Debian Science Statistics packages

ESS ("Emacs Speaks Statistics") is a GNU Emacs and XEmacs mode for interactive statistical programming and data analysis. Languages supported are the S family (S 3/4, S-PLUS 3/4/5/6/7, and R), SAS, XLispStat, Stata, BUGS and Julia.

ESS grew out of the desire for bug fixes and extensions to S-mode and SAS-mode as well as a consistent union of their features in one package.

The 'r' (aka 'littler') program provides a leightweight 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.

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.

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

OpenTURNS is a large project with more than 300 C++ classes which uses well known and supported software such as R for the statistical methods and BLAS/LAPACK for the linear algebra.

This package provides a textual user interface that gives access to all the functionalities provided by the OpenTURNS 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 2 version.

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.

Python-stats provides a collection of statistical functions for Python. These functions can be applied to Python lists, Python tuples, and Numeric arrays. Most common statistical functions such as mean, standard deviation, median, mode, skewness, kurtosis, etc., are provided, along with functions for statistical hypothesis testing, analysis of variance, and for computing probability distribution functions.

statsmodels Python 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 six 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 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".

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.

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.

URL: http://statwww.epfl.ch/davison/BMA/library.html

This CRAN package provides a Cairo/GTK2 graphics device for GNU R that 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 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.

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.

This is a set of routines for GNU R that implement Imai and Lu's parametric and nonparametric Bayesian ecological inference algorithms using Markov chain Monte Carlo estimation. Ecological inference is a statistical technique designed to recover individual-level information from aggregate-level data.

The suggested r-cran-mcmcpack package includes other EI estimators that may be useful alternatives to those included in this package.

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/

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

fEcofin provides economic and financial time series and data sets.

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.

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 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 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.

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 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.

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.

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.

This is a set of routines for GNU R that implement various statistical and econometric models using Markov chain Monte Carlo (MCMC) estimation, which allows "solving" models that would otherwise be intractable with traditional techniques, particularly problems in Bayesian statistics (where one or more "priors" are used as part of the estimation procedure, instead of an assumption of ignorance about the "true" point estimates), although MCMC can also be used to solve frequentist statistical problems with uninformative priors. MCMC techniques are also preferable over direct estimation in the presence of missing data.

Currently implemented are a number of ecological inference (EI) routines (for estimating individual-level attributes or behavior from aggregate data, such as electoral returns or census results), as well as models for traditional linear panel and cross-sectional data, some visualization routines for EI diagnostics, two item-response theory (or ideal-point estimation) models, metric, ordinal, and mixed-response factor analysis, and models for Gaussian (linear) and Poisson regression, logistic regression (or logit), and binary and ordinal-response probit models.

The suggested packages (r-cran-bayesm, -eco, and -mnp) contain additional models that may also be useful for those interested in this package.

This package provides functions for generalised additive models (GAMs) and other generalized ridge regression problems with multiple smoothing parameter selection by GCV or UBRE. It includes an implementation (not a clone) of gam().

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 a collection of miscellaneous 3d plots, including rgl-based isosurfaces:

 contour3d     Uses rgl to render isosurfaces, or three-dimensional
               contours, computed by the marching cubes algorithm.
 image3d       Crude 3d analog of image() using rgl to plot points on a
               three dimensional grid representing values in a three
               dimensional array. Assumes high values are inside and
               uses alpha blending to make outside points more
               transparent.
 lines3d       Draws connected line segments in an rgl window.
 parametric3d  Plots a two-parameter surface in three dimensions in
               rgl.  Based on Mathematica's Param3D
 slices3d      Uses tkrplot to create an interactive slice view of three or
               four dimensional volume data, such as MRI data.

MNP is an R package that fits Bayesian Multinomial Probit (MNP) models via Markov chain Monte Carlo (MCMC). Along with the standard multinomial probit model, it can also fit models with different choice sets for each observation and complete or partial ordering of all the available alternatives. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2004).

Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates. A variety of observation schemes are supported, including processes observed at arbitrary times, completely-observed processes, and censored states.

This package provides functions to perform multiple comparison tests and simultaneous confidence intervals in one-way layout testing setups.

This package provides a collection of functions to compute the multivariate Normal and T distributions.

This package provides functions to fit and compare linear and non-linear mixed-effects models.

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 consists of R functions developed at the Political Science Computational Laboratory at Stanford University; it currently includes models for count data (hurdle regression and zero-inflated negative binomial and Poisson models), ordinal probit and logit (also available in MASS via polr), and an item-response theory model. Also included are Vuong tests for non-nested hypothesis testing and convenience functions for handling roll-call vote matrices.

This package includes several procedures used in psychometrics and scaling, including:

• Cohen's Kappa and weighted Kappa (two-rater agreement tests)
• Cronbach's Alpha (an item-reliability test)
• Focused Principal Components Analysis
• Intraclass correlation coefficients
• Light's Kappa (an N-rater agreement test)
• Screeplots and graphical representations of principal components

R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTLs) in experimental crosses. It is implemented as an add-on-package for the freely available and widely used statistical language/software R (see http://www.r-project.org).

The development of this software as an add-on to R allows one to take advantage of the basic mathematical and statistical functions, and powerful graphics capabilities, that are provided with R. Further, the user will benefit by the seamless integration of the QTL mapping software into a general statistical analysis program. The goal is to make complex QTL mapping methods widely accessible and allow users to focus on modeling rather than computing.

A key component of computational methods for QTL mapping is the hidden Markov model (HMM) technology for dealing with missing genotype data. The main HMM algorithms were implemented, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses.

The current version of R/qtl includes facilities for estimating genetic maps, identifying genotyping errors, and performing single-QTL genome scans and two-QTL, two-dimensional genome scans, by interval mapping (with the EM algorithm), Haley-Knott regression, and multiple imputation. All of this may be done in the presence of covariates (such as sex, age or treatment). One may also fit higher-order QTL models by multiple imputation.

This CRAN package provides functions and documentation for solving quadratic programming problems in GNU R.

This package provides a simple GUI, based on the GNU interface to Tcl/Tk, that is suitable for teaching introductory statistics.

This package provides functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model.

This Debian package contains the R package for access to the ggobi data visualization system for high-dimensional data from within GNU R.

See http://www.ggobi.org/rggobi for rggobi, http://www.ggobi.org/ggobi and http://www.r-project.org for GNU R.

This package provides functions implementing a new graphics device suitable for visualisation of GNU R objects in three dimensions using the OpenGL libraries.

This package provides facilities in the S language (i.e. the language which GNU R provides) for programming graphical interfaces using Gtk, the GNOME GUI toolkit.

This package extends and updates the previous r-omegahat-rgtk package to the 2.8 release of the Gtk libraries.

This CRAN package provides an interface to the MPI (Message-Passing Interface) API. It also provides an interactive R slave environment in which distributed statistical computing can be carried out.

Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. rms is a collection of 229 functions that assist with and streamline modeling. It 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.

See Frank Harrell (2001), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/Rrms.

The package implements access to MySQL databases via the DBI interface of R.

This CRAN package provides access to any Open DataBase Connectivity (ODBC) accessible database.

The package should be platform independent and provide access to any database for which a driver exists. It has been tested with MySQL and PostgreSQL on both Linux and Windows (and to those DBMSs on Linux hosts from R under Windows), Microsoft Access, SQL Server and Excel spreadsheets (read-only), and users have reported success with connections to Oracle and DBase.

Usage is covered in the R Data Import/Export manual (available via the r-doc-pdf, r-doc-html and r-doc-info packages).

This package provides functions to recursive partioning and regression trees.

