2 noarch
r-ecosimr 0.1.0 Given a site by species interaction matrix, users can make inferences about species interactions by performance hypothesis comparing test statistics against a null distribution. The current package provides algorithms and metrics for niche-overlap, body size ratios and species co-occurrence. Users can also integrate their own algorithms and metrics within these frameworks or completely novel null models. Detailed explanations about the underlying assumptions of null model analysis in ecology can be found at http://ecosimr.org. / MIT noarch
r-ecosolver 0.5.2 R interface to the Embedded COnic Solver (ECOS), an efficient and robust C library for convex problems. Conic and equality constraints can be specified in addition to integer and boolean variable constraints for mixed-integer problems. This R interface is inspired by the python interface and has similar calling conventions. / GPL-3 linux-64, osx-64, win-64
r-ecotoxicology 1.0.1 Implementation of the EPA’s Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis. Probit and Spearman-Karber methods from Finney’s book Probit analysis a statistical treatment of the sigmoid response curve with options for most accurate results or identical results to the book. Probit and all the tables from Finney’s book (code-generated, not copied) with the generating functions included. Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard. Toxicity scales: Horsfall-Barratt, Archer, Gauhl-Stover, Fullerton-Olsen, etc. / GPL-3 noarch
r-ecotroph 1.6 EcoTroph is an approach and software for modelling marine and freshwater ecosystems. It is articulated entirely around trophic levels. EcoTroph’s key displays are bivariate plots, with trophic levels as the abscissa, and biomass flows or related quantities as ordinates. Thus, trophic ecosystem functioning can be modelled as a continuous flow of biomass surging up the food web, from lower to higher trophic levels, due to predation and ontogenic processes. Such an approach, wherein species as such disappear, may be viewed as the ultimate stage in the use of the trophic level metric for ecosystem modelling, providing a simplified but potentially useful caricature of ecosystem functioning and impacts of fishing. This version contains catch trophic spectrum analysis (CTSA) function and corrected versions of the mf.diagnosis and create.ETmain functions. / GPL-3 noarch
r-ecovirtual 1.1 Computer simulations of classical ecological models as a learning resource. / GPL-2 noarch
r-ecp 3.1.1 Implements various procedures for finding multiple change-points. Two methods make use of dynamic programming and pruning, with no distributional assumptions other than the existence of certain absolute moments in one method. Hierarchical and exact search methods are included. All methods return the set of estimated change- points as well as other summary information. / GPL-2 linux-64, osx-64, win-64
r-ed50 0.1.1 Functions of five estimation method for ED50 (50 percent effective dose) are provided, and they are respectively Dixon-Mood method (1948) <doi:10.2307/2280071>, Choi’s original turning point method (1990) <doi:10.2307/2531453> and it’s modified version given by us, as well as logistic regression and isotonic regression. Besides, the package also supports comparison between two estimation results. / GPL-3 noarch
r-edci 1.1_3 Detection of edge points in images based on the difference of two asymmetric M-kernel estimators. Linear and circular regression clustering based on redescending M-estimators. Detection of linear edges in images. / GPL-2 linux-64, osx-64, win-64
r-edesign 1.0_13 An implementation of maximum entropy sampling for spatial data is provided. An exact branch-and-bound algorithm as well as greedy and dual greedy heuristics are included. / GPL (>= 2.0) linux-64, osx-64, win-64
r-edf 1.0.0 Import physiologic data stored in the European Data Format (EDF and EDF) into R. Both EDF and EDF files are supported. Discontinuous EDF files are not yet supported. / MIT noarch
r-edfreader 1.2.1 Reads European Data Format files EDF and EDF, see <http://www.edfplus.info>, BioSemi Data Format files BDF, see <http://www.biosemi.com/faq/file_format.htm>, and BDF files, see <http://www.teuniz.net/edfbrowser/bdfplus%20format%20description.html>. The files are read in two steps: first the header is read and then the signals (using the header object as a parameter). / GPL-3 noarch
r-edfun 0.2.0 Easily creating empirical distribution functions from data: ‘dfun’, ‘pfun’, ‘qfun’ and ‘rfun’. / GPL-2 | GPL-3 noarch
r-edgarwebr 1.0.0 A set of methods to access and parse live filing information from the U.S. Securities and Exchange Commission (SEC - <https://sec.gov>) including company and fund filings along with all associated metadata. / MIT noarch
