Debian Science Statistics packages

The Apophenia Statistical C Library is an open source C library for working with data sets and statistical models. It provides functions on the same level as those of the typical stats packages (such as OLS, probit, or singular value decomposition) but gives the user more flexibility to be creative in model-building.

Apophenia is meant to scale well, to comfortably work with gigabyte data sets, million-step simulations, or computationally-intensive agent-based models.

Apophenia builds upon the GNU Scientific and SQLite libraries, MySQL/mariaDB is also supported. The core functions are written in C, but experience has shown them to be easy to bind in Python, Julia, Perl, Ruby, etc. The source code is distributed under the GNU General Public License version 2 (GPL2).

This package provides several command line utility examples.

GNU Datamash is a command-line program which performs basic numeric, textual and statistical operations on input textual data files. It is designed to be portable and reliable, and aid researchers to easily automate analysis pipelines, without writing code or even short scripts.

Emacs Speaks Statistics (ESS) is an add-on package for GNU Emacs. It is designed to support editing of scripts and interaction with various statistical analysis programs such as R, S, S-Plus, SAS, Stata, BUGS/JAGS and Julia.

It provides the following features:

• Editing source code (R, S, S-plus, SAS, BUGS/JAGS, Stata, Julia)
• Syntactic indentation and highlighting of source code
• Partial evaluation of code
• Loading and error-checking of code
• Source code revision maintenance
• Batch execution (SAS, BUGS/JAGS)
• Use of imenu to provide links to appropriate functions
• Interacting with the process (R, S, S-plus, SAS, Stata, Julia)
• Command-line editing
• Searchable Command history
• Command-line completion of R, S, S-plus object names and file names
• Quick access to object lists and search lists
• Transcript recording
• Interface to the help system
• Transcript manipulation (R, S, S-plus, Stata)
• Recording and saving transcript files
• Manipulating and editing saved transcripts
• Re-evaluating commands from transcript files
• Interaction with Help Pages and other Documentation (R)
• Fast Navigation
• Sending Examples to running ESS process
• Fast Transfer to Further Help Pages
• Help File Editing (R)
• Syntactic indentation and highlighting of source code
• Sending Examples to running ESS process
• Previewing

This is a library written in Java, providing probability density function, cumulative distribution function, quantiles and random variate generation for several common statistical distributions.

The 'r' (aka 'littler') program provides a lightweight binary wrapper around the GNU R language and environment for statistical computing and graphics.

While R can be used in batch mode, the r binary adds full support for both 'shebang'-style scripting (i.e. using a hashmark-exclamation-path expression as the first line in scripts) as well as command-line use in standard Unix pipelines. In other words, r provides the R language without the environment.

This virtual package ensures that 'r-cran-littler' is installed. Once that is done, this 'littler' package can be removed.

Persalys is a graphical user interface for OpenTURNS, dedicated to the treatment of uncertainty and the management of variabilities. The software is a tool between the computer simulation, probabilistic analyses and the data sciences. The interface is available in French or in English.

Persalys allows one to:

• create mathematical models: analytical, coupling with an external model (finite elements, ...), FMU;
• analyse the variability of one's parameters thanks to many methods and visualisation tools;
• statistically analyse one's measuring data, infer probability distributions or create metamodels.

Psignifit allows fitting of psychometric functions to datasets while maintaining full control over a large number of parameters. Data can either be read from text files or passed through a pipe.

Psignifit performs the calculation of confidence intervals as well as goodness-of-fit tests.

This is the command line version.

Please note: This is the legacy version 2.x of psignifit.

PSPP is a program for statistical analysis of sampled data. It is a free replacement for the proprietary program SPSS.

PSPP supports T-tests, ANOVA, GLM, factor analysis, non-parametric tests, and other statistical features. PSPP produces statistical reports in plain text, PDF, PostScript, CSV, HTML, SVG, and OpenDocument formats.

PSPP has both text-based and graphical user interfaces. The PSPP user interface has been translated into a number of languages.

