The ‘Changes on CRAN’ article from the 2009-1 issue.
Publishing a source package on CRAN now adds the publication date and the repository name (CRAN) to the package , and standardizes the license specification if possible.
The CRAN package web pages (remember that these gained persistent URLs of the form
http://CRAN.R-project.org/package=foo
in early 2008) have again been enhanced.
If available, package citation (from , suitably HTMLified), installation (from a top-level file), classification ( Classification/ACM, Classification/JEL and Classification/MSC fields with subject classifications using the Computing Classification System of the Association for Computing Machinery, the Journal of Economic Literature Classification System, and the Mathematics Subject Classification of the American Mathematical Society, respectively) and language ( Language field) information is now linked from the package web page.
In addition, the direct reverse dependencies of the package (i.e., the CRAN packages which depend on, import, suggest or enhance the package) are shown at the bottom of the package web page.
Standardizable license specifications are fully hyperlinked to the respective license texts.
All CRAN (HTML) web pages should now be valid XHTML.
Areal Interpolation for GIS data. Can be used to interpolate spatially associated data onto arbitrary target polygons which lack such data. Version 1.0 of the package is oriented toward convenient interpolation of specific US census data for California, but the tools provided should work for any combination of GIS data source and target polygon, provided appropriate care is taken. Future versions will be aimed at facilitating more general applications. By Benjamin P. Bryant and Anthony Westerling.
Functions for analyzing animal (including human) behavior data originating from a variety of sources, including functions to analyze time coded behaviors from CowLog (open source software for coding behaviors from digital video) data files and observation files with similar format. Other features include hourly, daily, weekly and monthly summaries of time coded data, analysis of RIC (roughage intake system, Insentec automation) data files and labeling measurement data according to behavioral observations for e.g. model building purposes. By Matti Pastell.
An integrated development toolbox for aquatic chemical model generation focused on (ocean) acidification and CO\(_2\) air-water exchange. Contains all elements necessary to model the pH, the related CO\(_2\) air-water exchange, as well as aquatic acid-base chemistry in general for an arbitrary marine, estuarine or freshwater system. Chemical batches can be modeled as well. In addition to the routines necessary to calculate desired information, AquaEnv also contains a suite of tools to visualize this. Furthermore, AquaEnv can not only be used to build dynamic models of aquatic systems, but can also serve as a simple desktop tool for the experimental aquatic chemist to generate and visualize all possible derived information from a set of measurements with one single easy to use R function. By Andreas F. Hofmann, Karline Soetaert, and Filip J. R. Meysman.
Bayesian association model for genomic data with missing covariates. Fits a linear mixed model where the covariates for the random effects have missing values. By V. Gopal, Z. Li and G. Casella.
Bayesian model averaging using Bayesian Adaptive Sampling. Performs BMA in 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 Liang et al. hyper-\(g\) priors. Other model selection criterion include AIC and BIC. Sampling probabilities may be updated based on the sampled models. By Merlise Clyde, with contributions from Michael Littman.
Block Gibbs Sampler with Incomplete Multinomial Distribution. Implements an efficient block Gibbs sampler with incomplete data from a multinomial distribution taking values from the \(k\) categories \(1,2,\ldots,k\), 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 \(A_1, A_2, \ldots, A_m\) of \(1,2,\ldots,k\) and the \(k\) categories are labeled such that only consecutive \(A\)’s may overlap. By Kwang Woo Ahn and Kung-Sik Chan.
Approximate and exact methods for pairwise binary Markov models. The exact method uses an implementation of the junction tree algorithm for binary graphical models. By Holger Hoefling.
Statistical methods for safety assessment in agricultural field trials. By Frank Schaarschmidt.
Functions for Bayesian Data Analysis, with data sets from the book “Bayesian Data Analysis” (second edition) by Gelman, Carlin, Stern and Rubin. (Not all data sets yet.) Compiled by Kjetil Halvorsen.
R Utilities Accompanying the BayesX software package for structured additive regression. Provides functionality for exploring and visualizing estimation results obtained with BayesX. Also provides functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX. By Thomas Kneib, Felix Heinzl, Andreas Brezger, and Daniel Sabanes Bove.
Performs Hansen’s (1995) Covariate-Augmented Dickey-Fuller (CADF) test. The \(p\)-values of the test are computed using a procedure proposed in Costantini, Lupi and Popp (2007), illustrated in Lupi (2008). By Claudio Lupi.
Calculation of the standard molal thermodynamic properties and chemical affinities of reactions between and among minerals, inorganic, organic, and/or biomolecular aqueous and crystalline species. Incorporation of an extensive database of thermodynamic properties of species and algorithms for computing the standard thermodynamic properties of neutral and ionized proteins from amino acid group contributions. Generation of chemical speciation and predominance diagrams for stable and metastable equilibrium in open systems as a function of temperature, pressure, and chemical activities or fugacities of basis species. By Jeffrey M. Dick.
Robust Bayesian priors in clinical trials. Fuquene, Cook, & Pericchi (2008) (http://www.bepress.com/mdandersonbiostat/paper44) make a comprehensive proposal putting forward robust, heavy-tailed priors over conjugate, light-tailed priors in Bayesian analysis. The behavior of Robust Bayesian methods is qualitatively different from Conjugate and short tailed Bayesian methods and arguably much more reasonable and acceptable to the practitioner and regulatory agencies. This package is useful to compute the distributions (prior, likelihood and posterior) and moments of the robust models: Cauchy/Binomial, Cauchy/Normal and Berger/Normal. Both Binomial and Normal Likelihoods can be handled. Furthermore, allows assessment of the hyper-parameters and posterior analysis. By A. Jairo and P. Fuquene.
Converts between the "Date" class and d/m/y for several calendars,
including Persian, Islamic, and Hebrew. By ‘Project Pluto’
(www.projectpluto.com/calendar)
and Thomas Lumley.
\(L_1\) version of Cramer-von Mises two sample tests. Contains functions for computing the Cramer-von Mises two sample test scores and the exact \(p\)-value(s) for given test score(s) under the assumption that the populations under comparison have the same probability distribution. The \(L_1\) Cramer-von Mises test, like its \(L_2\) counterpart, is distribution-free, but of less computational intensity. In certain cases, this version of Cramer-von Mises test is almost as powerful as its \(L_2\) counterpart. Simulation studies also show that it is more powerful than the Kolmogorov-Smirnov test in certain cases. By Yuanhui Xiao and Ye Cui.
Data Analysis and Knowledge Spaces. Functions and example data sets for the psychometric theory of knowledge spaces. Implements data analysis methods and procedures for simulating data and transforming different formulations in knowledge space theory. By Anatol Sargin and Ali Ünlü.
