63 packages on CRAN
1 packages on GitHub
The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, JSS), and the book by Eddelbuettel (2013, Springer); see 'citation("Rcpp")' for details on these last two.
Implementation of a function 'digest()' for the creation of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32', 'xxhash' and 'murmurhash' algorithms) permitting easy comparison of R language objects, as well as a function 'hmac()' to create hash-based message authentication code. Please note that this package is not meant to be deployed for cryptographic purposes for which more comprehensive (and widely tested) libraries such as 'OpenSSL' should be used.
Boost provides free peer-reviewed portable C++ source libraries. A large part of Boost is provided as C++ template code which is resolved entirely at compile-time without linking. This package aims to provide the most useful subset of Boost libraries for template use among CRAN package. By placing these libraries in this package, we offer a more efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. As of release 1.62.0-1, the following Boost libraries are included: 'algorithm' 'any' 'atomic' 'bimap' 'bind' 'circular_buffer' 'concept' 'config' 'container' 'date'_'time' 'detail' 'dynamic_bitset' 'exception' 'filesystem' 'flyweight' 'foreach' 'functional' 'fusion' 'geometry' 'graph' 'heap' 'icl' 'integer' 'interprocess' 'intrusive' 'io' 'iostreams' 'iterator' 'math' 'move' 'mpl' 'multiprcecision' 'numeric' 'pending' 'phoenix' 'preprocessor' 'propery_tree' 'random' 'range' 'scope_exit' 'smart_ptr' 'spirit' 'tuple' 'type_traits' 'typeof' 'unordered' 'utility' 'uuid'.
R and 'Eigen' integration using 'Rcpp'. 'Eigen' is a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms. It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems. Its performance on many algorithms is comparable with some of the best implementations based on 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header files from the 'Eigen' C++ template library (currently version 3.3.3). Thus users do not need to install 'Eigen' itself in order to use 'RcppEigen'. Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License (version 2); earlier version were licensed under the GNU LGPL version 3 or later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Note that Armadillo requires a fairly recent compiler; for the g++ family at least version 4.6.* is required.
Functionality to dynamically define R functions and S4 methods with inlined C, C++ or Fortran code supporting .C and .Call calling conventions.
Creation and use of R Repositories via helper functions to insert packages into a repository, and to add repository information to the current R session. Two primary types of repositories are support: gh-pages at GitHub, as well as local repositories on either the same machine or a local network. Drat is a recursive acronym: Drat R Archive Template.
Full 64-bit resolution date and time support with resolution up to nanosecond granularity is provided, with easy transition to and from the standard 'POSIXct' type.
Provides a function kitten() which creates cute little packages which pass R package checks. This sets it apart from package.skeleton() which it calls, and which leaves imperfect files behind. As this is not exactly helpful for beginners, kitten() offers an alternative.
'Rcpp' Access to the 'CCTZ' timezone library is provided. 'CCTZ' is a C++ library for translating between absolute and civil times using the rules of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License, is included in this package. See <https://github.com/google/cctz> for more details.
Convert input in any one of character, integer, numeric, factor, or ordered type into 'POSIXct' (or 'Date') objects, using one of a number of predefined formats, and relying on Boost facilities for date and time parsing.
The US Census Bureau provides a seasonal adjustment program now called 'X-13ARIMA-SEATS' building on both earlier programs called X-11 and X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau offers both source and binary versions -- which this package integrates for use by other R packages.
C++ classes to embed R in C++ applications The 'RInside' packages makes it easier to have "R inside" your C++ application by providing a C++ wrapper class providing the R interpreter. As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the eight subdirectories of the examples/ directory of the installed package: standard, mpi (for parallel computing) qt (showing how to embed 'RInside' inside a Qt GUI application), wt (showing how to build a "web-application" using the Wt toolkit), armadillo (for 'RInside' use with 'RcppArmadillo') and eigen (for 'RInside' use with 'RcppEigen'). The example use GNUmakefile(s) with GNU extensions, so a GNU make is required (and will use the GNUmakefile automatically). Doxygen-generated documentation of the C++ classes is available at the 'RInside' website as well.