r-edgebundler 0.1.4 Generates interactive circle plots with the nodes around the circumference and linkages between the connected nodes using hierarchical edge bundling via the D3 JavaScript library. See <http://d3js.org/> for more information on D3. / GPL-3 noarch
r-edgecorr 1.0 Facilitates basic spatial edge correction to point pattern data. / GPL-2 noarch
r-edison 1.1.1 Package EDISON (Estimation of Directed Interactions from Sequences Of Non-homogeneous gene expression) runs an MCMC simulation to reconstruct networks from time series data, using a non-homogeneous, time-varying dynamic Bayesian network. Networks segments and changepoints are inferred concurrently, and information sharing priors provide a reduction of the inference uncertainty. / GPL-2 noarch
r-edma 1.5_3 Perform dynamic model averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing. / GPL-2 linux-64, osx-64, win-64
r-eegkitdata 1.0 Contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository. / GPL-2 noarch
r-effectsrelbaseline 0.5 Functions to test for changes of a response to a stimulus grouping relative to a background or baseline response. / GPL-3 noarch
r-effectstars 1.9 Notice: The package EffectStars2 provides a more up-to-date implementation of effect stars! EffectStars provides functions to visualize regression models with categorical response as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. The effects of the variables are plotted with star plots in order to allow for an optical impression of the fitted model. / GPL-2 noarch
r-effectstars2 0.1_2 Provides functions for the method of effect stars as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. Effect stars can be used to visualize estimates of parameters corresponding to different groups, for example in multinomial logit models. Beside the main function ‘effectstars’ there exist methods for special objects, for example for ‘vglm’ objects from the ‘VGAM’ package. / GPL-2 noarch
r-effecttreat 0.4 In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. Part of this software has been developed using funding provided from the European Union’s 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552. / GPL-2 noarch
r-efficientmaxeigenpair 0.1.4 An implementation for using efficient initials to compute the maximal eigenpair in R. It provides three algorithms to find the efficient initials under two cases: the tridiagonal matrix case and the general matrix case. Besides, it also provides two algorithms for the next to the maximal eigenpair under these two cases. / MIT noarch
r-efflog 1.0 Fitting a causal loglinear model and calculating the causal effects for a causal loglinear model with the multiplicative interaction or without the multiplicative interaction, obtaining the natural direct, indirect and the total effect. It calculates also the cell effect, which is a new interaction effect. / GPL-2 noarch
r-effsize 0.7.6 A collection of functions to compute the standardized effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A). The computation algorithms have been optimized to allow efficient computation even with very large data sets. / GPL-2 noarch
r-egst 1.0.0 Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual’s phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) <doi:10.1101/674226>. / GPL-3 noarch
r-eha 2.6.0 Sampling of risk sets in Cox regression, selections in the Lexis diagram, bootstrapping. Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. Parametric accelerated failure time models for left truncated and right censored data. / GPL-2 osx-64, win-64
r-ehof 1.8 Extended and enhanced hierarchical logistic regression models (called Huisman-Olff-Fresco in biology, see Huisman et al. 1993 JVS <doi:10.1111/jvs.12050>) models. Response curves along one-dimensional gradients including no response, monotone, plateau, unimodal and bimodal models. / GPL-2 noarch
r-eiadata 0.0.5 An R wrapper to allow the user to query categories and Series IDs, and import data, from the EIA’s API <https://www.eia.gov/opendata/>. / GPL-2 noarch
r-eigeninv 2011.8_1 Solves the ``inverse eigenvalue problem’’ which is to generate a real-valued matrix that has the specified real eigenvalue spectrum. It can generate infinitely many dense matrices, symmetric or asymmetric, with the given set of eigenvalues. Algorithm can also generate stochastic and doubly stochastic matrices. / GPL-2 noarch
r-eigenmodel 1.11 Estimation of the parameters in a model for symmetric relational data (e.g., the above-diagonal part of a square matrix), using a model-based eigenvalue decomposition and regression. Missing data is accommodated, and a posterior mean for missing data is calculated under the assumption that the data are missing at random. The marginal distribution of the relational data can be arbitrary, and is fit with an ordered probit specification. See Hoff (2007) <arXiv:0711.1146> for details on the model. / GPL-2 noarch