Spatial modeling for distance sampling data. Provides functions for fitting spatial models to line transect sampling data and to estimate abundance within a region. By Devin Johnson, Jeff Laake, and Jay VerHoef.
Functions for conducting and plotting Dunnett’s modified Tukey-Kramer pairwise multiple comparison test accounting for unequal variance and unequal sample sizes. By Matthew K. Lau.
Implements semiparametric estimation of copula models, and deals with structural break problems in copula modeling. By Andrew C. Chou and Jing Tao.
Estimate parameters for Jump Diffusion processes via the EM algorithm. The jump times are considered to be Bernoulli distributed with normally distributed jump sizes. By Matthias Graser.
Data sets from Nelson, Coffin and Copeland “Introductory Statistics for Engineering Experimentation” (Elsevier, 2003) with sample code. R port by Douglas Bates and Karen A. F. Copeland.
Exact computation of bivariate projection depth based on Fortran code. By Yijun Zuo and Xiangyang Ye.
FISH Based Normalization and copy number (CN) inference of SNP microarray data. Normalizes the data from a file containing the raw values of the SNP probes of microarray data by using the FISH probes and their corresponding CNs. By Adrian Andronache and Luca Agnelli.
Measuring functional diversity (FD) from multiple traits. Computes different multidimensional FD indices. Implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. By Etienne Lalibert�.
Fast Kalman Filter. A flexible implementation entirely written in C and fully relying on linear algebra subroutines contained in BLAS and LAPACK. Due to the speed of the filter, the fitting of high-dimensional linear state space models to large data sets becomes possible. Also contains a function for the visualization of the state vector and graphical diagnostics of the residuals. By David Lüthi and Philipp Erb.
Functions for selecting attributes from a given data set. Attribute subset selection is the process of identifying and removing as much of the irrelevant and redundant information as possible. By Piotr Romanski.
Multivariate exploration of a data table with factorial analysis and cluster methods. By Campo Elías Pardo and Pedro César del Campo, with contributions from Ivan Diaz and Mauricio Sadinle.
Infrastructure for extended formulas (e.g., with two parts on the right hand side). By Yves Croissant and Achim Zeileis.
Visualization technique for interpretation of high-throughput genetic or proteomic experiments. Integrates data sets derived from gene expression profiles with preexisting information from public databases such as KEGG pathway or Gene Ontology using Gene Set Enrichment Analysis (GSEA) and introduces a framework allowing to visualize biologically annotated data sets and relevant ratings of genes via a color gradient. By Sanjay Bhatikar and Kanika Arora.
Gauge R and R Confidence Intervals. Generates confidence intervals for the variance components in Gauge R and R data using ANOVA with the Satterthwaite approximation as well as the method of Generalized Inference. By Walter Resch, with information from “Measurement System Assessment Via Generalized Inference” by Michael Hamada and Sam Weerandi.
Executes functions on remote hosts, clusters or grids. In addition, users are provided with an interface to share variables and functions with other users. By Dennis Wegener, Malte Lohmeyer and Stefan Rüping.
A framework for writing map/reduce scripts for use in Hadoop Streaming. Also facilitates operating on data in a streaming fashion, without Hadoop. By David S. Rosenberg.
Simulate haplotypes through meioses. Allows specification of population parameters. By Albart Coster, John Bastiaansen.
Functions that read and solve Linear Inverse Model (food web, linear programming) problems, which find solutions to linear or quadratic functions: min or max \(f(x)\), where \(f(x) = ||Ax-b||^2\) or \(f(x) = \sum a_i x_i\) subject to equality constraints \(Ex=f\) and inequality constraints \(Gx \ge h\). Uses package limSolve. By Karline Soetaert and Dick van Oevelen.
Lambert \(W\) parameter estimation, plots, simulation. Lambert \(W\) random variables offer a new way of dealing with slightly skewed data. By Georg M. Goerg.
Functions for Learning Exploratory Data Analysis. By Jim Albert.
Calculation of the nonparametric Minimum Averaged Mean Squared Error (MAMSE) weights for univariate, right-censored or multivariate data. The MAMSE weights can be used in a weighted likelihood or to define a mixture of empirical distribution functions. By Jean-François Plante.
Tools for evaluating Monte Carlo Error. Has functions to estimate MC error in simulation studies, and functions helping with planning the number of replications required in a simulation study to reach a specified level of certainty. By Elizabeth Koehler and Sebastien Haneuse.
MCMC Generalized Linear Mixed Models. By Jarrod Hadfield.
Simulate data to study performance of clustering algorithms. By Volodymyr Melnykov, Wei-Chen Chen and Ranjan Maitra.
Create random forest and stochastic gradient boosting models, and apply them to GIS files to build detailed prediction maps. Validates the models with an independent test set, cross validation, or in the case of random forest Models, with out of bag (OOB) predictions on the training data. Creates graphs and tables of the model validation results, and applies the models to GIS files of predictors to create detailed prediction surfaces. Handles large predictor files for map making via chunking. By Elizabeth Freeman and Tracey Frescino.
Performance of \(N\)-ary classification testing. By C. Nardini and Y.-H. Liu.
NMR Spectroscopy. Developed to directly load spectra in the Bruker spectroscopy format. Displays the spectrum reference and manages basic operations such as setting the chemical shift of a certain compound (TSP or DSS) to 0 ppm, zero order and first order phase corrections, baseline adjustment and spectral area selection. By José L. Izquierdo.
Harvest metadata using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) version 2.0. By Kurt Hornik.
Oncogenetic trees: functions to construct and evaluate directed tree structures that model the process of occurrence of genetic alterations during carcinogenesis. By Aniko Szabo and Lisa Pappas.
Least squares, logistic, and Cox-regression with ordered predictors. In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators. By Kaspar Rufibach.
Compute least squares estimates of one bounded or two ordered antitonic regression curves. Consider the problem of estimating two antitonic regression curves \(f_1\) and \(f_2\) under the constraint that \(f_1 \ge f_2\). Given two sets of \(n\) data points \(g_1(x_1), \ldots, g_1(x_n)\) and \(g_2(x_1), \ldots, g_2(x_n)\) that are observed at (the same) deterministic points \(x_1, \ldots, x_n\), the estimates are obtained by minimizing the Least Squares criterion \(L(f_1, f_2) = \sum_{i=1}^n (g_1(x_i) - f_1(x_i))^2 w_1(x_i) + \sum_{i=1}^n (g_2(x_i) - f_2(x_i))^2 w_2(x_i)\) over the class of pairs of functions \((f_1, f_2)\) such that \(f_1\) and \(f_2\) are antitonic and \(f_1(x_i) \ge f_2(x_i)\) for all \(i = 1, \ldots, n\). The estimates are computed with a projected subgradient algorithm where the projection is calculated using a PAVA. By Fadoua Balabdaoui, Kaspar Rufibach, and Filippo Santambrogio.
Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in “A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis” by Witten, Tibshirani and Hastie (2009). By Daniela Witten and Rob Tibshirani.
Visualization using Eulerian tours and Hamiltonian decompositions. By C. B. Hurley and R. W. Oldford.
Pharmacovigilance signal detection methods extended to the multiple comparison setting. Functions can be used as standard R functions or through an user friendly interface (PhViD.gui). By Ismail Ahmed and Antoine Poncet.
Functionality for carrying out estimation with data collected using Respondent-Driven Sampling. Current functionality is extremely limited. By W. Whipple Neely.
R/EQS Interface. Contains the function run.eqs() which calls an
EQS script file, executes the EQS estimation, and, finally, imports
the results as R objects. These two steps can be performed
separately: call.eqs() calls and executes EQS, whereas
read.eqs() imports existing EQS outputs as objects into R. By
Patrick Mair and Eric Wu.
Heart rate variability analysis of ECG data. By M. Lado, A. Méndez, D. Olivieri, L. Rodrı́guez-Liñares, and X. Vila.
Distributions and statistics from Random Matrix Theory. Functions for working with the Tracy-Widom laws and other distributions related to the eigenvalues of large Wishart matrices. The tables for computing the Tracy-Widom densities and distribution functions were computed by Momar Dieng’s MATLAB package ‘RMLab’ (http://math.arizona.edu/~momar/research.htm). By Patrick O. Perry, with contributions from Iain M. Johnstone, Zongming Ma, and Morteza Shahram.
Optimally robust estimation using S4 classes and methods. Old version still needed for current versions of ROptRegTS and RobRex. By Matthias Kohl.
R-based Qualitative Data Analysis, currently only supporting plain text. By Huang Ronggui.
Alternative interfaces to RSiteSearch. By Sundar Dorai-Raj, Romain François and Spencer Graves.
Analysis of Spatially Distributed Data. Processes such data and is capable of error corrections and data visualization. Also provides a graphical user interface. By Jason C. Fisher.
An Rcmdr plug-in based on the survival package for easier student access to survival analysis. By Daniel Leucuta.
An Rcmdr plugin providing a GUI for the orloca package. By Fernando Fernández-Palacı́n and Manuel Muñoz-Márquez.
An Rcmdr plug-in based on the qcc package, providing an integration between the user and the tools of SPC. By Renan Cortes, with contributions of I. Prestes and Suzi Camey.
An Rcmdr plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests, diagnostics and graphs. By John Fox.
Rcpp R/C++ interface classes and examples. Maps data types between R
and C++, and includes support for R types real, integer, character,
vector, matrix, "Date", Date-Time (i.e., "POSIXct") at
microsecond resolution, data frame, and function. Also supports
calling R functions from C++. By Dirk Eddelbuettel, with
contributions by Simon Urbanek and David Reiss.
R interface to the CSDP semidefinite programming library. Installs version 6.0.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. By Hector Corrada Bravo (CSDP by Brian Borchers).
Overlays on Google map tiles in R. Serves two purposes: (i) provide a comfortable R interface to query the Google server for static maps, and (ii) use the map as a background image to overlay plots within R. By Markus Loecher.
Import data from a labkey database into an R data frame. By Valerie Obenchain.
\(R \times C\) Ecological Inference With optional incorporation of survey information. Fits the \(R \times C\) inference model described in Greiner and Quinn (2009). By D. James Greiner, Paul Baines, and Kevin M. Quinn.
Functions and data sets from “Sampling: Design and Analysis” S. Lohr (1999), Duxbury. By Tobias Verbeke.
Model Comparisons for structural equation modeling (SEM). Conduct tests of difference in fit for mean and covariance structure models as in SEM. By Roy Levy.
Spatial Graph based Clustering Summaries for spatial point patterns. By Tuomas Rajala.
Companion to “Statistical Modelling in R” by Aitkin et al (2009), Oxford University Press. By Murray Aitkin, Brian Francis, John Hinde, and Ross Darnell.
Multiple comparison test of means using the clustering method of Scott & Knott. By Enio Jelihovschi, José Cláudio Faria, and Sérgio Oliveira.
Simultaneous Comparisons (tests and confidence intervals) for general linear hypotheses when there is more than one primary response variable (endpoint). The procedure of Hasler (2009) is applied for differences or ratios of means of normally distributed data. The covariance matrices (containing the covariances between the endpoints) may be assumed to be equal or possibly unequal for the different groups. By Mario Hasler.
Discovering Genetic Ancestry Using Spectral Graph Theory. By Ann Lee, Diana Luca, Bert Klei, Bernie Devlin, and Kathryn Roeder.
Processing and statistical analysis of molecular fingerprint profiles. By Rory Michelland and Laurent Cauquil.
Estimation of the parameters of a spatio-temporal model using the EM algorithm, estimation of the parameter standard errors using a spatio-temporal parametric bootstrap, and spatial mapping. By Michela Cameletti.
Annotation and identification of the KEGG pathways. Facilitates sub-pathway annotation and identification of metabolic pathways. Also provides annotation and identification of entire pathways. By Chunquan Li.
Utilities for Sweave together with the TeX listings package. Features defining R/Rd as a listings “language” and including R/Rd source files (snippets) copied from R-Forge in its most recent version (or another URL) thereby avoiding inconsistencies between vignette and documented source code. By Peter Ruckdeschel.
Discrete triangular distributions, which complete the classical discrete distributions like binomial, Poisson and Negative binomial. By Tristan Senga Kiessé, Silvio S. Zocchi, and Célestin C. Kokonendji.
Time Series database interface (TSdbi) extensions for PostgreSQL. By Paul Gilbert.
Time Series database interface (TSdbi) extensions for fame. By Paul Gilbert.
Time Series database interface (TSdbi) extensions for
get.hist.quote to retrieve data from historical quote URLs. By
Paul Gilbert.
Time Series database interface (TSdbi) extensions for ODBC. By Paul Gilbert.
Foundations of inference in survey sampling. By Hugo Andrés Gutiérrez Rojas.
The Variance Gamma Distribution. Provides density, distribution and quantile functions. In addition, there are also functions for random number generation and fitting of the variance gamma to data. By David Scott and Christine Yang Dong.
Wavelet Changepoint Detection for Array CGH. Detects changepoints and finds gain/loss regions. By M. S. Islam and A. I. McLeod.
Cross-platform Perl based R function to create Excel 2003 () files from one or more data frames. Each data frame will be written to a separate named worksheet in the Excel spreadsheet. The worksheet name will be the name of the data frame it contains. By Marc Schwartz.
