The ‘Changes on CRAN’ article from the 2009-2 issue.
Design, Monitoring, and Analysis of Clinical Trials.
Packages: ClinicalRobustPriors\(^*\), GroupSeq\(^*\), HH,
Hmisc\(^*\), MChtest\(^*\), PwrGSD\(^*\), asypow,
bifactorial\(^*\), binomSamSize, blockrand\(^*\), clinfun\(^*\),
coin, copas, epibasix, epicalc, experiment\(^*\),
gsDesign\(^*\), ldbounds\(^*\), meta, metafor, multcomp,
rmeta, seqmon\(^*\), speff2trial\(^*\), ssanv, survival\(^*\).
Maintainer: Ed Zhang.
Medical Image Analysis.
Packages: AnalyzeFMRI\(^*\), DICOM\(^*\), PET\(^*\),
Rniftilib\(^*\), VR, adimpro\(^*\), bitops, dcemri\(^*\),
dti\(^*\), fmri\(^*\), minpack.lm, tractor.base\(^*\).
Maintainer: Brandon Whitcher.
(* = core package)
BAS, glmmBUGS.
AquaEnv, CHNOSZ, NMRS, gpls, nlreg, paltran, plspm, quantchem, spls.
nnclust.
Lmoments, VarianceGamma, denstrip, evdbayes, evir, fExtremes, fitdistrplus, gamlss.dist\(^*\), gamlss.mx, ig.
CADFtest, Mcomp, Zelig, aod, expsmooth, fma, forecast.
aod, primer, vegetarian.
DoE.base\(^*\), DoE.wrapper\(^*\), RcmdrPlugin.DoE.
YieldCurve, atmi, forecast, ttrTests.
cudaBayesreg, doMC, doSNOW, foreach, speedglm.
penalizedSVM.
openNLPmodels.en, openNLPmodels.es.
Rcsdp, nleqslv.
MLCM, SEMModComp, irtProb, latdiag.
RgoogleMaps, nlme.
AER, BMA, BayHaz, DAAG, Epi, ICE, LearnBayes, LogicReg, MAMSE, MCMCglmm, MCMCpack, OrdFacReg, SMIR, SMPracticals, SimHap, VGAM, XReg, clinfun, cmprskContin, coin, condGEE, coxme, epiR, etm, fitdistrplus, gbm, gof, gss, interval, ipred, km.ci, kmi, lmec, locfit, logspline, maxstat, mixAK, mstate, multcomp, multtest, mvpart, nltm, p3state.msm, pamr, party, penalized, phmm, polspline, quantreg, rankhazard, rhosp, risksetROC, rms\(^*\), spatstat, superpc, survcomp, survey, uniCox.
CADFtest, EvalEst, KFAS, Mcomp, dlnm, expsmooth, fma, forecast\(^*\), fractalrock, mar1s, tiger, tis.
(* = core package)
Anderson-Darling GoF test with p-value calculation based on Marsaglia’s 2004 paper “Evaluating the Anderson-Darling Distribution”. By Carlos J. Gil Bellosta.
Estimation in adaptive group sequential trials. By Niklas Hack and Werner Brannath.
Statistical downscaling through CDF transform. Also performs computation of the Cramèr-von Mises and Kolmogorov-Smirnov statistics. By Mathieu Vrac and Paul-Antoine Michelangeli.
Variables selection for index models via correlation pursuit. By Wenxuan Zhong.
A machine learning suite for classification, regression, feature evaluation and ordinal evaluation, based on C++ code. Contains several model learning techniques in classification and regression, for example decision and regression trees with optional constructive induction and models in the leafs, random forests, kNN, naive Bayes, and locally weighted regression. Especially strong in feature evaluation algorithms where it contains several variants of Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM, etc. Additional strengths are its ordEval algorithm and its visualization used for ordinal features and classes. By Marko Robnik-Sikonja and Petr Savicky.
Simulate and analyze calcium imaging data obtained with ratiometric dyes. By Sebastien Joucla, Christophe Pouzat.
Statistical analysis of circular data using non-negative trigonometric sums (NNTS) models. Includes functions for calculation of densities and distributions, the estimation of parameters, plotting and more. By Juan José Fernández-Durán and Maria Mercedes Gregorio-Domínguez.
A collection of functions for color continuous high resolution images of circular spatial data, circular kriging, and simulation of circular random fields. By Bill Morphet.
DR-Integrator: Integrative analysis of DNA copy number and gene expression data described in Salari et al (2009). By Keyan Salari, Robert Tibshirani, and Jonathan R. Pollack.
Doubly Truncated Data Analysis. Implements different algorithms for analyzing randomly, one-sided and two-sided (i.e., doubly) truncated data. By Carla Moreira, Jacobo de Uña-Álvarez and Rosa Crujeiras.
Diagnostic accuracy of classification models. Several functions for evaluating the accuracy of classification models. Provides performance measures ‘cv’, ‘0.632’ and ‘0.632+’, estimation of the misclassification rate, sensitivity, specificity and AUC. If an application is computationally intensive, parallel execution can be used to reduce the time taken. By Sergej Potapov, Werner Adler and Berthold Lausen.
An intuitive graphical data analysis system for use with JGR. By Ian Fellows.
Functions for re-sampling a community matrix to compute Shannon’s Diversity index at different sampling levels. By Matthew K. Lau.
Creation of full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Additionally provides some utility functions used also by other DoE packages. By Ulrike
Wrapper package for design of experiments functionality. Creates various kinds of designs for (industrial) experiments by using and possibly enhancing design generation routines from other packages. Currently, response surface designs from package rsm and latin hypercube samples from package lhs have been implemented. By Ulrike
Extended Quasi Likelihood function (EQL). Computation of the EQL for a given family of variance functions, Saddlepoint-approximations and related auxiliary functions (e.g., Hermite polynomials). By Thorn Thaler.
Enhanced routines for plotting and analyzing valued relational data, considering valued ties. In particular, relative distances are calculated using social conductance methods. By Andrew C. Thomas.
A package dedicated to questionnaires. By Fournier Gwenaëlle, Cadoret Marine, Fournier Olivier, Le Poder François, Bouche Jérôme, and Lê Sébastien.
Dynamic Systems Estimation (DSE) extensions. By Paul Gilbert.
Identification of family relations using linked markers. By Øivind Skare.
A Flexible Modeling Environment for inverse modeling, sensitivity, identifiability, Monte Carlo analysis. Intended to work with models written as a set of differential equations that are solved either by an integration routine from package deSolve, or a steady-state solver from package rootSolve, but can also be used with other types of functions. By Karline Soetaert and Thomas Petzoldt.