BayesPy provides tools for Bayesian inference with Python. The user constructs a model as a Bayesian network, observes data and runs posterior inference. The goal is to provide a tool which is efficient, flexible and extendable enough for expert use but also accessible for more casual users.

This package provides the modules for Python 3

OpenTURNS is a powerful and generic tool to treat and quantify uncertainties in numerical simulations in design, optimization and control. It allows both sensitivity and reliability analysis studies:

• defining the outputs of interest and decision criterion;
• quantify and model the source of uncertainties;
• propagate uncertainties and/or analyse sensitivity and
• rank the sources of uncertainty

Targeted users are all engineers who want to introduce the probabilistic dimension in their so far deterministic studies.

This package provides the Python3 bindings to the library.

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data:

• Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
• Ordered and unordered (not necessarily fixed-frequency) time series data.
• Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
• Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure

This package contains the Python 3 version.

Persalys is a graphical user interface for OpenTURNS, dedicated to the treatment of uncertainty and the management of variabilities. The software is a tool between the computer simulation, probabilistic analyses and the data sciences. The interface is available in French or in English.

Persalys allows one to:

• create mathematical models: analytical, coupling with an external model (finite elements, ...), FMU;
• analyse the variability of one's parameters thanks to many methods and visualisation tools;
• statistically analyse one's measuring data, infer probability distributions or create metamodels.

This package contains the Python3 bindings.

SciPy supplements the popular NumPy module (python-numpy package), gathering a variety of high level science and engineering modules together as a single package.

SciPy is a set of Open Source scientific and numeric tools for Python. It currently supports special functions, integration, ordinary differential equation (ODE) solvers, gradient optimization, genetic algorithms, parallel programming tools, an expression-to-C++ compiler for fast execution, and others.

statsmodels Python3 module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for several distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem.

This package provides the abind function, a generalization of the cbind and rbind function in the R base package. The abind function takes a sequence of vectors, matrices, or arrays and produces a single array of the same or higher dimension.

This package provides the functions ace() and avas() for GNU R which can be used to select regression transformations.

The r-cran-afex package is a GNU R package providing convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated- measures), or mixed between-within (i.e., split- plot) ANOVAs for data in long format (i.e., one observation per row), potentially aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex uses type 3 sums of squares as default (imitating commercial statistical software).

This package was born to release the TAO robust neural network algorithm to the R users. It has grown and can be of interest for the users wanting to implement their own training algorithms as well as for those others whose needs lye only in the "user space".

R functions for processing 'lm', 'glm', 'svy.glm', 'merMod' and 'polr' outputs.

r-cran-bayesfactor is a GNU R package providing a suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one- way designs, general ANOVA designs, and linear regression.

The bayesm package covers many important models used in marketing and micro-econometrics applications. The package includes:

• Bayes Regression (univariate or multivariate dep var)
• Multinomial Logit (MNL) and Multinomial Probit (MNP)
• Multivariate Probit,
• Multivariate Mixtures of Normals
• Hierarchical Linear Models with normal prior and covariates
• Hierarchical Multinomial Logits with mixture of normals prior and covariates
• Bayesian analysis of choice-based conjoint data
• Bayesian treatment of linear instrumental variables models
• Analyis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)).

For further reference, consult the authors' book, Bayesian Statistics and Marketing by Allenby, McCulloch and Rossi.

r-cran-bms is a GNU Package for Bayesian model averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, flexible and hyper-g priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches.

This package provides functions and datasets from the book "Bootstrap Methods and Their Applications" by A. C. Davison and D. V. Hinkley (1997, CUP). It was witten by Angelo Canty, and ported to R by Brian Ripley.

This package is part of the set of packages that are 'recommended' by R Core and shipped with upstream source releases of R itself.

Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.

r-cran-ca is a GNU R package for computation and visualization of simple, multiple and joint correspondence analysis

This CRAN package provides a Cairo/GTK2 graphics device for GNU R that, in addition to standalone functionality, can be used to create devices as embedded components in a GUI using a Gtk drawing area widget, e.g. using RGtk2.