Makes time series cross-sectional forecasts with multiple cross-sections based on your assumptions given a variety of smoothing assumptions based on similarities among the levels or trends in the expected value of the dependent variable rather than the coefficients. By Federico Girosi, Gary King, and Elena Villalon.
Calculate the Generalized Discrimination Score, also known as Two Alternatives Forced Choice Score (2AFC). By Andreas Weigel.
Investigate agreement between two measurement methods using a simulation approach. By Fabio Frascati and Bruno Mario Cesana.
Computes the alpha-shape and alpha-convex hull of a given sample of points in the plane. These concepts generalize the definition of the convex hull of a finite set of points. The programming is based on the duality between the Voronoi diagram and Delaunay triangulation. Also includes a function that returns the Delaunay mesh of a given sample of points and its dual Voronoi diagram in one single object. By Beatriz Pateiro-López and Alberto Rodrı́guez-Casal.
Adaptive Management of Epidemiological Interventions. Provides a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic while simultaneously propagating uncertainty regarding underlying disease parameters through to the decision process via Bayesian posterior inference. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates. By Daniel Merl, Leah R. Johnson, Robert B. Gramacy, and Marc Mangel.
A framework for archetypal analysis supporting arbitrary problem solving mechanisms for the different conceptual parts of the algorithm. (Used as real-world test application for the Roxygen documentation system.) By Manuel J. A. Eugster.
An implementation of the model-based mining algorithm for NB-frequent itemsets presented in “A model-based frequency constraint for mining associations from transaction data” by M. Hahsler (Data Mining and Knowledge, 2006). In addition, implements an extension for NB-precise rules. By Michael Hahsler.
Export R objects to asciidoc or txt2tags. Comes with two drivers for Sweave. By David Hajage.
Aspects of Multivariables. Consists of two main functions:
corAspect() performs optimal scaling by maximizing an aspect
(i.e., target function such as sum of eigenvalues, sum of squared
correlations, squared multiple correlations, etc.) of the
corresponding correlation matrix. lineals() performs optimal
scaling by minimizing a non-correlational aspect based on pairwise
correlations and correlation ratios. The resulting correlation
matrix and category scores can be used for further multivariate
methods such as SEM. A platform including related
PsychoR packages is
provided on R-forge. By Jan de Leeuw and Patrick Mair.
Asymptotic testing. By the Cqls Team.
Performs automatic interpolation by automatically estimating the variogram and then calling gstat. By Paul Hiemstra.
A generalization of Fisher’s exact test that allows for structural zeros. By Robin K. S. Hankin (R) and Luke G. West (C++).
Bayesian analysis of array CGH data. A set of methods for Bayesian analysis of proportions of chromosomal changes within clinical sets. By Thomas Hardcastle.
Tests/Searches for significant clusters in genetic data. By V. Gopal, C. Fuentes and G. Casella.
Visualization via beanplots (univariate comparison graphs providing an alternative to boxplot, stripcharts and violin plots). By Peter Kampstra.
The sample size according to the Bethel’s procedure. By Michele De Meo.
Simulate correlated binary data using an algorithm based on Qaqish (2003). By Kunthel By and Bahjat F. Qaqish.
Generalized and classical blockmodeling of valued networks. In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithm) can be computed and partitioned matrices can be plotted. By Ales Ziberna.
Bootstrap for testing the hypothesis that the spectral densities of a number \(m \ge 2\) of not necessarily independent time series are equal. By Tatjana Kinsvater.
R graphics device targeting the HTML canvas element. Implements the CanvasRenderingContext2D JavaScript API. By Jeffrey Horner.
Combinatorially Complex Equilibrium Model Selection. Dissociation constants of quasi-equilibriums of enzymes and protein-ligand binding equilibriums in general are systematically scanned though possibilities of being infinity and/or equal to others. The automatically generated space of models is then fitted to data. By Tom Radivoyevitch.
Implements a methodology for using cell volume distributions to estimate cell growth rates and division times as described in “Cell Volume Distributions Reveal Cell Growth Rates and Division Times” by Michael Halter, John T. Elliott, Joseph B. Hubbard, Alessandro Tona and Anne L. Plant (Journal of Theoretical Biology). By Katharine M. Mullen, Michael Halter, John Lu and Nathan Dodder.
Functions for calculating clinical significance. By Jim Lemon.
A clustering method based on local shrinking. Contains functions for automatically estimating the number of clusters and obtaining the final cluster partition without any input parameter except the stopping rule for convergence. Also provides functions to evaluate and compare the performances of partitions of a data set both numerically and graphically. By Fang Chang, Vincent Carey, Weiliang Qiu, Ruben H. Zamar, Ross Lazarus, and Xiaogang Wang.
Generate high-dimensional correlated count random variables with a prespecified Pearson correlation. By Vinzenz Erhardt.
The (C)orrelated (RA)ndom (W)alk (L)ibrary of R functions for fitting continuous-time correlated random walk (CTCRW) models with time indexed covariates. The model is fit using the Kalman-Filter on a state space version of the continuous-time stochastic movement process. By Devin S. Johnson.
Depth functions methodology applied to multivariate analysis. Allows calculation of depth values and depth-based location estimators, and includes functions for drawing contour plots and perspective plots of depth functions. By Jean-Claude Massé and Jean-François Plante.
Implements the diffusion map method of data parametrization, including creation and visualization of diffusion map and course-graining using diffusion \(K\)-means. By Joseph Richards and Ann Lee.
Calculate SMR’s from population and case data. Works with regular data set files and shape files. By Patrick Brown.
Detection Localization Mapping for QTL. By Emma Huang and Andrew George.
Analysis of dyadic observational data. Contains original implementation of the Gottman-Murray marriage model and revisions using threshold autoregressive models. These programs allow modeling of dyadic observational data, such as interaction between husband and wife. Intended for researchers interested in modeling social behavior. By Tara Madhyastha and Ellen Hamaker.
Computes dynamical correlation estimates and percentile bootstrap confidence intervals for pairs of longitudinal responses, including consideration of lags and derivatives. By Joel Dubin, Dandi Qiao, and Hans-Georg Müller.
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. By Luke Miratrix.
Empirical likelihood test (two-sample, censored data). Calculates the \(p\)-value for a mean-type hypothesis (or multiple mean-type hypotheses) based on two samples. By William H. Barton, under the supervision of Mai Zhou.
Sweave-based automatic generation of standardized exams for large-lecture courses, with multiple-choice questions and arithmetic problems. By Achim Zeileis and Bettina Grün.
Facilitates the analysis of factorial experiments, including purely within-Ss designs (a.k.a. “repeated measures”), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package provide easy access to descriptive statistics, ANOVA, permutation tests, and visualization of results. By Michael A. Lawrence.