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 (of the S-Plus original): http://www.mayo.edu/hsr/Sfunc.html

This CRAN package provides an interface to the PVM (Parallel Virtual Machine) API, along with documentation and examples.

This CRAN package provides an interface for GNU R to the QuantLib library for modeling, trading, and risk management of financial assets.

This package provide Rserve, a headless server for GNU R. Rserve acts as a socket server (TCP/IP or local sockets) which allows binary requests to be sent to R. Every connection has a separate workspace and working directory. Client-side implementations are available for popular languages such as C/C++ and Java, allowing any application to use facilities of R without the need of linking to R code. Rserve supports remote connection, user authentication and file transfer. A simple R client is included in this package as well.

URL: http://www.rosuda.org/Rserve/

This package provides an interface to the SPRNG (Scalable Parallel Random Number Generator) library API, along with examples and documentation.

This package contains model-robust standard error estimators for time series and longitudinal data.

This CRAN package provides functions and documentation for kernel smoothing methods for GNU R. The functions are described in the book "Applied Smoothing Techniques for Data Analysis: The kernel approach with S-Plus illustrations" by Adrian W. Bowman & Adelchi Azzalini, Oxford UP, 1997.

This package provide the 'simple network of workstations' (SNOW), an easy-to-use system for parallel computing in GNU R.

This package functions for testing, dating and monitoring of structural change in linear regression relationships. The strucchange package features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM, recursive/moving estimates) and F statistics, respectively. It is possible to monitor incoming data online using fluctuation processes.

Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals. Emphasis is always given to methods for visualizing the data.

This package provides functions and datasets for survival analysis: descriptive statistics, two-sample tests, parametric accelerated failure models, Cox model. Delayed entry (truncation) is allowed for all models; interval censoring for parametric models.

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 is a set of demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming.

This CRAN package provides a Tk graphics device which enables embedding of GNU R plot() results inside a Tk widget.

This CRAN package provides additional time-series analysis functions, as well as several computational finance routines.

Visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. The package was inspired by the book "Visualizing Categorical Data" by Michael Friendly.

This package implements a variety of functions for estimating vector generalized additive models (VGAMs), as well as vector generalized linear models (VGLMs), reduced-rank VGLMs, and quadratic reduced-rank VGLMs.

Specific models that can be fit include a wide variety of categorical-response models (with nominal and ordinal dependent variables), bivariate binary-response models (bivariate logit/probit), and seemingly-unrelated regressions. The package also includes a number of standard and non-standard distribution functions of use to statisticians.

This package provides functions and datasets from the book by William N. Venables and Brian D. Ripley, "Modern Applied Statistics with S", 4th ed., Springer, 2002. The VR package contains four individuals packages: MASS, class, nnet and spatial.

These packages are now shipped in individual packages upon which this package depends. Once all reverse Depends: on r-cran-vr are resolved you will be able to remove the metapackage.

This package provides facilities for the R language to

• parse XML files, URLs and strings, using either the DOM (Document Object Model)/tree-based approach, or the event-driven SAX (Simple API for XML) mechanism;
• generate XML content to buffers, files, URLs, and internal XML trees;
• read DTDs as R objects.

With thousands of contributors who have written hundreds of packaged routines, R can deal with nearly any statistical problem. Although this high level of participation may be its greatest strength, the enormous diversity in approaches to statistical inference covered by R often results in a virtual babel of competing functions and inconsistent syntax.

To address these problems from a common perspective, the upstream authors have created Zelig, a single, easy-to-use program, with a unified framework and syntax, that can estimate, help interpret, and present the results of a large range of statistical methods. It literally is "everyone's statistical software" because Zelig uses R code from many researchers. They also hope it will become "everyone's statistical software" for applications, and they have designed it so that anyone can use it or add their methods to it. Zelig comes with detailed, self-contained documentation that minimizes startup costs for Zelig and R, automates graphics and summaries for all models, and, with only three simple commands required, generally makes the power of R accessible for all users. Zelig also works well for teaching, and is designed so that scholars can use the same program they use for their research.