Robust inference based on applying Fast and Robust Bootstrap on robust estimators. Available methods are multivariate regression, PCA and Hotelling tests. By Ella Roelant, Stefan Van Aelst and Gert Willems.
Gaussian Graphs Models selection: graph estimation in Gaussian Graphical Models. The main functions return the adjacency matrix of an undirected graph estimated from a data matrix. By Annie Bouvier, Christophe Giraud, Sylvie Huet, and Verzelen N.
Generalized Linear Multilevel Model with AR(p) errors. Functions to estimate the GLMM-AR(p) model for analyzing discrete time-series cross-sectional data via MCMC simulation. Also contains several useful utility functions, including an independent function for computing the Bayes factor with GLMM-AR(p) output, a function to recover the random coefficients at the individual level, and a function to do prediction by using the posterior distributions. By Xun Pang.
Goodness-Of-Fit tests for the family of Skew-Normal models. Implements a method for checking if a skew-normal model fits the observed dataset, when all parameters are unknown. While location and scale parameters are estimated by moment estimators, the shape parameter is integrated with respect to the prior predictive distribution, as proposed in Box (1980). A default and proper prior on skewness parameter is used to obtain the prior predictive distribution, as proposed in Cabras, & Castellanos (2008). By Veronica Paton Romero.
Genome-Wide Association analyses with Family data: Functions to test genetic associations between SNPs and a continuous/dichotomous trait using family data, and to make genome-wide p-value plots and QQ plots. By Ming-Huei Chen and Qiong Yang.
Infer time delay gene regulatory networks using time course gene expression profiles. The main idea of the time delay linear model is to fit a linear regression model using a set of putative regulators to estimate the transcription pattern of a specific target gene. By Tao Huang.
Simulation and analysis of spatial structure of population genetics data. By Sophie Ancelet.
Statistical analysis of static chamber concentration data for trace gas flux estimation. By Asger R. Pedersen.
A Handbook of Statistical Analyses Using R (2nd Edition). Functions, data sets, analyses and examples from the second edition of the book “A Handbook of Statistical Analyses Using R” (Brian S. Everitt and Torsten Hothorn, Chapman & Hall/CRC, 2008). By Brian S. Everitt and Torsten Hothorn.
Facilitates automated HTML report creation, in particular framed HTML pages and dynamically sortable tables. By Markus Loecher.
Bayesian investigation of Hardy-Weinberg Equilibrium via estimation and testing. Three models are currently considered: HWE, a model parametrized in terms of the allele frequencies and a single inbreeding coefficient f, and the saturated model. Testing is based on Bayes factors. By Jon Wakefield.
Performs a genetic association analysis of case-parent triad (trio) data with multiple markers. Can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. Estimates relative risk and p-values associated with each haplotype. Uses MLE to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. Also allows estimation of effects of maternal haplotypes, particularly appropriate in perinatal epidemiology. By H. K. Gjessing.
An implementation of the Hybrid Monte Carlo and Multipoint Hybrid Monte Carlo sampling techniques described in Liu (2001), “Monte Carlo Strategies in Computing”. By Richard D. Morey.
Testing for monotonic relationship between gene expression and doses in a microarray experiment. Several testing procedures including the global likelihood ratio test (Bartholomew, 1961), Williams (1971, 1972), Marcus (1976), M (Hu et al. 2005) and the modified M (Lin et al. 2007) are used to test for the monotonic trend in gene expression with respect to doses. BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutilie 2004) FDR controlling procedures are applied to adjust the raw p-values obtained from the permutations. By Dan Lin et al.
Functions for fast Kalman filtering, state and disturbance smoothing, forecasting and simulation of multivariate time-variant state space models. All functions can use exact diffuse initialization when distributions of some or all elements of initial state vector are unknown. Filtering, state smoothing and simulation functions use the sequential processing algorithm, which is faster than standard approach, and also allows singularity of the prediction error variance matrix. By Jouni Helske.
Decomposes a set of variables into cliques and separators depending on their association which is measured using Random Forests. By Corinne Dahinden.
Hybrid Markov chain Monte Carlo using Gaussian Processes. Uses hybrid MCMC to simulate from a multimodal target distribution. A Gaussian process approximation makes this possible when derivatives are unknown. Serves to minimize the number of function evaluations in Bayesian calibration of computer models using parallel tempering. Allows replacement of the true target distribution in high temperature chains, or complete replacement of the target. Mark J. Fielding.
Maximum Likelihood Conjoint Measurement. Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them. This package contains functions to estimate the contribution of the \(n\) scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact. By Kenneth Knoblauch and Laurence T. Maloney.
Implement different types of model mixing and model selection methods for linear or logistic models. By Marie Morfin and David Makowski.
Non-clustered heatmap alternatives. Creates heatmap like plots in 2 and 3 dimensions, without the need for cluster analysis. Like the heatmap, the plots created display both a dimensionally reduced representation of the data as well as the data itself. They are intended to be used in conjunction with dimensional reduction techniques such as PCA. By Satwik Rajaram and Yoshi Oono.
Order-restricted Information Criterion-based Clustering of genes. Clusters are given by genes matched to prespecified profiles across various ordered treatment groups. Particularly useful for analyzing data obtained from short time-course or dose-response microarray experiments. By Tianqing Liu, Nan Lin, Ningzhong Shi and Baoxue Zhang.
Functions for the Oja median, Oja signs and ranks and methods based upon them. By Daniel Fischer, Jyrki Klaus Nordhausen and Daniel Vogel.
Partial Correlation Coefficient with Information Theory. Provides the PCIT algorithm developed by Reverter and Chan (2008) which identifies significant gene to gene associations to define edges in a weighted network. By Nathan S. Watson-Haigh.
Calculate the probability of correct selection (PCS). Given \(k\) populations (can be in thousands), what is the probability that a given subset of size \(t\) contains the true top \(t\) populations? This package finds this probability and offers three tuning parameters (\(G\), \(d\), \(L\)) to relax the definition. By Jason Wilson.
Multiplicity control using Pooled LIS (PLIS) statistics. PLIS is a multiple testing procedure for testing several groups of hypotheses. Linear dependency is expected from the hypotheses within the same group and is modeled by hidden Markov Models. It is noted that, for PLIS, a smaller p value does not necessarily imply more significance because of dependency among the hypotheses. A typical application of PLIS is to analyze genome wide association studies datasets, where SNPs from the same chromosome are treated as a group and exhibit strong linear genomic dependency. By Zhi Wei & Wenguang Sun.
Implementation of the Pearson distribution system, including full support for the (d,p,q,r)-family of functions for probability distributions and fitting via method of moments and maximum likelihood method. By Martin Becker.