This package accompanies J. Fox, An R and S-PLUS Companion to Applied Regression, Sage, 2002. The package contains mostly functions for applied regression, linear models, and generalized linear models, with an emphasis on regression diagnostics, particularly graphical diagnostic methods. There are also some utility functions.

This GNU R package provides misc functions for training and plotting classification and regression models.

This package contains function for chronologically ordered objects to handle dates and time in GNU R.

This package provides functions and datasets for cluster analysis originally written by Peter Rousseeuw, Anja Struyf and Mia Hubert.

This package is part of the set of packages that are 'recommended' by R Core and shipped with upstream source releases of R itself.

This package provides output analysis and diagnostics for Markov Chain Monte Carlo simulations and estimations within R. It also includes some graphical analysis routines and a facility for importing output from BUGS (Bayes Using Gibbs Sampling), a popular tool for running MCMC simulations.

The related r-cran-mcmcpack package includes MCMC estimators for some common models in the social sciences.

This package provides functions for code analysis for R.

This package is part of the set of packages that are 'recommended' by R Core and shipped with upstream source releases of R itself.

The r-cran-coin package is a GNU R package providing conditional inference procedures for the general independence problem including two- sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems.

The contfrac package is a GNU R package providing various utilities for evaluating continued fractions.

GNU R package which implements a James-Stein-type shrinkage estimator for the covariance matrix, with separate shrinkage for variances and correlations. The approach is both computationally as well as statistically very efficient, it is applicable to "small n, large p" data, and always returns a positive definite and well-conditioned covariance matrix. In addition to inferring the covariance matrix the package also provides shrinkage estimators for partial correlations and partial variances. The inverse of the covariance and correlation matrix can be efficiently computed, as well as any arbitrary power of the shrinkage correlation matrix. Furthermore, functions are available for fast singular value decomposition, for computing the pseudoinverse, and for checking the rank and positive definiteness of a matrix.

This CRAN package provides functions for handling dates, along with examples and documentation.

Note that this is a legacy package providing functionality that had been absent from R. However, more recent versions of R have more powerful date and datetime functionality in the base package, i.e. r-base-core under Debian.

A generic database interface (DBI) definition for communication between R and relational database management systems -- similar to Perl's DBI system. All classes in this package are virtual and need to be extended by the various R/DBMS implementations as e.g. RMySQL.

This GNU R package calculates the Delaunay triangulation and the Dirichlet or Voronoi tessellation (with respect to the entire plane) of a planar point set. This package is used in Spatial Point Pattern analysis, model-fitting and simulation as it is done for instance in the package r-cran-spatstat.

The deSolve package is a GNU R package providing functions that solve initial value problems of a system of first-order ordinary differential equations (ODE), of partial differential equations (PDE), of differential algebraic equations (DAE), and of delay differential equations.

The ecodist package provides dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data.

This package provides functions for 'effect' displays: Graphical and tabular effect displays, e.g., of interactions, for linear generalized linear, multinomial-logit, and proportional-odds logit models.

An earlier version was described in the article by Fox (2003) in the Journal of Statistical Software available at http://www.jstatsoft.org/v08/i15/

The elliptic package is a GNU R package providing a suite of elliptic and related functions including Weierstrass and Jacobi forms. Also includes various tools for manipulating and visualizing complex functions.

The energy package implements E-statistics (energy) tests and statistics for comparing distributions: multivariate normality, multivariate distance components and k-sample test for equal distributions, hierarchical clustering by e-distances, multivariate independence tests, distance correlation, goodness-of-fit tests. Energy-statistics concept based on a generalization of Newton's potential energy is due to Gabor J. Szekely.

eRm fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM). Missing values are allowed in the data matrix. Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, itemfit and personfit statistics including infit and outfit measures, various ICC and related plots, automated stepwise item elimination, simulation module for various binary data matrices. An eRm platform is provided at R-forge (see URL).

The estimability package is a GNU R package providing tools for determining estimability of linear functions of regression coefficients, and 'epredict' methods that handle non-estimable cases correctly.