An R Interface to 'Bloomberg' is provided via the 'Blp API'.
'Rcpp' integration for 'GNU GSL' vectors and matrices The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for scientific computing. It is particularly useful for C and C++ programs as it provides a standard C interface to a wide range of mathematical routines such as special functions, permutations, combinations, fast fourier transforms, eigensystems, random numbers, quadrature, random distributions, quasi-random sequences, Monte Carlo integration, N-tuples, differential equations, simulated annealing, numerical differentiation, interpolation, series acceleration, Chebyshev approximations, root-finding, discrete Hankel transforms physical constants, basis splines and wavelets. There are over 1000 functions in total with an extensive test suite. The 'RcppGSL' package provides an easy-to-use interface between 'GSL' data structures and R using concepts from 'Rcpp' which is itself a package that eases the interfaces between R and C++. This package also serves as a prime example of how to build a package that uses 'Rcpp' to connect to another third-party library. The 'autoconf' script, 'inline' plugin and example package can all be used as a stanza to write a similar package against another library.
The 'RQuantLib' package makes parts of 'QuantLib' accessible from R The 'QuantLib' project aims to provide a comprehensive software framework for quantitative finance. The goal is to provide a standard open source library for quantitative analysis, modeling, trading, and risk management of financial assets.
A scripting and command-line front-end is provided by 'r' (aka 'littler') as 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 hash-mark-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.
The true random number service provided by the RANDOM.ORG website created by Mads Haahr samples atmospheric noise via radio tuned to an unused broadcasting frequency together with a skew correction algorithm due to John von Neumann. More background is available in the included vignette based on an essay by Mads Haahr. In its current form, the package offers functions to retrieve random integers, randomized sequences and random strings.
The configuration format defined by 'TOML' (which expands to "Tom's Obvious Markup Language") specifies an excellent format (described at <https://github.com/toml-lang/toml>) suitable for both human editing as well as the common uses of a machine-readable format. This package uses 'Rcpp' to connect the 'cpptoml' parser written by Chase Geigle (in modern C++11) to R.
The Ziggurat generator for normally distributed random numbers, originally proposed by Marsaglia and Tsang (JSS, 2000), has been improved upon a few times starting with Leong et al (JSS, 2005). This package provides an aggregation in order to compare different implementations. The goal is to provide an 'faster but good enough' alternative for use with R and C++ code. The package is still in an early state. Unless you know what you are doing, sticking with the generators provided by R may be a good idea as these have been extremely diligently tested.
An R interface to the Pushbullet messaging service which provides fast and efficient notifications (and file transfer) between computers, phones and tablets. An account has to be registered at the site http://www.pushbullet.com site to obtain a (free) API key.
Protocol Buffers are a way of encoding structured data in an efficient yet extensible format. Google uses Protocol Buffers for almost all of its internal 'RPC' protocols and file formats. Additional documentation is available in two included vignettes one of which corresponds to our paper in the Journal of Statistical Software (2016, v71i02). Either version 2 or 3 of the 'Protocol Buffers' 'API' is supported.
'Annoy' is a small C++ library for Approximate Nearest Neighbors written for efficient memory usage as well an ability to load from / save to disk. This package provides an R interface by relying on the 'Rcpp' package, exposing the same interface as the original Python wrapper to 'Annoy'. See <https://github.com/spotify/annoy> for more on 'Annoy'. 'Annoy' is released under Version 2.0 of the Apache License. Also included is a small Windows port of 'mmap' which is released under the MIT license.
The RcppClassic package provides a deprecated C++ library which facilitates the integration of R and C++. New projects should use the new Rcpp API in the Rcpp package.