Implementation of the Fourier Amplitude Sensitivity Test (FAST), a method to determine global sensitivities of a model on parameter changes with relatively few model runs. By Dominik Reusser.
Functions and example data sets for Fechnerian scaling of discrete object sets. Computes Fechnerian distances among objects representing subjective dissimilarities, and other related information. By Thomas Kiefer and Ali Ünlü, based on original MATLAB source by Ehtibar N. Dzhafarov.
Fishery methods and models from Quinn and Deriso (1999), Haddon (2001), and literature. By Gary A. Nelson.
Exploratory analysis for pharmacometrics via the NONMEM platform. By Eun-Kyung Lee, Gyujeong Noh, and Hyeong-Seok Lim.
Help to fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. By Marie Laure Delignette-Muller, Régis Pouillot, and Jean-Baptiste Denis.
Implementation of optimal hierarchical clustering. Code by Fionn Murtagh, packaging by Peter Langfelder.
Greedy variable selection via forward, backward, and foba sparse learning algorithms for ridge regression, described in “Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations”. By Tong Zhang.
Compute the noncentrality parameter of the noncentral \(F\) distribution for given probabilities of type I and type II error and degrees of freedom of the numerator and the denominator. May be useful for computing minimal detectable differences for general ANOVA models. The program is documented in “On the computation of the noncentral \(F\) and noncentral beta distribution” by A. Baharev and S. Kemeny (Statistics and Computing, 2008, http://dx.doi.org/10.1007/s11222-008-9061-3). By Ali Baharev.
A collection of utility functions to expedite software development. By Brian Lee Yung Rowe.
Graphical Cluster Explorer. Visualize cluster results and investigate additional properties of clusters using interactive neighborhood graphs. By clicking on the node representing the cluster, information about the cluster is provided using additional graphics or summary statistics. For microarray data, tables with links to genetic databases like gene ontology can be created for each cluster. By Theresa Scharl, Ingo Voglhuber, and Friedrich Leisch.
Model diagnostics based on cumulative residuals. By Klaus K. Holst.
Generalized Orthogonal GARCH (GO-GARCH) models. By Bernhard Pfaff.
Provides R interfaces to a handful of common data-mining algorithms implemented in parallel using a mixture of nVidia’s CUDA language and cublas library. On a computer equipped with an nVidia GPU these functions may be substantially more efficient than native R routines. By Josh Buckner, with contributions from Justin Wilson.
Quality Control for Graphics in R. Provides functions to generate graphics files, compare them with “model” files, and report the results. By Stephen Gardiner and Paul Murrell.
Efficient algorithms for fitting the regularization path for linear or logistic regression models penalized by the group lasso, group bridge, or group MCP methods. The algorithm is based on the idea of a locally approximated coordinate descent. A technical report describing the methods and algorithms is available at http://www.stat.uiowa.edu/techrep/tr393.pdf. By Patrick Breheny.
Convenient R Functions. By Nathan Stephens and Vicky Yang.
Implements a data structure using R environments similar to hashes in Perl and dictionaries in Python but with a purposefully R flavor. By Christopher Brown.
Binning and plotting functions for hexagonal bins. Now uses and relies on grid graphics and formal (S4) classes and methods. By Dan Carr, ported by Nicholas Lewin-Koh and Martin Mächler.
A suite of routines for the hyperdirichlet distribution. By Robin K. S. Hankin.
Minimum distance estimation in an imprecise probability model. The imprecise probability model consists of upper coherent previsions whose credal sets are given by a finite number of constraints on the expectations. The parameter set is finite. The estimator chooses that parameter such that the empirical measure lies next to the corresponding credal set with respect to the total variation norm. By Robert Hable.
A collection of tools for calculating measures of influential data for mixed effects models. The basic rationale behind identifying influential data is that when iteratively single units are omitted from the data, models based on these data should not produce substantially different estimates. To standardize the assessment of how influential a (single group of) observation(s) is, several measures of influence are common practice. First, DFBETAS is a standardized measure of the absolute difference between the estimate with a particular case included and the estimate without that particular case. Second, Cook’s distance provides an overall measurement of the change in all parameter estimates, or a selection thereof. By Rense Nieuwenhuis, Ben Pelzer, and Manfred te Grotenhuis.
Analysis of introgression of genotypes between divergent, hybridizing lineages, including estimation of genomic clines from multi-locus genotype data and testing for deviations from neutral expectations. Functions are also provided for maximum likelihood estimation of molecular hybrid index and graphical analysis. By Zachariah Gompert and C. Alex Buerkle.
Uncertainty quantification and propagation in the framework of Dempster-Shafer Theory and imprecise probabilities. By Philipp Limbourg.
Utilities and probability distributions related to multidimensional person Item Response Models (IRT). By Gilles Raiche.
Active set and generalized PAVA for isotone optimization. Contains
two main functions: gpava() solves general isotone regression
problem using the pool-adjacent-violators algorithm (PAVA).
activeSet() provides a framework of active set methods for isotone
optimization problems. Various loss functions are pre-specified. By
Jan de Leeuw, Kurt Hornik, and Patrick Mair.
Algorithms to perform approximate joint diagonalization of a finite set of square matrices. Depending on the algorithm, an orthogonal or non-orthogonal diagonalizer is found. These algorithms are particularly useful in the context of blind source separation. By Cédric Gouy-Pailler.
Knowledge Space Theory: a set-theoretical framework which proposes mathematical formalisms to operationalize knowledge structures in a particular domain. Provides basic functionalities to generate, handle, and manipulate knowledge structures and knowledge spaces. By Christina Stahl and David Meyer.
Convert labels or tables to a dendrogram, offering a dendrogram representation of series of labels as especially needed in Markov Chain Monte Carlo clustering. By Vahid Partovi Nia, Anthony Davison and Arpit Chaudhary.
A graphical user interface for exploratory visualization. Primarily an interface to the Lattice graphics system, but also produces displays from the vcd package for categorical data. Given a multivariate data set (either a data frame or a table), it attempts to produce useful displays based on the properties of the data. The displays can be customized by editing the calls used to generate them. By Felix Andrews.
Learn Chain graphs (and as a special case, Bayesian networks) via Decomposition. By Zongming Ma and Xiangrui Meng.
Latent Class Discriminant Analysis: local discrimination via latent class models. By Michael Buecker.
Linear Mixed-Effects Models with Censored Responses. Fits a linear mixed-effects model in the formulation described in Laird and Ware (1982) but allowing for censored normal responses. In this version, the within group errors are assumed independent and identically distributed. By Florin Vaida and Lin Liu.
Computes model II simple linear regression using ordinary least squares (OLS), major axis (MA), standard major axis (SMA), and ranged major axis (RMA). By Pierre Legendre.