The matrix exponential GNU R package enables computation of the matrix exponential and related quantities.

Fastcluster implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines: “linkage” in the SciPy package “scipy.cluster.hierarchy”, “hclust” in R's “stats” package, and the “flashClust” package. It provides the same functionality with the benefit of a much faster implementation. Moreover, there are memory-saving routines for clustering of vector data, which go beyond what the existing packages provide. For information on how to install the Python files, see the file INSTALL in the source distribution.

This package of functions for financial engineering and computational finance is part of Rmetrics, a collection of packages written and compiled by Diethelm Wuertz.

fBasics provides basic statistical tests, distributions and other tools used by many of the Rmetrics packages.

This package of functions for financial engineering and computational finance is part of Rmetrics, a collection of packages written and compiled by Diethelm Wuertz.

fCopulae provides functions for (nonlinear) dependence structure modelling.

This package of functions for financial engineering and computational finance is part of Rmetrics, a collection of packages written and compiled by Diethelm Wuertz.

fExtremes provides functions to analyze extreme values.

This package of functions for financial engineering and computational finance is part of Rmetrics, a collection of packages written and compiled by Diethelm Wuertz.

fMultivar provides multivariate analysis for financial time series.

This package of functions for financial engineering and computational finance is part of Rmetrics, a collection of packages written and compiled by Diethelm Wuertz.

fOptions provides functions to price and hedge plain and exotic options on one or several assets.

This package provides functions for reading and writing data stored by statistical packages such as Minitab, S, SAS, SPSS, Stata, ...

This package is part of the set of packages that are 'recommended' by R Core and shipped with upstream source releases of R itself.

This package of functions for financial engineering and computational finance is part of Rmetrics, a collection of packages written and compiled by Diethelm Wuertz.

fPortfolio provides functions for portfolio and asset price modeling, drawdown statistics, value-at-risk and Markowitz portfolio construction.

Functions for fitting and working with generalized additive models, as described in chapter 7 of “Statistical Models in S” (Chambers and Hastie (eds), 1991), and “Generalized Additive Models” (Hastie and Tibshirani, 1990).

This package of R programming tools was written by Gregory Warnes with code contributions by Ben Bolker and Thomas Lumley.

This package used to be part of the gregmisc bundle packaged for Debian as r-cran-gregmisc.

Generalized estimating equations solver for parameters in mean, scale, and correlation structures, through mean link, scale link, and correlation link. Can also handle clustered categorical responses.

ggplot2 combines the advantages of both base and lattice graphics. Conditioning and shared axes are handled automatically, and you can still build up a plot step by step from multiple data sources. It also implements a sophisticated multidimensional conditioning system and a consistent interface to map data to aesthetic attributes.

Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.

This package of R programming tools for model fitting was written by Gregory Warnes with code contributions by Ben Bolker and Thomas Lumley.

This package used to be part of the gregmisc bundle packaged for Debian as r-cran-gregmisc.

This R package provides Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit for continuous univariate distributions, using efficient algorithms.

This package of R programming tools for plotting data was written by Gregory Warnes with code contributions by Ben Bolker and Thomas Lumley.

This package used to be part of the gregmisc bundle packaged for Debian as r-cran-gregmisc.

This package of miscellaneous functions for GNU R that written by Gregory Warnes with code contributions by Ben Bolker, Bendix Carstensen, Don MacQueen, William Venables, Marc Schwartz, Ben Bolker, Ian Wilson, Kjetil Halvorsen, Arni Magnusson, Lodewijk Bonebakker, and others.

Most of the function in the gregmisc library fall into six general areas: permutations and combinations, tools for linear models, plots, data manipulation, and fixed or extended versions of existing functions.

Note that as of version 2.0.8, gregmisc's code is now provided by the new packages gdata, gmodels, gplots and gtools which contain all the code formerly provided by gregmisc. Unless any of your R code still loads the gregmisc package directly, and you thus need it to load the component packages, you can remove this package.

This package of R programming tools was written by Gregory Warnes with code contributions by Ben Bolker and Thomas Lumley.