Functions for Qualitative Comparative Analysis (QCA). Can be used for all three types of QCA. Has methods for simplifying assumption and contradictory simplifying assumption, and can return constrained results by including or excluding specific conditions. By Ronggui Huang.
Easy handling of and access to files organized in structured directories. A file set refers to a set of files located in one or more directories on the file system. This package provides classes and methods to locate, setup, subset, navigate and iterative over such sets. The API is designed such that these classes can be subsetted to provide for instance a richer API for special file formats. By Henrik Bengtsson.
Simple R Interface to Microsoft PowerPoint using rcom. By Wayne Jones.
Write MS-Word documents from R, using the statconnDCOM server to communicate with MS-Word via the COM interface. By Christian Ritter.
Interface to Embedded Oracle Berkeley DB(tm). By Jeffrey A. Ryan.
Reproducible Computing. Allows the user to create and use reproducible computations for the purpose of research and education. The meta data about the computations are stored in a remote repository which is hosted at www.freestatistics.org. By Patrick Wessa.
Estimates regression trees with random effects as a way to use data mining techniques to describe longitudinal or panel data. By Rebecca Sela and Jeffrey Simonoff.
Bindings between R and the ImageJ Java based image processing and analysis platform. By Romain François and Philippe Grosjean.
C++ classes to embed R in C++ applications. By Dirk Eddelbuettel.
R interface to last.fm API. By Greg Hirson.
Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures. This is a model-based clustering algorithm that returns a hierarchy of classes, similar to hierarchical clustering, but also similar to finite mixture models. By E. Andres Houseman.
Simulation Investigation for Empirical Network Analysis. Fits models to longitudinal networks. By various authors.
Robust tests for case-control genetic association studies: allelic based test, Cochran-Armitage trend test, maximin efficiency robust test, MAX3 test and genetic model selection test. By Yong Zang, Wingkam Fung and Gang Zheng.
Rcmdr plugin for (industrial) Design of Experiments. Currently in beta status. By Ulrike
An Rcmdr plug-in based on the Quality control class Stat 4300. By Erin Hodgess.
Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions. Includes transport routines in porous media, in estuaries, and in bodies with variable shape. By Karline Soetaert and Filip Meysman.
Functions for reading JPEG and PNG files, requiring libjpeg. By Markus Loecher.
Relative risk estimation for prospective and retrospective data. By Bob Wheeler.
R MPFR - Multiple Precision Floating-Point Reliable. Aims to provide S4 classes and methods for arithmetic including transcendental (“special”) functions for arbitrary precision floating point numbers. To this end, it interfaces to the LGPL’ed MPFR (Multiple Precision Floating-Point Reliable) Library which itself is based on the GMP (GNU Multiple Precision) Library. By Martin Mächler.
R interface to nifticlib to read/write ANALYZE(TM)7.5/NIfTI-1 volume images. By Oliver Granert.
Workbooks in Excel that illustrate statistical concepts by accessing R functions from Excel. These workbooks use the automatic recalculation mode of Excel to update calculations and graphs in R. Downloads an executable which installs the workbooks on MS Windows systems where RExcel has already been installed. By Richard M. Heiberger and Erich Neuwirth.
Statistical Analysis of Fuzzy Data. Aims to provide some basic functions for doing statistics with one dimensional fuzzy data (in the form of polygonal fuzzy numbers). Contains functions for the basic operations on the class of fuzzy numbers (sum, scalar product, mean, Hukuhara difference) as well as for calculating (Bertoluzza) distance, sample variance, sample covariance, sample correlation, and the Dempster-Shafer (levelwise) histogram. Includes functionality to simulate fuzzy random variables, bootstrap tests for the equality of means, and to do linear regression given trapezoidal fuzzy data. By Wolfgang Trutschnig and Asun Lubiano.
Semiparametric elicitation. Implements a novel method for transferring expert statements about an uncertain bounded quantity into a probability distribution (see Bornkamp and Ickstadt (2009) for a detailed description). For this purpose B-splines are used, and the density is obtained by penalized least squares, where the penalty encourages to distribute probability mass as uniformly as possible. Provides methods for fitting the expert’s distribution as well as methods for evaluating the underlying density and cdf. In addition methods for plotting the expert’s distribution, drawing random numbers and calculating quantiles of the expert’s distribution are provided. By Bornkamp.
SNP-Haplotype Adaptive REgression (SHARE): an adaptive algorithm to select the most informative set of SNPs for genetic association. By James Y. Dai.
(Iterative) Sure Independence Screening for Generalized Linear Models and Cox’s Proportional Hazards models. By Jianqing Fan, Yang Feng, Richard Samworth and Yichao Wu.
SWord: Use R in Microsoft Word (Installer). Creating articles and reports in Word is easy. Adding R results (figures, tables, summaries) requires manually copying the data from R to Word. Changing the data requires redoing the analyses in R and re-inserting the results into Word manually. SWord integrates R scripts and results into Word documents. Documents can be edited and read even without R installed (or without any knowledge of R). The functionality of embedding the scripts is somewhat similar to what Sweave does for LaTeX documents. By Thomas Baier.
Implementation of the SigWin-detector for the detection of regions of increased or decreased gene expression (RIDGEs and anti-RIDGES) in transcriptome maps and the presentation in so called RIDGEOGRAMs as described by Marcia A. Inda et al. (2008). By Wim de Leeuw.
Social Relation Model (SRM) analyses for single round-robin groups. These analyses are either based on one manifest variable, one latent construct measured by two manifest variables, two manifest variables and their bivariate relations, or two latent constructs each measured by two manifest variables (in the last case, four Round-Robin matrices have to be provided). By S.C. Schmukle, F.D. Schoenbrodt and M.D. Back.
A GUI implementation of hierarchical Bayesian models of working memory, used for analyzing change detection data. By Richard D. Morey.
Modeling and estimation of the yield curve, implementing the Nelson-Siegel, Diebold-Li and Svensson models. Also includes the data of the term structure of interest rate of Federal Reserve and European Central Bank. By Sergio Salvino Guirreri.
Robust spline interpolation for dual color array comparative genomic hybridisation data. By Tomas William Fitzgerald.
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. By Marc Vandemeulebroecke.
Fit generalized additive mixed models based on the mixed model algorithm of lme4. By Fabian Scheipl.
Statistical analysis of surveys with anchoring vignettes. By Jonathan Wand, Gary King, and Olivia Lau.
Version of the ant apache build tool, with a few R specific tasks to ease use of ant within an R package. By Romain François.