'Asio' is a cross-platform C++ library for network and low-level I/O programming that provides developers with a consistent asynchronous model using a modern C++ approach. 'Asio' is also included in Boost but requires linking when used with Boost. Standalone it can be used header-only provided a recent-enough compiler. 'Asio' is written and maintained by Christopher M. Kohlhoff. 'Asio' is released under the 'Boost Software License', Version 1.0.
The 'cnpy' library written by Carl Rogers provides read and write facilities for files created with (or for) the 'NumPy' extension for 'Python'. Vectors and matrices of numeric types can be read or written to and from files as well as compressed files. Support for integer files is available if the package has been built with -std=c++11 which is the default starting with release 0.2.3 following the release of R 3.1.0, and available on all platforms following the release of R 3.3.0 with the updated 'Rtools'.
This package provides other packages with access to the internal R serialization code. Access to this code is provided at the C function level by using the registration of native function mechanism. Client packages simply include a single header file RApiSerializeAPI.h provided by this package. This packages builds on the Rhpc package by Junji Nakano and Ei-ji Nakama which also includes a (partial) copy of the file src/main/serialize.c from R itself. The R Core group is the original author of the serialization code made available by this package.
Display a randomly selected quote about Richard M. Stallman based on the collection in the 'GNU Octave' function 'fact()' which was aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site stallmanfacts.com (which is accessible only via <http://archive.org>).
An efficient C++ based implementation of the 'DEoptim' function which performs global optimization by differential evolution. Its creation was motivated by trying to see if the old approximation "easier, shorter, faster: pick any two" could in fact be extended to achieving all three goals while moving the code from plain old C to modern C++. The initial version did in fact do so, but a good part of the gain was due to an implicit code review which eliminated a few inefficiencies which have since been eliminated in 'DEoptim'.
Examples for Seamless R and C++ integration The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways. This package provides some usage examples. Note that the documentation in this package currently does not cover all the features in the package. It is not even close. On the other hand, the site <http://gallery.rcpp.org> is regrouping a large number of examples for 'Rcpp'.
This package provides R with access to Boost Date_Time functionality by using Rcpp modules. Functionality from Boost Date_Time for dates, durations (both for days and datetimes), timezones, and posix time ("ptime") is provided. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of R's 64) to present a ptime object.
A 'tufte'-alike style for 'rmarkdown'.
This package provides R with access to the Sequential Monte Carlo Template Classes by Johansen (Journal of Statistical Software, 2009, v30, i6). At present, two additional examples have been added, and the first example from the JSS paper has been extended. Further integration and extensions are planned.
A function to retrieve the system timezone on Unix systems which has been found to find an answer when 'Sys.timezone()' has failed. It is based on an answer by Duane McCully posted on 'StackOverflow', and adapted to be callable from R. The package also builds on Windows, but just returns NULL.
Connection to the 'Redis' key/value store using the C-language client library 'hiredis'. 'MsgPack' encoding is optional if the 'RcppMsgPack' package is detected. You can install via from the 'ghrr' drat repository listed below.
Statistical analysis of monthly background checks of gun purchases for the New York Times story "What Drives Gun Sales: Terrorism, Obama and Calls for Restrictions" at <http://www.nytimes.com/interactive/2015/12/10/us/gun-sales-terrorism-obama-restrictions.html?> is provided.
R access to the 'FOAAS' (F... Off As A Service) web service is provided.
The RDieHarder packages provides an R interface to the dieharder suite of random number generators and tests that was developed by Robert G. Brown and David Bauer, extending earlier work by George Marsaglia and others.
Display a random fact about Carl Friedrich Gauss based the on collection curated by Mike Cavers via the <http://gaussfacts.com> site.
The 'Streamulus' (template, header-only) library by Irit Katriel (at <https://github.com/iritkatriel/streamulus>) provides a very powerful yet convenient framework for stream processing. This package connects 'Streamulus' to R by providing both the header files and all examples.
This package provides access to some of the C level functions of the xts package. In its current state, the package is mostly a proof-of-concept to support adding useful functions, and does not yet add any of its own.