Functions for regional frequency analysis using the methods in “Regional frequency analysis: an approach based on L-moments” by J. R. M. Hosking and J. R. Wallis (1997). By J. R. M. Hosking.
Simplicial, Mahalanobis and ellipsoid local and global depth. By Claudio Agostinelli and Mario Romanazzi.
Recursive partitioning of longitudinal data using mixed-effects models. By Sam Stewart and Mohamed Abdolell.
A flexible mapReduce framework for parallel computation. Provides (a) a pure R implementation, (b) a syntax following the mapReduce paper, and (c) a flexible and parallelizable back end. By Christopher Brown.
Calculates statistical power of detecting associated markers based on one of the model selection strategies: marginal selection, exhaustive search, or forward selection. With assumed genetic effects (including interaction effect), allele frequencies, noise level, sample size, number of genotyped markers, and control level (i.e., number of markers/models intended to select), this package provides fast and accurate consultation on power of different model selection strategies. It helps researchers to decide a more efficient way for marker detection in genome-wide association studies. By Zheyang Wu and Hongyu Zhao.
Various distributions and utilities to build and study two-dimensional Monte Carlo simulations. By Régis Pouillot, Marie Laure Delignette-Muller and Jean-Baptiste Denis.
A collection of functions that allows the reenactment of the R programs used in the book “EnteR Monte Carlo Methods” without further programming. Programs can also be modified by the user to conduct one’s own simulations. By Christian P. Robert.
Medication Adherence: commonly used definitions. By Xiangyang Ye.
Meta-analysis for MicroArrays. Combines either \(p\)-values or modified effect sizes from different studies to find differentially expressed genes. By Guillemette Marot.
Meta-analysis of correlation coefficients. Implement the DerSimonian-Laird (DSL) and Olkin-Pratt (OP) meta-analytical approaches with correlation coefficients as effect sizes. By Etienne Laliberté.
Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences. The times must be equidistant but missing values of the observables are allowed. The observables are allowed to be multivariate. It is possible to have multiple sequences of data. Estimation of the parameters of the models are made using EM-algorithms. Computationally demanding parts of the algorithm are implemented in C for performance. Finally, the package is designed to allow users to specify their own emission distributions, giving flexibility in the type of data that may be analyzed. By Jared O’Connell and Søren Højsgaard.
Implements entropy optimization distribution under specified constraints. Also offers an R interface to the MinxEnt and MaxEnt distributions. By Senay Asma.
Contains a mixture of statistical methods including MCMC methods to analyze normal mixtures. By Arnošt Komárek.
Mixture Rasch Models with JMLE. Estimates mixture Rasch models, including the dichotomous Rasch model, the rating scale model, and the partial credit model. By John T. Willse.
Provides an implementation of the Modified Maximum Contrast Method.
The current version features function mmcm.resamp which gives
\(p\)-values for the modified maximum contrast statistics by using a
resampling based procedure. By Kengo Nagashima and Yasunori Sato.
Provides a way of running parallel computations in R on machines with multiple cores or CPUs, and methods for results collection. Jobs can share the entire initial workspace. By Simon Urbanek.
MUlti-Stage COnvex Relaxation. Solves a number of convex/non-convex sparse regularization formulations for regression or binary classification using the algorithm described in “Multi-stage Convex Relaxation for Learning with Sparse Regularization”. Loss functions include least squares, logistic, truncated ls/huber. By Tong Zhang.
Multivariate interactive visualization. By Moritz Gschwandtner.
Generates correlated binary data based on the method of Emrich and Piedmonte (1991). By Kunthel By and Bahjat Qaqish.
Solve a system of non linear equations using a Broyden or a Newton method with a choice of global strategies such as linesearch and trust region. There are options for using a numerical or an analytical Jacobian and fixed or automatic scaling of parameters. By Berend Hasselman.
A set of operators for common tasks such as regex manipulation. By Romain François.
Orthogonal \(B\)-spline basis functions. Represents the basis functions via a simple matrix formulation that facilitates taking integrals, derivatives, and orthogonalizing the basis functions. By Andrew Redd.
Estimates the matrix of partial correlations based on different regularized regression methods: lasso, adaptive lasso, PLS, and Ridge Regression. By Nicole Krämer and Juliane Schäfer.
Partitioning using deletion, substitution, and addition moves, a novel tool for generating a piecewise constant estimation list of increasingly complex predictors based on an intensive and comprehensive search over the entire covariate space. By Annette Molinaro, Karen Lostrito, and Steve Weston.
Pedigree related functions. By Albart Coster.
Feature selection SVM using penalty functions. The smoothly clipped absolute deviation (SCAD) and \(L_1\) norm penalties are available up to now. By Natalia Becker, Wiebke Werft, and Axel Benner.
Proportional Hazards Mixed-effects Model (PHMM). Fits PHMMs using an EM algorithm using Markov Chain Monte Carlo at the E-step. By Michael Donohue and Ronghui Xu.
Tools for project planning. Supports the creation of burndown charts. By Dan Kelley.
Plot principal component histograms around a bivariate scatter plot. By Stephen Milborrow.
Partial Least Squares (PLS) methods with emphasis on structural equation models with latent variables. By Gaston Sanchez.
Functions for one-dimension jump position detection using one-sided polynomial kernel detectors (polynomial order from 0 to 3). By Zhihua Su.
Routines for power calculations for interactions for GWAS. By Charles Kooperberg.
Functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks. Also includes functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials. By Weiliang Qiu, Jorge Chavarro, Ross Lazarus, Bernard Rosner, and Jing Ma.
Utilities to fit paired comparison models for preferences. Generates design matrix for analyzing real paired comparisons and derived paired comparison data (Likert type items, ratings or rankings) using a log-linear approach. Fits log-linear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). By Reinhold Hatzinger.
Functions and data for the forthcoming book “A Primer of Ecology with R”. Functions are primarily for systems of ordinary differential equations, difference equations, and eigenanalysis and projection of demographic matrices; data are for examples. By M. Henry H. Stevens.
Spearman’s rank correlation test with improved accuracy. By Petr Savicky.
Datasets for the book “A guide to QTL Mapping with R/qtl”. By Karl W. Broman.
Interface to access the SymPy computer algebra system running under Jython from R. By G. Grothendieck.
Benchmarking routine for R. Inspired by the Perl module Benchmark,
and intended to facilitate benchmarking of arbitrary R code.
Consists of just one function, benchmark, which is a simple
wrapper around system.time. Given a specification of the
benchmarking process (counts of replications, evaluation
environment) and an arbitrary number of expressions, benchmark
evaluates each of the expressions in the specified environment,
replicating the evaluation as many times as specified, and returning
the results conveniently wrapped into a data frame. By Wacek
Kusnierczyk.