r-eigenprcomp 1.0 Computes confidence intervals for the proportion explained by the first 1,2,k principal components, and computes confidence intervals for each eigenvalue. Both computations are done via nonparametric bootstrap. / GPL-2 noarch
r-eikosograms 0.1.1 An eikosogram (ancient Greek for probability picture) divides the unit square into rectangular regions whose areas, sides, and widths, represent various probabilities associated with the values of one or more categorical variates. Rectangle areas are joint probabilities, widths are always marginal (though possibly joint margins, i.e. marginal joint distributions of two or more variates), and heights of rectangles are always conditional probabilities. Eikosograms embed the rules of probability and are useful for introducing elementary probability theory, including axioms, marginal, conditional, and joint probabilities, and their relationships (including Bayes theorem as a completely trivial consequence). They are markedly superior to Venn diagrams for this purpose, especially in distinguishing probabilistic independence, mutually exclusive events, coincident events, and associations. They also are useful for identifying and understanding conditional independence structure. As data analysis tools, eikosograms display categorical data in a manner similar to Mosaic plots, especially when only two variates are involved (the only case in which they are essentially identical, though eikosograms purposely disallow spacing between rectangles). Unlike Mosaic plots, eikosograms do not alternate axes as each new categorical variate (beyond two) is introduced. Instead, only one categorical variate, designated the response, presents on the vertical axis and all others, designated the conditioning variates, appear on the horizontal. In this way, conditional probability appears only as height and marginal probabilities as widths. The eikosogram is therefore much better suited to a response model analysis (e.g. logistic model) than is a Mosaic plot. Mosaic plots are better suited to log-linear style modelling as in discrete multivariate analysis. Of course, eikosograms are also suited to discrete multivariate analysis with each variate in turn appearing as the response. This makes it better suited than Mosaic plots to discrete graphical models based on conditional independence graphs (i.e. Bayesian Networks or BayesNets). The eikosogram and its superiority to Venn diagrams in teaching probability is described in W.H. Cherry and R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/paper.pdf>, its value in exploring conditional independence structure and relation to graphical and log-linear models is described in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/independence/paper.pdf>, and a number of problems, puzzles, and paradoxes that are easily explained with eikosograms are given in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/examples/paper.pdf>. / GPL-3 noarch
r-eila 0.1_2 Implementation of Efficient Inference of Local Ancestry using fused quantile regression and k-means classifier / GPL-2 noarch
r-eipartialid 0.1.2 Estimate district-level bounds for 2x2 ecological inference based on the approach described in the forthcoming article Jiang et al. (2019), Ecological Regression with Partial Identification, Political Analysis. Interval data regression is used to bound the nonidentified regression parameter in a linear contextual effects model, from which district-level bounds are derived. The approach here can be useful as a baseline of comparison for future work on ecological inference. / MIT noarch
r-eive 2.3 Performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced. / GPL-3 linux-64, osx-64, win-64
r-eiwild 0.6.7 This package allows to use the hybrid Multinomial-Dirichlet-Model of Ecological Inference for estimating inner Cells of RxC-Tables. This was already implemented in the eiPack-package. eiwild-package now has the possibility to use individual level data to support the aggregate level data and using different Hyperpriori-Distributions. / GPL-3 linux-64, osx-64, win-64
r-ekmcmc 0.1.0 Functions for estimating catalytic constant and Michaelis-Menten constant for enzyme kinetics model using Metropolis-Hasting algorithm within Gibbs sampler based on the Bayesian framework. Additionally, a function to create plot to identify the goodness-of-fit is included. / GPL-3 noarch
r-el 1.0 Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. / GPL-2 linux-64, osx-64
r-el2surv 1.1 Functions for computing critical values and implementing the one-sided/two-sided EL tests. / GPL-2 noarch