This package used to be part of the gregmisc bundle packaged for Debian as r-cran-gregmisc.

This package provides the functions hdf5save and hdf5load which allow saving and loading of R variables in the Hierarchical Data Format (HDF) format. Lists, strings, vectors, matrices and higher-dimensional arrays are supported.

The Hmisc library contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, translating SAS datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of S objects to LaTeX code, recoding variables, and bootstrap repeated measures analysis.

The r-cran-hypergeo package is a GNU R package providing the Gaussian hypergeometric function for complex numbers.

This package contains an S4 class for handling irregular time series

This package provides functions for kernel smoothing and density estimation corresponding to M.P. Wand and M.C. Jones, "Kernel Smoothing", 1995.

This package is part of the set of packages that are 'recommended' by R Core and shipped with upstream source releases of R itself.

URL: http://www.maths.unsw.edu.au/~wand

This package provides functions for 'Trellis' graphics -- a framework for data visualization developed at the Bell Labs by Rick Becker, Bill Cleveland et al, extending ideas presented in Bill Cleveland's 1993 book 'Visualizing Data'.

This package is part of the set of packages that are 'recommended' by R Core and shipped with upstream source releases of R itself.

This CRAN package provides generic functions and standard methods for trellis-based displays.

This package supplies latent variable analysis for R. One can use lavaan to estimate a large variety of multivariate statistical models, including path analysis, confirmatory factor analysis, structural equation modeling and growth curve models.

This package provides a number of methods for creating and augmenting Latin Hypercube Samples.

Latin hypercube sampling (LHS) is a statistical method for generating a sample of plausible collections of parameter values from a multidimensional distribution. The sampling method is often used to construct computer experiments.

This CRAN package provides S4 classes and methods for fitting and examining linear mixed effects models (also called multilevel models, panel data models, and several other names) and generalized linear mixed effects models.

This package provides a collection of tests, data sets and examples for diagnostic checking in linear regression models.

The logspline package provides routines for logspline density estimation based on the algorithm from Stone et. al. (1997).

The r-cran-lsmeans package is a GNU R package to obtain least-squares means for many linear, generalized linear, and mixed models. Additionally it can compute contrasts or linear functions of least- squares means, and comparisons of slopes.

This package provides facilities for easily producing high-resolution maps based on data sets in the GNU R statistical computing environment. It includes high-resolution map data for the entire world, along with maps of China (including provincial boundaries) and a higher-resolution map of New Zealand and a number of nearby Pacific islands.

The suggested r-cran-mapproj package adds facilities for calculating geographic projections, which are used by mapmakers to compensate for the inaccuracies inherent in projecting a spheroid's surface onto a two-dimensional plane.

This package provides facilities for easily producing cartographic projections of maps based on data sets in the GNU R statistical computing environment. Projections are used by mapmakers to compensate for the inaccuracies inherent in projecting a spheroid's surface onto a two-dimensional plane.

This package provides facilities for easily producing maps based on data sets in the GNU R statistical computing environment.

The r-cran-maps package includes map data for the United States (including state and county-level maps), New Zealand, and a world map; additional maps (including a higher-resolution world map) are available in the suggested r-cran-mapdata package.

The suggested r-cran-mapproj package adds facilities for calculating geographic projections, which are used by mapmakers to compensate for the inaccuracies inherent in projecting a spheroid's surface onto a two-dimensional plane.

This GNU R package provides functions with example data for graphing, pruning, and mapping models from hierarchical clustering, and classification and regression trees.

The MASS package provides functions and datasets to support the book 'Modern Applied Statistics with S' (4th edition) by W.N. Venables and B.D. Ripley. The following URL provides more details about the book: URL: http://www.stats.ox.ac.uk/pub/MASS4

MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2004) for improving parametric statistical models by preprocessing data with nonparametric matching methods.

MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly within Zelig.

This CRAN package provides S4 classes and methods for numerical linear algebra using dense or sparse matrices. The sparse matrix implementation uses code from the LDL sparse matrix package and code from the Metis package of partitioning algorithms.