Analysis of large Affymetrix microarray data sets. Implements classes for files and sets of files for various Affymetrix file formats, e.g., AffymetrixCdfFile, AffymetrixCelFile, and AffymetrixCelSet. These are designed to be memory efficient but still being fast. The idea is to keep all data on file and only read data into memory when needed. Clever caching mechanisms are used to minimize the overhead of data I/O. All of the above is hidden in the package API and for the developer (and the end user), the data is queried as if it lives in memory. With this design it is only the diskspace that limits what number of arrays can be analyzed. By Henrik Bengtsson, Ken Simpson, Elizabeth Purdom, and Mark Robinson.
Private support package for aroma.affymetrix et al., with the package API still in alpha/beta. By Henrik Bengtsson.
Creation of additive models with semiparametric predictors, emphasizing term objects, especially (1) implementation of a term class hierarchy, and (2) interpretation and evaluation of term estimates as functions of explanatories. By Charlotte Maia.
Analysis and usage of the trading rules, which are based on technical market indicators as well as on the time series analysis. By Waldemar Kemler and Peter Schaffner.
Bayesian clustering using spike-and-slab hierarchical model, suitable for clustering high-dimensional data. Builds a dendrogram with log posterior as a natural distance defined by the model. Can also compute Bayesian discrimination probabilities equivalent to the implemented Bayesian clustering. Spike-and-Slab models are adopted in a way to be able to produce an importance measure for clustering and discriminant variables. The method works properly for data with small sample size and high dimensions. The model parameter estimation maybe difficult, depending on data structure and the chosen distribution family. By Vahid Partovi Nia and Anthony C. Davison.
Cross-Validation for the SVD (Bi-Cross-Validation). This package implements methods for choosing the rank of an SVD approximation via cross validation. It provides both Gabriel-style “block” holdouts and Wold-style “speckled” holdouts. Also included is an implementation of the SVDImpute algorithm. See Owen & Perry’s 2009 AOAS article (http://arxiv.org/abs/0908.2062) and Perry’s 2009 PhD thesis (http://arxiv.org/abs/0909.3052) for more information about Bi-crossvalidation. By Patrick O. Perry.
Bayesian Discrete Ordered Classification of DNA Barcodes. By Michael Anderson.
Routines for Block Diagonal Symmetric matrices, a special case of sparse matrices, used by coxme and kinship. By Terry Therneau.
Best subset GLM using AIC, BIC, EBIC, BICq or Cross-Validation. For the normal case, the “leaps” is used; otherwise, a slower exhaustive search. By A.I. McLeod and Changjiang Xu.
Confidence intervals and sample size determination for a binomial proportion under simple random sampling and pooled sampling. Such computations are e.g. of interest when investigating the incidence or prevalence in populations. Contains functions to compute coverage probabilities and coverage coefficients of the provided confidence intervals procedures. Sample size calculations are based on expected length. By Michael with contributions by Wei Liu.
Calculation of bootstrapped response and correlation functions for use in dendroclimatology. By Christian Zang.
Solvers for boundary value problems (BVPs) of systems of ordinary
differential equations (ODEs) via an interface to the FORTRAN
function twpbvp and an R implementation of the shooting method. By
Karline Soetaert.
A Tcl/Tk GUI for computation and visualization of simple, multiple and joint correspondence analysis with the ca package. By Angelos Markos.
A collection of functions useful for various general situations. By David M. Schruth.
A set of functions for retrieving data from CIMIS, the California Irrigation Management Information System. By Greg Hirson.
Categorical Marginal Models: Quite extensive package for the estimation of marginal models for categorical data. By Wicher Bergsma and Andries van der Ark.
Estimation and testing of continuous mark-specific relative risks in two groups as described in Gilbert, McKeague & Sun (2008). Implements the methods presented in the paper for testing mark-specific hazards ratios and for estimation of mark-specific incidence ratios that are cumulative in time or cumulative in both time and the continuous mark. By Peter Gilbert, Ian McKeague, and Yanqing Sun.
A collection of useful helper routines developed by students of the Center for the Mathematical Research, Stankin, Moscow. By Andrey Paramonov.
Read big text files column by column: tries to solve the memory restrictions posed by such files by breaking them into columns which are subsequently read individually into R. By Carlos J. Gil Bellosta.
Solves for the mean parameters, the variance parameter, and their asymptotic variance in a conditional GEE for recurrent event gap times, as described by Clement and Strawderman (2009). Makes a parametric assumption for the length of the censored gap time. By David Clement.
Create and manipulate data frames that contain country names or country coding schemes. Standardizes country names, converts them into one of seven coding schemes, assigns region descriptors, and generates empty dyadic or country-year dataframes from the coding schemes. By Vincent Arel-Bundock.
Mixed Effects Cox Models: Cox proportional hazards models containing Gaussian random effects, also known as frailty models. By Terry Therneau.
Various R tools for http://crantastic.org/. By Bjørn Arild Maeland.
Package for CShapes, a GIS dataset of country boundaries (1946–2008). Includes functions for data extraction and the computation of weights matrices. By Nils B. Weidmann, Doreen Kuse, and Kristian Skrede Gleditsch.
CUDA Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis. Compute Unified Device Architecture (CUDA) is a software platform for massively parallel high-performance computing on NVIDIA GPUs. This package provides a CUDA implementation of a Bayesian multilevel model for the analysis of brain fMRI data. By Adelino Ferreira da Silva.
A collection of routines and documentation that allows one to perform a quantitative analysis of dynamic contrast-enhanced or diffusion-weighted MRI data. Medical imaging data should be organized using either the Analyze or NIfTI data formats. By Brandon Whitcher and Volker Schmid, with contributions from Andrew Thornton.
Descriptive statistics: functions to describe weighted categorical variables and functions to facilitate the character encoding conversion of objects. By Jakson Aquino. Includes R source code and/or documentation written by Dirk Enzmann, Marc Schwartz, and Nitin Jain.
Harrington and Derringer-Suich type desirability functions. By Heike Trautmann and Detlef Steuer and Olaf Mersmann.
Some traditional methods to detect dichotomous differential item functioning (DIF) in psychometrics. Both uniform and non-uniform DIF effects can be detected, with methods relying upon item response models or not. Some methods deal with more than one focal group. By Sébastien Béland, David Magis and Gilles Raîche.
An easy to use Graphical User Interface for spots correlation analysis, score plot, classification, feature selection and power analysis for 2D DIGE experiment data. By Yue Fan, Thomas Brendan Murphy, and R. William G. Watson.
Estimate parameters in Dirichlet-Multinomial and compute profile log-likelihoods. By Torben Tvedebrink.