r-elasth 0.3.1 O pacote desponibiliza funções para estimar modelos de componentes não observados e determinar intervenções automaticamente. Com especial atenção para a replicação dos modelos utilizados na metodologia de calculo do resultado estrutural da SPE/MF. The package provides simple ways to estimates general unobserved components models and automatically detects intervenctions. It is specially useful to replicate Brazilian Ministry of Finance methodology to estimate income-output gap elasticities. / GPL-3 noarch
r-elasticnet 1.1.1 Provides functions for fitting the entire solution path of the Elastic-Net and also provides functions for estimating sparse Principal Components. The Lasso solution paths can be computed by the same function. First version: 2005-10. / GPL-2 noarch
r-elec 0.1.2.1 This is a bizarre collection of functions written to do various sorts of statistical election audits. There are also functions to generate simulated voting data, and simulated truth so as to do simulations to check characteristics of these methods. / GPL-2 noarch
r-elections 1.0 This includes a dataset on the outcomes of the USA presidential elections since 1920, and various predictors, as used in <http://vanderwalresearch.com/blog/15-elections>. / GPL-2 noarch
r-electivity 1.0.2 Provides all electivity algorithms (including Vanderploeg and Scavia electivity) that were examined in Lechowicz (1982) <doi:10.1007/BF00349007>, plus the example data that were provided for moth resource utilisation. / MIT noarch
r-elemstatlearn 2015.6.26.2 Useful when reading the book above mentioned, in the documentation referred to as `the book’. / GPL-2 noarch
r-elexr 1.0 Provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results. / MIT noarch
r-elhmc 1.1.0 A tool to draw samples from a Empirical Likelihood Bayesian posterior of parameters using Hamiltonian Monte Carlo. / GPL-2 noarch
r-elitism 1.0.4 Recently many new p-value based multiple test procedures have been proposed, and these new methods are more powerful than the widely used Hochberg procedure. These procedures strongly control the familywise error rate (FWER). This is a comprehensive collection of p-value based FWER-control stepwise multiple test procedures, including six procedure families and thirty multiple test procedures. In this collection, the conservative Hochberg procedure, linear time Hommel procedures, asymptotic Rom procedure, Gou-Tamhane-Xi-Rom procedures, and Quick procedures are all developed in recent five years since 2014. The package name elitism is an acronym of equipment for logarithmic and linear time stepwise multiple hypothesis testing. Version 1.0.0 was released on June 26, 2019. See Gou, J., and Zhang, F. (2020). Quick multiple test procedures and p-value adjustments. Technical report. / GPL-3 noarch
r-elliplot 1.2.0 Correlation chart of two set (x and y) of data. Using Quantiles. Visualize the effect of factor. / MIT (FOSS) noarch
r-ellipse 0.4.1 Contains various routines for drawing ellipses and ellipse-like confidence regions, implementing the plots described in Murdoch and Chow (1996), A graphical display of large correlation matrices, The American Statistician 50, 178-180. There are also routines implementing the profile plots described in Bates and Watts (1988), Nonlinear Regression Analysis and its Applications. / GPL-2 noarch
r-ellipsis 0.1.0 In S3 generics, it’s useful to take … so that methods can have additional argument. But this flexibility comes at a cost: misspelled arguments will be silently ignored. The ellipsis packages is an experiment that allows a generic to warn if any arguments passed in … are not used. / GPL-3 linux-64, osx-64, win-32, win-64
r-elliptic 1.4_0 A suite of elliptic and related functions including Weierstrass and Jacobi forms. Also includes various tools for manipulating and visualizing complex functions. / GPL-2 noarch
r-elmr 1.0 Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function. / GPL-2 | GPL-3 noarch
r-elmso 1.0.0 An implementation of the algorithm described in Efficient Large- Scale Internet Media Selection Optimization for Online Display Advertising by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below for journal text/citation and <http://www-bcf.usc.edu/~gareth/research/ELMSO.pdf> for a full-text version of the paper). The algorithm here is designed to allocate budget across a set of online advertising opportunities using a coordinate-descent approach, but it can be used in any resource-allocation problem with a matrix of visitation (in the case of the paper, website page- views) and channels (in the paper, websites). The package contains allocation functions both in the presence of bidding, when allocation is dependent on channel-specific cost curves, and when advertising costs are fixed at each channel. / GPL-3 noarch