Provides the CDK libraries for use with R. To make use of the CDK within R, using the rcdk package, which exposes functionality in a more idiomatic way, is suggested; however, it is also possible to directly interact with CDK using rJava. By Rajarshi Guha.
Functions for simulating future forest conditions using the CONIFERS growth model. By Jeff D. Hamann and Martin W. Ritchie.
Item analysis with alpha standard error and principal axis factoring for continuous variable scales. By Michael Chajewski.
Regularized Multivariate Regression for Identifying Master Predictors. Developed for fitting multivariate response regression models under the high-dimension-low-sample-size setting. By Jie Peng, Pei Wang, and Ji Zhu.
Generate LaTeX tables of descriptive statistics. Especially helpful when writing data analysis reports using Sweave. By Kaspar Rufibach.
Functions for beginners and social sciences students or researchers. Currently includes functions for cross-tabulation, weighting, results export, and maps plotting. Documentation and help pages are written in French. By Julien Barnier.
Random number generator WELL19937a by F. Panneton, P. L’Ecuyer and M. Matsumoto with initialization using MRG32k5a by P. L’Ecuyer. WELL19937a is of similar type as Mersenne Twister and has the same period. It is slightly slower than Mersenne Twister, but has better equidistribution and “bit-mixing” properties and faster recovery from states with prevailing zeros than Mersenne Twister. The output function may be set to provide numbers with 53 or 32 random bits. By Petr Savicky.
Methods for the imputation of compositional data including robust methods, (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, and (robust) Anderson-Darling normality tests for compositional data. By Matthias Templ, Karel Hron, and Peter Filzmoser.
Functions to generate response-surface designs, fit first- and second-order response-surface models, make contour plots, obtain the path of steepest ascent, and do canonical analysis. By Russell V. Lenth.
Convenient representation and manipulation of realizations of Random Time Variables. By Charlotte Maia.
Statistical analysis of multi-process random effect response data by providing SABRE functionality from within R. Responses can take the form of binary, ordinal, count and linear recurrent events. Response sequences can be of different types. Such multi-process data is common in many research areas, e.g., the analysis of work and life histories. May be used for analyzing longitudinal data sets surveys either with recurrent information collected over time or with a clustered sampling scheme. By R. Crouchley.
Implements the Scout method for regression, described in “Covariance-regularized regression and classification for high-dimensional problems” by Witten and Tibshirani (2008, Journal of the Royal Statistical Society, Series B). By Daniela M. Witten and Robert Tibshirani.
Shrinkage Discriminant Analysis and feature selection. Provides an efficient framework for high-dimensional linear and diagonal discriminant analysis with feature selection. The classifier is trained using Stein-type shrinkage estimators and features are ranked using correlation-adjusted \(t\)-scores. Feature selection is controlled using false non-discovery rates or higher criticism scores. By Miika Ahdesmäki and Korbinian Strimmer.
Statistical disclosure control for tabular data. By Bernhard Meindl.
Scenario Discovery Tools to support robust decision making. Currently only implements a modified version of the Patient Rule Induction Method. By Benjamin P. Bryant.
Calculates the gain from a model selection, using part of Tallis’ (1961) algorithm. By Xuefei Mi, Xuefei Mi, and Friedrich Utz.
Statistical Inference for MOdular NEtworks (SIMoNe). Iteratively combines edge estimation and node classification on the basis of a mixture model for edge weight distributions. Edge inference may be managed via two alternative methods: GLasso and the Meinshausen-Bühlmann approach. Node Classification is managed by MixNet, a mixture model for random graphs. By Christophe Ambroise, Julien Chiquet, Gilles Grasseau, and Alexander Adam Smith.
Stable Isotope Sourcing using Sampling: source partitioning using stable isotopes. By Erik Barry Erhardt.
Sparse linear discriminant analysis for gaussians and mixture of gaussians models. By Line Clemmensen, with contributions by Max Kuhn.
Functionals and indices for measuring segregation in multitype spatial point patterns with graph based neighborhood description. Included indices: Mingling, Shannon, Simpson; functionals: Mingling, Shannon, Simpson, ISAR; neighborhoods: Geometric, \(k\)-nearest neighbors, Gabriel, Delauney. By Tuomas Rajala.
SPatial COverage SAmpling and random sampling from compact geographical strata created by \(k\)-means. By D. J. J. Walvoort, D. J. Brus, and J. J. de Gruijter.
Sparse Partial Least Squares (SPLS) regression. By Dongjun Chung, Hyonho Chun, and Sunduz Keles.
Functions and data sets from “An Introduction to Scientific Programming and Simulation, Using R”. By Owen Jones, Robert Maillardet, Andrew Robinson, Olga Borovkova, and Steven Carnie.
Functions to assess and compare the performance of risk prediction (survival) models. By Benjamin Haibe-Kains, Christos Sotiriou, and Gianluca Bontempi.
Provides various portfolio optimization strategies including random matrix theory and shrinkage estimators. Portfolio optimization typically requires an estimate of a covariance matrix of asset returns. There are many approaches for constructing such a covariance matrix, some using the sample covariance matrix as a starting point. This package provides implementations for two such methods: random matrix theory and shrinkage estimation. Each method attempts to clean or remove noise related to the sampling process from the sample covariance matrix. By Brian Lee Yung Rowe.
Helpful tools for data processing and visualization of results of the hydrological model WASIM-ETH. By Dominik Reusser, Till Francke.
Chronological and calendarical objects for Rmetrics environment for teaching “Financial Engineering and Computational Finance”. By Diethelm Würtz and Yohan Chalabi.
Financial time series objects for the Rmetrics environment for teaching “Financial Engineering and Computational Finance”. By Diethelm Würtz and Yohan Chalabi.
Trimmed maximum likelihood estimation: a general framework for robust fitting of finite mixture models. Parameter estimation is performed using the EM algorithm. By P. Neytchev, P. Filzmoser, R. Patnaik, A. Eisl and R. Boubela.
Computes truncated multivariate normal probabilities, quantiles and densities, including one-dimensional marginal densities. By Stefan Wilhelm.
Trunctated normal distribution. By Heike Trautmann, Detlef Steuer, and Olaf Mersmann.
Least angle regression for time series analysis. By Sarah Gelper.
An algorithm for general-purpose unconstrained non-linear
optimization. The algorithm is of quasi-Newton type with BFGS
updating of the inverse Hessian and soft line search with a trust
region type monitoring of the input to the line search algorithm.
The interface of
ucminf is designed
for easy interchange with optim. By Hans Bruun Nielsen and Stig
Bousgaard Mortensen.
Univarate shrinkage prediction for survival analysis using in the Cox model. Especially useful for high-dimensional data, including microarray data. By Rob Tibshirani.