Distributed Lag Non-linear Models. Contains functions to specify basis and cross-basis matrices in order to run distributed lag models and their non-linear extension, then to predict and graph the results for a fitted model. By Antonio Gasparrini and Ben Armstrong.
Provides a parallel backend for the foreach %dopar% function
using Simon Urbanek’s multicore package. By REvolution Computing.
Provides a parallel backend for the foreach %dopar% function
using Luke Tierney’s snow package. By REvolution Computing.
Expands factors, characters and other eligible classes into dummy/indicator variables. By Christopher Brown.
Interactive visualization of dataframes and factorial planes. By Julien Durand, Sébastien Lê.
Empirical likelihood ratio tests for means. By Dan Yang, Dylan Small.
Fits a Bayesian multinomial probit model with endogenous selection, which is sometimes called an endogenous switching model. This can be used to model discrete choice data when respondents select themselves into one of several groups. This package is based on the MNP package by Kosuke Imai and David A. van Dyk. By Lane F. Burgette.
Estimates Output: stores the estimates of several models, and formats these to a table of the form estimate starred and standard error. below. Default output is LaTeX but output to CSV for later editing in a spreadsheet tool is possible as well. Works for linear models and panel models from package plm. By Felix Kaminsky.
Exact Conditional Tests and Confidence Intervals for \(2 \times 2\) tables. Calculates Fisher’s exact and Blaker’s exact tests. By M.P. Fay.
A mixed integer description of an arable farm. Finds optimal farming plans given economic and social preference information. By Ira R. Cooke.
Functional data sets. By Han Lin Shang and Rob J Hyndman.
Path algorithm for the general Fused Lasso Signal Approximator. By Holger
Estimates the baseline of mortality free of influenza epidemics, and the respective excess deaths, for more than one time series (age groups, gender, regions, etc.). By Nunes B, Natario I and Carvalho L.
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. Intended to be used for its return value, rather than for its side effects. Using foreach without side effects also facilitates executing the loop in parallel. By REvolution Computing.
Generate fractal time series with non-normal returns distribution. The basic principle driving fractal generation of time series is that data is generated iteratively based on increasing levels of resolution. The initial series is defined by a so-called initiator pattern and then generators are used to replace each segment of the initial pattern. Regular, repeatable patterns can be produced by using the same seed and generators. By using a set of generators, non-repeatable time series can be produced. This technique is the basis of the fractal time series process in this package. By Brian Lee Yung Rowe.
Implementation of the “Flexible kernels for RBF network” algorithm. By Fernando Martins.
Estimates a frequency Moving Average Plot (MAP) from multinomial data and a continuous covariate. The frequency MAP is a moving average estimate of category frequencies, where frequency means and posterior bounds are estimated. Comparisons of two frequency MAPs as well as odds ratios can be plotted. By Colin McCulloch.
Functional time series analysis. By Rob J Hyndman and Han Lin Shang.
Computes the bootstrap goodness-of-fit test for the generalized Pareto distribution proposed by Villaseñor-Alva and González-Estrada (2009). The null hypothesis includes heavy and non-heavy tailded gPd’s. Also provides functionality for fitting the gPd to data using the parameter estimation methods proposed in the same article. By Elizabeth González Estrada, José A. Villaseñor Alva.
Efficient selection of undirected graphical models for high-dimensional datasets. Provides tools for selecting trees, forests and decomposable models minimizing information criteria such as AIC or BIC, and for displaying the independence graphs of the models. Also some useful tools for analyzing graphical structures. It supports the use of discrete, continuous, or both types of variables. By Gabriel Coelho Goncalves de Abreu, Rodrigo Labouriau, and David Edwards.
Playing games using a NWS Server. Allows playing different card games (e.g. uno, poker, …) using an NWS Server as the card table. By Markus Schmidberger and Fabian Grandke.
Data for GAMLSS models. By Mikis Stasinopoulos and Bob Rigby.
Fit generalized additive mixed models via a version of mgcv’s
gamm() function, using lme4 for estimation via Fabian Scheipl’s
trick. By Simon Wood.
Predict the discrete response based on selected high dimensional features, such as gene expression data. The data are modeled with Bayesian Gaussian models. 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 will appear stronger. This package provides a way to avoid this bias and yields well-calibrated prediction for the test cases when one uses F-statistic to select features. By Longhai Li.
Simulate, model, and display data from a time-course microarray experiment with periodic gene expression. Fits models in a normal mixture model framework with mean approximated by a truncated Fourier series and covariance structure modeled by an ARMA\((p,q)\) process. Estimation is performed with the EM algorithm. By Timothy McMurry and Arthur Berg.
Map a gene list to function categories defined in GOSlim or Kegg. The results can be plotted as a pie chart to provide a quick view of the distribution of the gene list among the function categories. By Xutao Deng.
Simulation of GLMDM. By Jeff Gill, George Casella, Minjung Kyung and Jonathan Rapkin.
A permutation test for inference in generalized linear models. Useful when parameter estimates in ordinary GLMs fail to converge or are unreliable due to small sample size. By Wiebke Werft and Douglas M. Potter.
GLM model selection and multimodel inference made easy: automated
model selection for GLMs. Provides a wrapper for glm() and similar
functions, automatically generating all possible models (under
constraints set by the user) with the specified response and
explanatory variables, and finding the best models in terms of some
Information Criterion (AIC, AICc or BIC). Can handle very large
numbers of candidate models. Features a Genetic Algorithm to find
the best models when an exhaustive screening of the candidates is
not feasible. By Vincent Calcagno.
Read data from files readable by gnumeric. Can read a whole sheet or a range, from several file formats, including the native format of gnumeric. Reading is done by using ssconvert (a file converter utility included in the gnumeric distribution http://projects.gnome.org/gnumeric/) to convert the requested part to CSV. By Karoly Antal.
Derive group sequential designs and describe their properties. By Keaven Anderson.
Tool for sending interactive bioinformatic heatmaps with tool-tip content. By Daniel P Gaile, Lori A. Shepherd, Lara Sucheston, Andrew Bruno, and Kenneth F. Manly.
Implements various measures of information theory based on several entropy estimators. By Patrick E. Meyer.
Convert inline comments to documentation. Generates Rd files from R source code with comments, providing for quick, sustainable package development. The syntax keeps code and documentation close together, and is inspired by the Don’t Repeat Yourself principle. By Toby Dylan Hocking.
Classes and methods for automated spatial interpolation. By Edzer Pebesma, Jon Skøien and others.