r-elo 2.0.0 A flexible framework for calculating Elo ratings and resulting rankings of any two-team-per-matchup system (chess, sports leagues, ‘Go’, etc.). This implementation is capable of evaluating a variety of matchups, Elo rating updates, and win probabilities, all based on the basic Elo rating system. It also includes methods to benchmark performance, including logistic regression and Markov chain models. / GPL-2 linux-64, osx-64, win-64
r-elrm 1.2.4 Implements a Markov Chain Monte Carlo algorithm to approximate exact conditional inference for logistic regression models. Exact conditional inference is based on the distribution of the sufficient statistics for the parameters of interest given the sufficient statistics for the remaining nuisance parameters. Using model formula notation, users specify a logistic model and model terms of interest for exact inference. See Zamar et al. (2007) <doi:10.18637/jss.v021.i03> for more details. / GPL-2 linux-64, osx-64, win-64
r-elyp 0.7_5 Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) models. Empirical likelihood tests within a Cox model, for parameters defined via both baseline hazard function and regression parameters. / GPL-2 linux-64, osx-64, win-64
r-embc 2.0.2 Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, (Expectation-Maximization Binary Clustering for Behavioural Annotation). / GPL-3 linux-64, osx-64, win-64
r-embedsom 1.9.1 Provides a smooth mapping of multidimensional points into low-dimensional space defined by a self-organizing map. Designed to work with ‘FlowSOM’ and flow-cytometry use-cases. See Kratochvil et al. (2019) <doi:10.1101/496869>. / GPL-3 linux-64, osx-64, win-64
r-emc 1.3 random walk Metropolis, Metropolis Hasting, parallel tempering, evolutionary Monte Carlo, temperature ladder construction and placement / GPL-2 linux-64, osx-64, win-64
r-emcdf 0.1.2 Computes and visualizes empirical joint distribution of multivariate data with optimized algorithms and multi-thread computation. There is a faster algorithm using dynamic programming to compute the whole empirical joint distribution of a bivariate data. There are optimized algorithms for computing empirical joint CDF function values for other multivariate data. Visualization is focused on bivariate data. Levelplots and wireframes are included. / GPL-3 linux-64, osx-64, win-64
r-emcluster 0.2_12 EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning. / Mozilla Public License 2.0 linux-64, osx-64, win-64
r-emdbook 1.3.11 Auxiliary functions and data sets for Ecological Models and Data, a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0). / GPL-3 noarch
r-emdist 0.3_1 Package providing calculation of Earth Mover’s Distance (EMD). / MIT linux-64, osx-64, win-64
r-emg 1.0.7 Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution. / GPL-2 noarch
r-emhawkes 0.9.0 Simulate and fitting exponential multivariate Hawkes model. This package simulates a multivariate Hawkes model, introduced by Hawkes (1971) <doi:10.1093/biomet/58.1.83>, with an exponential kernel and fits the parameters from the data. Models with the constant parameters, as well as complex dependent structures, can also be simulated and estimated. The estimation is based on the maximum likelihood method, introduced by introduced by Ozaki (1979) <doi:10.1007/BF02480272>, with ‘maxLik’ package. / GPL-2 noarch
r-emistatr 1.2.2.0 Provides a fast and parallelised calculator to estimate combined wastewater emissions. It supports the planning and design of urban drainage systems, without the requirement of extensive simulation tools. The ‘EmiStatR’ package implements modular R methods. This enables to add new functionalities through the R framework. / GPL-3 noarch
r-emme2 0.9 This package includes functions to read and write to an EMME/2 databank / GPL-3 noarch
r-emmeans 1.3.4 Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and compact letter displays. Least-squares means are discussed, and the term estimated marginal means is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>. / GPL-2 | GPL-3 linux-32, linux-64, noarch, osx-64, win-32, win-64
r-emmixmfa 2.0.7 We provide functions to fit finite mixtures of multivariate normal or t-distributions to data with various factor analytic structures adopted for the covariance/scale matrices. The factor analytic structures available include mixtures of factor analyzers and mixtures of common factor analyzers. The latter approach is so termed because the matrix of factor loadings is common to components before the component-specific rotation of the component factors to make them white noise. Note that the component-factor loadings are not common after this rotation. Maximum likelihood estimators of model parameters are obtained via the Expectation-Maximization algorithm. See descriptions of the algorithms used in McLachlan GJ, Peel D (2000) <doi:10.1002/0471721182.ch8> McLachlan GJ, Peel D (2000) <ISBN:1-55860-707-2> McLachlan GJ, Peel D, Bean RW (2003) <doi:10.1016/S0167-9473(02)00183-4> McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) <doi:10.1016/j.csda.2006.09.015> Baek J, McLachlan GJ, Flack LK (2010) <doi:10.1109/TPAMI.2009.149> Baek J, McLachlan GJ (2011) <doi:10.1093/bioinformatics/btr112> McLachlan GJ, Baek J, Rathnayake SI (2011) <doi:10.1002/9781119995678.ch9>. / GPL-2 linux-64, osx-64, win-64