Manipulation, normalization, and plotting of phonetic and sociophonetic vowel formant data. Used as the backend for the NORM website. By Tyler Kendall and Erik R. Thomas.
Translates numerical data into 3-dimensional representations like 3D scatter plots, meshes, bar charts and graphs in the Virtual Reality Markup Language (VRML). By Enrico Glaab.
Helpful tools for data processing and visualization of results of the hydrological model WASIM-ETH. By Dominik Reusser, Till Francke.
A wrapper function and GUI for the X12 binaries under Windows. By Alexander Kowarik.
Support for xterm256 escape sequences, enabling R functions to take advantage of foreground color, background color, in capable terminals. By Romain François.
Yet Another Canonical Correlation Analysis package. Provides an alternative canonical correlation/redundancy analysis function, with associated print, plot, and summary methods. A method for generating helio plots is also included. By Carter T. Butts.
Zhang and Yue-Pilon trends package. Contains an efficient implementation of Sen’s slope method plus implementation of Xuebin Zhang’s (Zhang, 1999) and Yue-Pilon’s (Yue, 2002) prewhitening approaches to determining trends in climate data. By David Bronaugh and Arelia Werner for the Pacific Climate Impacts Consortium.
Package Matrix is recommended for R \(\ge\) 2.9.0.
Bundle SciViews was moved to the Archive and is replaced by its unbundled packages (svGUI, svIDE, svMisc, and svSocket).
Packages DDHFm, GeneTS, LLN, ProbForecastGOP, ProbeR, Rlab, Rmdr, Rmetrics, SparseLogReg, caretLSF, caretNWS, classPP, femmeR, gllm, knnFinder, intcox, mlica, mlmmm, mota, pARtial, rggm, sdtalt, survBayes, and waveclock were moved to the Archive.
Frontend gnomeGUI was moved to the Archive.
AIGIS, Animal, AquaEnv, BAMD, BAS, BGSIMD, BMN, BSagri, BayesDA, BayesX, CADFtest, CHNOSZ, ClinicalRobustPriors, ConvCalendar, CvM2SL1Test, DAKS, DSpat, DTK, Depela, EMJumpDiffusion, EngrExpt, ExPD2D, FBN, FD, FKF, FSelector, FactoClass, Formula, GFMaps, GRRGI, GridR, HadoopStreaming, HaploSim, LIM, limSolve, LambertW, LearnEDA, MAMSE, MCE, MCMCglmm, MixSim, ModelMap, Multiclasstesting, NMRS, OAIHarvester, Oncotree, OrdFacReg, OrdMonReg, PMA, PairViz, PhViD, RDS, REQS, RHRV, RMTstat, ROptEstOld, ROptRegTS, RobRex, RQDA, RSiteSearch, RSurvey, RcmdrPlugin.SurvivalT, Rcmdr, survival, RcmdrPlugin.orloca, orloca, RcmdrPlugin.qcc, qcc, RcmdrPlugin.survival, Rcpp, Rcsdp, RgoogleMaps, Rlabkey, RxCEcolInf, SDaA, SEMModComp, SGCS, SMIR, ScottKnott, SimComp, SpectralGEM, StatFingerprints, Stem, SubpathwayMiner, SweaveListingUtils, TRIANG, TSPostgreSQL, TSfame, TShistQuote, TSodbc, TeachingSampling, VarianceGamma, WaveCGH, WriteXLS, YourCast, afc, agreement, alphahull, amei, archetypes, arulesNBMiner, ascii, aspect, PsychoR, asympTest, automap, gstat, aylmer, bayesCGH, bayesclust, beanplot, bethel, binarySimCLF, blockmodeling, bootspecdens, canvas, ccems, cellVolumeDist, clinsig, clues, corcounts, crawl, depth, diffusionMap, diseasemapping, dlmap, dyad, dynCorr, elec, emplik2, exams, ez, fast, fechner, fishmethods, fit4NM, fitdistrplus, flashClust, foba, fpow, futile, gcExplorer, gof, gogarch, gputools, graphicsQC, grpreg, hacks, hash, hexbin, grid, hyperdirichlet, imprProbEst, influence.ME, introgress, ipptoolbox, irtProb, isotone, jointDiag, kst, labeltodendro, latticist, vcd, lcd, lcda, lmec, lmodel2, lmomRFA, localdepth, longRPart, mapReduce, markerSearchPower, mc2d, mcsm, medAdherence, metaMA, metacor, mhsmm, minxent, mixAK, mixRasch, mmcm, multicore, muscor, mvgraph, mvtBinaryEP, nleqslv, operators, orthogonalsplinebasis, parcor, partDSA, pedigree, penalizedSVM, phmm, plan, plotpc, plspm, polydect, powerGWASinteraction, powerSurvEpi, prefmod, primer, pspearman, qtlbook, rSymPy, rbenchmark, rcdklibs, rcdk, rJava, rconifers, rela, remMap, reporttools, rgrs, rngwell19937, robCompositions, rsm, rtv, sabreR, scout, sda, sdcTable, sdtoolkit, selectiongain, simone, sisus, sparseLDA, spatialsegregation, spcosa, spls, spuRs, survcomp, tawny, wasim, timeDate, timeSeries, tlemix, tmvtnorm, truncnorm, tslars, ucminf, uniCox, vowels, vrmlgen, x12, xterm256, yacca, zyp, Matrix, SciViews, svGUI, svIDE, svMisc, svSocket, DDHFm, GeneTS, LLN, ProbForecastGOP, ProbeR, Rlab, Rmdr, Rmetrics, SparseLogReg, caretLSF, caretNWS, classPP, femmeR, gllm, knnFinder, intcox, mlica, mlmmm, mota, pARtial, rggm, sdtalt, survBayes, waveclock, gnomeGUI
ActuarialScience, Agriculture, Bayesian, ChemPhys, ClinicalTrials, Cluster, Distributions, Econometrics, Environmetrics, Epidemiology, ExperimentalDesign, ExtremeValue, Finance, HighPerformanceComputing, MachineLearning, MetaAnalysis, MissingData, MixedModels, NumericalMathematics, OfficialStatistics, Omics, Optimization, Phylogenetics, Psychometrics, ReproducibleResearch, Robust, Spatial, SpatioTemporal, Survival, TeachingStatistics, TimeSeries, Tracking, WebTechnologies
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For attribution, please cite this work as
Hornik, "Changes on CRAN", The R Journal, 2009
BibTeX citation
@article{RJ-2009-1-cran,
author = {Hornik, Kurt},
title = {Changes on CRAN},
journal = {The R Journal},
year = {2009},
note = {https://rjournal.github.io/},
volume = {1},
issue = {1},
issn = {2073-4859},
pages = {77-90}
}