Integrate Omics data project. Supplies two efficient methodologies, regularized CCA and sparse PLS, to unravel relationships between two heterogeneous data sets of size \(n \times p\) and \(n \times q\) where the \(p\) and \(q\) variables are measured on the same samples or individuals \(n\). These data may come from high throughput technologies, such as omics data (e.g., transcriptomics, metabolomics or proteomics data) that require an integrative or joint analysis. However, integrOmics can also be applied to any other large data sets where \(p+q \gg n\). rCCA is a regularized version of CCA to deal with the large number of variables. sPLS allows variable selection in a one step procedure and two frameworks are proposed: regression and canonical analysis. Numerous graphical outputs are provided to help interpreting the results. By Sébastien Dejean, Ignacio González and Kim-Anh Lê Cao.
integrative mixture of experts. Mixture of experts models (Jacobs et al., 1991) were introduced to account for nonlinearities and other complexities in the data, and are of interest due to their wide applicability and the advantages of fast learning via the expectation-maximization (EM) algorithm. The package features an ME extension to combine categorical clinical factors and continuous microarray data in a binary classification framework to analyze cancer studies. By Kim-Anh Lê Cao.
Weighted Logrank Tests and NPMLE for interval censored data. By Michael P. Fay.
The Iterative Signature Algorithm (ISA), a biclustering algorithm that finds modules in an input matrix. A module or bicluster is a block of the reordered input matrix. By Gábor Csárdi.
Iterator construct for R. Iterators allow a programmer to traverse through all the elements of a vector, list, or other collection of data. By REvolution Computing.
Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions. By Arthur Allignol.
Writes a file of commands for the dot program to draw a graph proposed by Rosenbaum and useful for checking some properties of various sorts of latent scale. By Michael Dewey.
Direct labeling functions that use the lattice package. By Toby Dylan Hocking.
Collapsed Gibbs sampling methods for topic models. Implements Latent Dirichlet allocation (LDA) and related models, including (but not limited to) sLDA, corrLDA, and the mixed-membership stochastic blockmodel. Inference for all of these models is implemented via a fast collapsed Gibbs sampler written in C. Utility functions for reading/writing data typically used in topic models, as well as tools for examining posterior distributions are also included. By Jonathan Chang.
Laplace approximated EM Microarray Analysis. LEMMA is used to detect “nonnull genes” — genes for which the average response in treatment group 1 is significantly different from the average response in group 2, in normalized microarray data. LEMMA is an implementation of an approximate EM algorithm to estimate the parameters in the assumed linear model in Bar, Booth, Schifano, Wells (2009). By Haim Bar and Elizabeth Schifano.
Tools for Longitudinal Data. By Christophe Genolini.
LOgistic Regression Differential Item Functioning (DIF) using Item Response Theory (IRT): analysis of DIF for dichotomous and polytomous items using an iterative hybrid of (ordinal) logistic regression and IRT. By Seung W. Choi, with contributions from Laura E. Gibbons and Paul K. Crane.
Multiplicative AR(1) with Seasonal Processes, a stochastic process model built on top of AR(1). Provides the following procedures for MAR(1)S processes: fit, compose, decompose, advanced simulate and predict. By Andrey Paramonov.
Datasets and tutorials for use in the MArine, Riverine, Estuarine, LAcustrine and Coastal sciences. By Karline Soetaert, Thomas Petzoldt, and Filip Meysman.
MApping Transitions In Continuous Character Evolution. Tools for an information-theoretic approach to estimating the probability of continuous character transitions on phylogenetic trees. By Andrew Hipp, with contributions from Marcial Escudero.
Methods that apply to rows and columns of a matrix. Methods are optimized for speed and memory. Currently in a beta phase. By Henrik Bengtsson (partly by Robert Gentleman).
Methods for processing a sample of (hard) clusterings, e.g., the MCMC output of a Bayesian clustering model. Includes methods that find a single best clustering to represent the sample, which are based on the posterior similarity matrix or a relabeling algorithm. By Arno Fritsch.
Parametric and nonparametric causal mediation analysis. Implements the methods and suggestions in Imai, Keele, and Yamamoto (2008) and Imai, Keele, Tingley (2009). In addition to the estimation of causal mediation effects, allows researchers to conduct sensitivity analysis for certain parametric models. By Luke Keele, Dustin Tingley, Teppei Yamamoto, Kosuke Imai.
A collection of functions for conducting meta-analyses in R. Fixed- and random-effects models (with and without moderators) can be fitted via the general linear (mixed-effects) model. For \(2 \times 2\) table data, the Mantel-Haenszel and Peto’s method are also implemented. By Wolfgang Viechtbauer.
Fits normal mixture models to data and uses them to compute effect size estimates and local and tail area false discovery rates. To make this precise, suppose you have many normally distributed \(z\)s, and each \(z_i\) has mean \(\delta_i\). This package will estimate \(\delta_i\) based on the \(z\)s (effect sizes), \(P(\delta_i=0|z_i)\) (local false discovery rates) and \(P(\delta_i=0\;|\;|z_i|>z)\) (tail area false discovery rates). By Omkar Muralidharan, with many suggestions from Bradley Efron.
Modeling temperature effects using time series data. Fits a constrained segmented distributed lag regression model to epidemiological time series of mortality, temperature, and other confounders. By Vito M. R. Muggeo.
Datasets and functions from Wright and London’s Modern Regression Techniques. By Daniel B. Wright.
Functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models. By Hein Putter, Liesbeth de Wreede, and Marta Fiocco.
Mailmerge using R, LaTeX, and the Web. Provides mailmerge methods for reading spreadsheets of addresses and other relevant information to create standardized but customizable letters. Provides a method for mapping US ZIP codes, including those of letter recipients. Provides a method for parsing and processing HTML code from online job postings of the American Political Science Association. By Ryan T. Moore and Andrew Reeves.
Testing of multiple outcomes using i.i.d. decompositions. By Christian B. Pipper, Christian Ritz.
Functions for exploring and using the Munsell colour system. By Charlotte Wickham.
Generalized Shapiro-Wilk test for multivariate normality as proposed by Villaseñor-Alva and González-Estrada (2009). By Elizabeth González Estrada and José A. Villaseñor Alva.
Multivariate generalization of the Shapiro-Francia test for normality. By David Delmail.
Functions for non-bipartite optimal matching. By Bo Lu, Robert Greevy, Cole Beck.
Nearest-neighbor tools for clustering. Finds nearest neighbors and the minimum spanning tree (MST) for large data sets, and does clustering using the MST. By Thomas Lumley.
Compute and visualize the Node Harvest estimator. Node harvest is a simple interpretable tree-like estimator for high-dimensional regression and classification. A few nodes are selected from an initially large ensemble of nodes, each associated with a positive weight. New observations can fall into one or several nodes and predictions are the weighted average response across all these groups. By Nicolai Meinshausen.