r-emmixskew 1.0.3 EM algorithm for Fitting Mixture of Multivariate Skew Normal and Skew t Distributions. An implementation of the algorithm described in Wang, Ng, and McLachlan (2009) <doi:10.1109/DICTA.2009.88>. / GPL-3 linux-64, osx-64, win-64
r-emmli 0.0.3 Fit models of modularity to morphological landmarks. Perform model selection on results. Fit models with a single within-module correlation or with separate within-module correlations fitted to each module. / MIT noarch
r-emmreml 3.1 The main functions are ‘emmreml’, and ‘emmremlMultiKernel’. ‘emmreml’ solves a mixed model with known covariance structure using the ‘EMMA’ algorithm. ‘emmremlMultiKernel’ is a wrapper for ‘emmreml’ to handle multiple random components with known covariance structures. The function ‘emmremlMultivariate’ solves a multivariate gaussian mixed model with known covariance structure using the ‘ECM’ algorithm. / GPL-2 noarch
r-emoa 0.5_0 Collection of building blocks for the design and analysis of evolutionary multiobjective optimization algorithms. / GPL-2 linux-64, osx-64, win-64
r-emon 1.3.2 Statistical tools for environmental and ecological surveys. Simulation-based power and precision analysis; detection probabilities from different survey designs; visual fast count estimation. / GPL-3 noarch
r-emov 0.1.1 Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position. / GPL-3 noarch
r-emp 2.0.5 Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) <DOI:10.1109/TKDE.2012.50>, <DOI:10.1016/j.ejor.2014.04.001>. / GPL-3 noarch
r-empichar 1.0.0 Evaluates the empirical characteristic function of univariate and multivariate samples. This package uses ‘RcppArmadillo’ for fast evaluation. It is also possible to export the code to be used in other packages at ‘C’ level. / MIT linux-64, osx-64, win-64
r-emplik 1.0_4.3 Empirical likelihood ratio tests for means/quantiles/hazards from possibly censored and/or truncated data. Now does regression too. This version contains some C code. / GPL-2 linux-64, osx-64, win-64
r-emplik2 1.21 Calculates the p-value for a mean-type hypothesis (or multiple mean-type hypotheses) based on two samples with censored data. / GPL-2 noarch
r-ems 1.2.7 Collection of functions for data analysis and editing of clinical and epidemiological data. Most of them are related to benchmark with prediction models. / GPL-2 noarch
r-emsaov 2.3 Provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS. / GPL-2 noarch
r-emsnm 1.0 It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure. / GPL-2 noarch
r-emt 1.1 The package provides functions to carry out a Goodness-of-fit test for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. A p-value can be calculated using different distance measures between observed and expected frequencies. A Monte Carlo method is provided to make the package capable of solving high-dimensional problems. / GPL-3 noarch
r-emulator 1.2_20 Allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. A vignette is provided and the help pages include examples. / GPL-3 noarch
r-emvs 1.0 An efficient expectation-maximization algorithm for fitting Bayesian spike-and-slab regularization paths for linear regression. Rockova and George (2014) <doi:10.1080/01621459.2013.869223>. / GPL-3 linux-64, osx-64, win-64
r-enc 0.2.1 Implements an S3 class for storing ‘UTF-8’ strings, based on regular character vectors. Also contains routines to portably read and write ‘UTF-8’ encoded text files, to convert all strings in an object to ‘UTF-8’, and to create character vectors with various encodings. / GPL-3 linux-64, osx-64, win-64
r-encode 0.3.6 Interconverts between ordered lists and compact string notation. Useful for capturing code lists, and pair-wise codes and decodes, for text storage. Analogous to factor levels and labels. Generics encode() and decode() perform interconversion, while codes() and decodes() extract components of an encoding. The function encoded() checks whether something is interpretable as an encoding. If a vector has an encoded ‘guide’ attribute, as_factor() uses it to coerce to factor. / GPL-3 noarch
r-endogenous 1.0 Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman’s classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) <DOI:10.3386/w0177> in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification. / GPL-2 noarch
r-endogmnp 0.2_1 endogMNP is an R package that fits a Bayesian multinomial probit model with endogenous selection, which is sometimes called an endogenous switching model. This can be used to model discrete