This package contains an S4 class with methods for totally ordered indexed observations such as irregular time series.

R is a system for statistical computation and graphics. It consists of a language plus a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files.

The design of R has been heavily influenced by two existing languages: Becker, Chambers & Wilks' S and Sussman's Scheme. Whereas the resulting language is very similar in appearance to S, the underlying implementation and semantics are derived from Scheme.

The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. Most of the user-visible functions in R are written in R. It is possible for the user to interface to procedures written in the C, C++, or FORTRAN languages for efficiency, and many of R's core functions do so. The R distribution contains functionality for a large number of statistical procedures and underlying applied math computations. There is also a large set of functions which provide a flexible graphical environment for creating various kinds of data presentations.

Additionally, several thousand extension "packages" are available from CRAN, the Comprehensive R Archive Network, many also as Debian packages, named 'r-cran-'.

This Debian package is now a metapackage that depends on a set of packages that are recommended by the upstream R core team as part of a complete R distribution, and distributed along with the source of R itself, as well as directly via the CRAN network of mirrors. This set comprises the following packages (listed in their upstream names):

• KernSmooth: Functions for kernel smoothing for Wand & Jones (1995)
• Matrix: Classes and methods for dense and sparse matrices and operations on them using Lapack and SuiteSparse
• MASS, class, nnet and spatial: packages from Venables and Ripley, `Modern Applied Statistics with S' (4th edition).
• boot: Bootstrap R (S-Plus) Functions from the book "Bootstrap Methods and Their Applications" by A.C. Davison and D.V. Hinkley (1997).
• cluster: Functions for clustering (by Rousseeuw et al.)
• codetools: Code analysis tools for R
• foreign: Read data stored by Minitab, S, SAS, SPSS, Stata, ...
• lattice: Implementation of Trellis (R) graphics
• mgcv: Multiple smoothing parameter estimation and GAMs by GCV
• nlme: Linear and nonlinear mixed effects models
• rpart: Recursive partitioning and regression trees
• survival: Survival analysis, including penalised likelihood.

RKWard aims to become an easy to use, transparent frontend to R, a powerful system for statistical computation and graphics. Besides a convenient GUI for the most important statistical functions, future versions will also provide seamless integration with an office-suite.

Evaluation of control charts by means of the zero-state, steady-state ARL (Average Run Length). Setting up control charts for given in-control ARL and plotting of the related figures. The control charts under consideration are one- and two-sided EWMA and CUSUM charts for monitoring the mean of normally distributed independent data. Now, the ARL calculation of EWMA-S^2 control charts is added. Other charts and parameters are in preparation. Further SPC areas will be covered as well (sampling plans, capability indices ...).

This metapackage will install Debian Science packages related to Mathematics. You might also be interested in the field::mathematics debtag and, depending on your focus, in the education-mathematics metapackage.

A GNU R package for analysing spatial data, mainly Spatial Point Patterns, including multitype/marked points and spatial covariates, in any two-dimensional spatial region. Contains functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, and pixel images. Point process models can be fitted to point pattern data. Cluster type models are fitted by the method of minimum contrast. Very general Gibbs point process models can be fitted to point pattern data using a function ppm similar to lm or glm. Models may include dependence on covariates, interpoint interaction and dependence on marks. Fitted models can be simulated automatically. Also provides facilities for formal inference (such as chi-squared tests) and model diagnostics (including simulation envelopes, residuals, residual plots and Q-Q plots).

Set of function for sparse matrix algebra. Differences with SparseM/Matrix are: (1) onlyone sparse matrix format is supported (essentially) , (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within GMRF. (4) S3 and S4 like-"compatible" ... and it is fast.

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

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

fCalendar provides basic time and date manipulations, holiday calculators and other tools. Its code was previously provided within the fBasics package from which it has been split off.