Nonparametric Analysis of Longitudinal Data in Factorial Experiments. By Kimihiro Noguchi, Mahbub Latif, Karthinathan Thangavelu, Frank Konietschke, Yulia R. Gel, and Edgar Brunner.
Grows oblique trees to classification data. By Alfred Truong.
odfWeave support for the survey package. By Thomas Lumley.
Object Oriented Statistical Programming. Support for OOSP, especially by extending S3 capabilities, providing pointer and component objects, and providing basic support for symbolic-numeric statistical programming. By Charlotte Maia.
Estimation of optimal number of biomarkers for two-group microarray based classifications at a given error tolerance level for various classification rules. By Mizanur Khondoker.
Analyzing survival data from illness-death models. By Luís Meira-Machado and Javier Roca-Pardiñas.
Toy example of an S4 package, to illustrate the book “Petit Manuel de Programmation Orientee Objet sous R”. By Christophe Genolini.
Elucidates the dependencies between user-contributed R packages and identifies key packages according to social network analysis metrics. By Radhakrishnan Nagarajan and Marco Scutari.
Simulation functions to assess or explore the power of a dataset to estimate significant random effects (intercept or slope) in a mixed model. Based on the lme4 package. By Julien Martin.
Detailed source code parser, based on the standard R parser but organizing the information differently. By Romain François.
Fit pedigree-based mixed-effects models. By Douglas Bates and Ana Ines Vazquez.
Population and Evolutionary Genetics Analysis System. Provides functions for reading, writing, plotting, analysing, and manipulating allelic data, and for the analysis of population nucleotide sequences including coalescence analyses. By Emmanuel Paradis.
Estimation of penalized (conditional) densities. By Christian Schellhase.
Exact or Asymptotic permutation tests. By Michael Fay.
Precision of Geometric Sampling. Computes mean squared errors of stereological predictors. By Kien Kieu and Marianne Mora.
Computes the p-hull of a finite planar set, which is a generalization of the convex hull, X-Y hull and bounding rectangle. A fast, \(O(n \log n)\) Graham-scan based routine is used. By Marek Gagolewski.
Basic functions for phylogenetic analysis. Provides functions to read, write, manipulate, estimate, and summarize phylogenetic trees including species trees which contain not only the topology and branch lengths but also population sizes. The input/output functions can read tree files in which trees are presented in parenthetic format. The trees are read in as a string and then transformed to a matrix which describes the relationship of nodes and branch lengths. The nodes matrix provides an easy access for developers to further manipulate the tree, while the tree string provides interface with other phylogenetic R packages such as ape. The input/output functions can also be used to change the format of tree files between NEXUS and PHYLIP. Some basic functions have already been established in the package for manipulating trees such as deleting and swapping nodes, rooting and unrooting trees, changing the root of the tree. The package also includes functions for summarizing phylogenetic trees, calculating the coalescence time, population size, and tree distance, and to estimate the species tree from multiple gene trees. By Liang Liu.
Penalized Linear Unbiased Selection. Efficient procedures for fitting an entire regression sequences with different model types. By Cun-Hui Zhang and Ofer Melnik.
Partial Orders and Relations: functions for computing closures of relations. By Charles J. Geyer.
Markov Chain Monte Carlo for Potts Models. By Charles J. Geyer.
Projected Spatial Gaussian Process methods for package intamap. By Ben Ingram and Remi Barillec.
The William and Mary Bayesian Analysis Tool. By Karl Kuschner, Qian Si and William Cooke, College of William and Mary.
Rainbow plots, bagplots and boxplots for functional data. By Han Lin Shang and Rob J Hyndman.
Rank-hazard plots (Karvanen and Harrell, Statistics in Medicine, 2009) which visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval \([0,1]\). The relative hazard is plotted with respect to the reference hazard, which can be e.g. the hazard related to the median of the covariate. Transformation to scaled ranks allows plotting of covariates measured in different units in the same graph, which helps in the interpretation of the epidemiological relevance of the covariates. Rank-hazard plots show the difference of hazards between the extremes of the covariate values present in the data and can be used as a tool to check if the proportional hazards assumption leads to reasonable estimates for individuals with extreme covariate values. Alternative covariate definitions or different transformations applied to covariates can be also compared using rank-hazard plots. By Juha Karvanen.
Implementation of the “Ringscale” method as proposed in the student research project “Detection of faint companions around young stars in speckle patterns of VLT/NACO cube mode images by means of post-processing” at the Friedrich-Schiller-University of Jena. By Daniel Haase.
Analysis of Quaternary science data, including constrained clustering, WA, WAPLS, IKFA, MLRC and MAT transfer functions, and stratigraphic diagrams. By Steve Juggins.
R Image Processing and Analysis. Makes it possible to process and analyze RGB, LAN (multispectral) and AVIRIS (hyperspectral) images. By Talita Perciano, with contributions from Alejandro C Frery.
Regression Modeling Strategies: regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. A collection of about 225 functions that assist with and streamline modeling. Also contains functions for binary and ordinal logistic regression models and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. rms works with almost any regression model, but it was especially written to work with binary or ordinal logistic regression, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression. By Frank E Harrell Jr.
eXperimental eXtraneous eXtraordinary functionality for Robust Statistics. By Werner Stahel, Martin Mächler and potentially others.
Functions that can be called in conjunction with rpart for deriving a classification tree when the response to be predicted is ordinal. By Kellie J. Archer.
Random Return Variables. Currently provides limited support for formatting money. By Charlotte Maia.
Smooth Backfitting for additive models using Nadaraya-Watson estimator. By A. Arcagni, L. Bagnato.
Safe Binary Regression. Overloads the glm() function in the
stats package so that a test for the existence of the maximum
likelihood estimate is included in the fitting procedure for binary
regression models. By Kjell Konis.
A simple toolbar GUI for brushing RGL plots. Controls for simple brushing, highlighting, labeling, and mouseMode changes are provided by point-and-click rather than through the R terminal interface. By Justin Donaldson.
Semidiscrete Decomposition (SDD), which approximates a matrix as a weighted sum of outer products formed by vectors with entries constrained to be in the set \(\{-1, 0, 1\}\). By Tamara G. Kolda and Dianne P. O’Leary.
Synthesizing list of Differentially Expressed Features. Performs two tests to evaluate if experiments are associated and returns a list of interesting features common to all the experiments. By Alberto Cassese and Marta Blangiardo.
Routines for the seasonal analysis of health data, including regression models, time-stratified case-crossover, plotting functions and residual checks. By Adrian Barnett and Peter Baker.
A simple SMTP client which provides a portable solution for sending emails from within R. By Olaf Mersmann.
A general framework for statistical simulation. By Andreas Alfons.
Sequential (or Safe) Implementation of Monte Carlo tests with uniformly bounded resampling risk. Features efficient computation of p-values for Monte Carlo tests, e.g., bootstrap tests. By Axel Gandy.
Functions for the Skellam distribution, including: pmf, cdf, quantiles and random variates. By Jerry W. Lewis.
Algorithms to compute spherical \(k\)-means partitions. Features several methods, including a genetic and a simple fixed-point algorithm and an interface to the CLUTO vcluster program. By Kurt Hornik, Ingo Feinerer and Martin Kober.
Sparse Lightweight Arrays and Matrices. Data structures and algorithms for sparse arrays and matrices, based on index arrays and simple triplet representations, respectively. By Kurt Hornik, David Meyer, and Christian Buchta.
R related Search Engines. By Spencer Graves, Sundar Dorai-Raj, and Romain François.
Fit spatial CAR count regression models using MCMC. By Holger Schabenberger.
Fitting LMs and GLMs to large data sets by updating algorithms. By Marco ENEA.
Semiparametric efficient estimation for a two-sample treatment effect: performs estimation and testing of the treatment effect in a 2-group randomized clinical trial with a quantitative or dichotomous endpoint. The method is a special case of Robins, Rotnitzky, and Zhao (1994, JASA). It improves efficiency by leveraging baseline predictors of the endpoint. The method uses inverse probability weighting to provide unbiased estimation when the endpoint is missing at random. By Michal Juraska, with contributions from Peter B. Gilbert, Min Zhang, Marie Davidian, Anastasios A. Tsiatis and Xiaomin Lu.
Gapped and word-based string kernels for use with kernlab. By Martin Kober.
Visualize correlation in biological sequence data using sublogo dendrogram plots. By Toby Dylan Hocking.
Plots to help optimizing diabetes therapy. Provides a series of plots to integrate glucose levels, basal rate, activities, events and carbohydrate uptake on a single page in a humanely interpretable manner. It is meant for best-possibly representing the content of a well-curated diabetes diary of up to a week’s time or of up to seven comparable days, from which conclusions for adjusting the individual treatment shall be drawn. By Steffen
SciViews GUI API: dialog boxes. Rapidly construct dialog boxes for your GUI, including an automatic function assistant. By Philippe Grosjean.
SciViews GUI API: Sweave support functions. By Philippe Grosjean.
SciViews GUI API: tools, aimed at wrapping some of the functionalities of the packages tools, utils and codetools into a nicer format so that an IDE can use them. By Romain François.
SciViews GUI API: unit testing. A complete unit test system and functions to implement its GUI part. By Philippe Grosjean.
SciViews GUI API: widgets & windows. High level management of widgets, windows and other graphical resources. By Philippe Grosjean.
N-Gram based text categorization. By Kurt Hornik, Johannes Rauch, Christian Buchta, and Ingo Feinerer.
A device for R graphics output in PGF/TikZ format. Enables LaTeX-ready output from R graphics functions, with LaTeX mathematics that can be typeset directly into labels and annotations. Graphics produced this way can also be annotated with custom TikZ commands. By Charlie Sharpsteen and Cameron Bracken.
A plug-in for the tm text mining framework providing mail handling functionality. By Ingo Feinerer.
Analysis of Weighted, Two-mode, and Longitudinal networks. By Tore Opsahl.
Functions for calculating tolerance intervals. Tolerance limits provide the limits between which we can expect to find a specified proportion of a population with a given level of confidence. Provides functions for estimating tolerance limits for various distributions, and plotting tolerance limits of continuous random variables. By Derek S. Young.
Basic functions for the TractoR (tractography with R) bundle. Consists of functions for working with magnetic resonance images. Can read and write image files stored in Analyze, NIfTI and DICOM formats (DICOM support is read only), generate images for use as regions of interest, and manipulate and visualize images. By Jon Clayden.
Methods for Tree-based Local Adaptive Thresholding. By Ludger Evers and Tim Heaton.
Standard Backtests for Technical Trading Rules in Financial Data. Four core functions evaluate the efficacy of a technical trading rule: conditional return statistics, bootstrap resampling statistics, test for data snooping bias among parameter choices, and robustness of parameter choices. By David St John.
Some utility functions. By Thorn Thaler.
An R based Twitter client via an interface to the Twitter web API. By Jeff Gentry.
Jost Diversity Measures for Community Data. Computes diversity for community data sets using the methods outlined by Jost (2006, 2007), which offer the advantage of providing diversity numbers equivalents, independent alpha and beta diversities, and the ability to incorporate “order” as a continuous measure of the importance of rare species in the metrics. Computes alpha diversities, beta diversities, gamma diversities, and similarity indices. Confidence intervals for diversity measures are calculated using a bootstrap method described by Chao et al. (2008). By Noah Charney, Sydne Record.
Methods for variance partitioning for linear models and canonical correlation and methods for interpreting regression effects using beta weights, standardized beta weights, structure coefficients, and adjusted effect sizes. By Kim Nimon and J. Kyle Roberts.
Recommended bundle VR was unbundled into the recommended packages MASS, class, nnet and spatial, and moved to the Archive.
Bundle BACCO was unbundled into its packages approximator, calibrator and emulator, and moved to the Archive.
Bundle forecasting was unbundled into its packages Mcomp, expsmooth, fma and forecast, and moved to the Archive.
Bundle hoa was unbundled into its packages cond, csampling, marg and nlreg, and moved to the Archive.
Packages ARES, MDD, GeneNT, HTMLapplets, RiboSort, SLmisc, UNF, WaveCGH, WeedMap, agreement, bivpois, boost, clac, crq, ggplot, mclust02, norm, partsm, pwr, sma, supclust, uroot and verify were moved to the Archive.
Packages Multiclasstesting, Rlab, intcox, km.ci, mixlow, sdtalt and survBayes were resurrected from the Archive.
Package spectrino was removed from CRAN.
Package openNLPmodels was split into openNLPmodels.en and openNLPmodels.es.
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For attribution, please cite this work as
Hornik & Zeileis, "Changes on CRAN", The R Journal, 2009
BibTeX citation
@article{RJ-2009-2-cran,
author = {Hornik, Kurt and Zeileis, Achim},
title = {Changes on CRAN},
journal = {The R Journal},
year = {2009},
note = {https://rjournal.github.io/},
volume = {1},
issue = {2},
issn = {2073-4859},
pages = {80-94}
}