The Rcpp package contains a C++ library that facilitates the integration of R and C++ in various ways via a rich API. This API was preceded by an earlier version which has been deprecated since 2010 (but is still supported to provide backwards compatability in the package RcppClassic). This package RcppClassicExamples provides usage examples for the older, deprecated API. There is also a corresponding package RcppExamples package with examples for the newer, current API which we strongly recommend as the basis for all new development.
Access to the C-level R date and datetime code is provided for C-level API use by other packages via registration of native functions. Client packages simply include a single header 'RApiDatetime.h' provided by this package, and also 'import' it. The R Core group is the original author of the code made available with slight modifications by this package.
The 'Vowpal Wabbit' project is a fast out-of-core learning system sponsored by Microsoft Research (having started at Yahoo! Research) and written by John Langford along with a number of contributors. This R package does not include the distributed computing implementation of the cluster/ directory of the upstream sources. Use of the software as a network service is also not directly supported as the aim is a simpler direct call from R for validation and comparison.
Recent gcc and clang compiler versions provide functionality to memory violations and other undefined behaviour; this is often referred to as "Address Sanitizer" (or SAN) and "Undefined Behaviour Sanitizer" (UBSAN). The Writing R Extension manual describes this in some detail in Section 4.9. This feature has to be enabled in the corresponding binary, eg in R, which is somewhat involved as it also required a current compiler toolchain which is not yet widely available, or in the case of Windows, not available at all (via the common Rtools mechanism). As an alternative, the pre-built Docker containers available via the Docker Hub at https://registry.hub.docker.com/u/eddelbuettel/docker-debian-r/ can be used on Linux, and via boot2docker on Windows and OS X. This package then provides a means of testing the compiler setup as the known code failures provides in the sample code here should be detected correctly, whereas a default build of R will let the package pass. The code samples are based on the examples from the Address Sanitizer Wiki at https://code.google.com/p/address-sanitizer/wiki/AddressSanitizer.
'GPU'/CPU Benchmarking on Debian-package based systems This package benchmarks performance of a few standard linear algebra operations (such as a matrix product and QR, SVD and LU decompositions) across a number of different 'BLAS' libraries as well as a 'GPU' implementation. To do so, it takes advantage of the ability to 'plug and play' different 'BLAS' implementations easily on a Debian and/or Ubuntu system. The current version supports - 'Reference BLAS' ('refblas') which are un-accelerated as a baseline - Atlas which are tuned but typically configure single-threaded - Atlas39 which are tuned and configured for multi-threaded mode - 'Goto Blas' which are accelerated and multi-threaded - 'Intel MKL' which is a commercial accelerated and multithreaded version. As for 'GPU' computing, we use the CRAN package - 'gputools' For 'Goto Blas', the 'gotoblas2-helper' script from the ISM in Tokyo can be used. For 'Intel MKL' we use the Revolution R packages from Ubuntu 9.10.
The 'APT Package Management System' provides Debian and Debian-derived Linux systems with a powerful system to resolve package dependencies. This package offers access directly from R.
The 'getconf' command-line tool provided by 'libc' allows querying of a large number of system variables. This package provides similar functionality.
Database interface and PostgreSQL driver for R This package provides a Database Interface (DBI) compliant driver for R to access PostgreSQL database systems. In order to build and install this package from source, PostgreSQL itself must be present your system to provide PostgreSQL functionality via its libraries and header files. These files are provided as postgresql-devel package under some Linux distributions. On Microsoft Windows system the attached libpq library source will be used. A wiki and issue tracking system for the package are available at Google Code at https://code.google.com/p/rpostgresql/ .
A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well.
rbenchmark is inspired by the Perl module Benchmark, and is intended to facilitate benchmarking of arbitrary R code. The library 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.
R interface to Python modules, classes, and functions. When calling into Python R data types are automatically converted to their equivalent Python types. When values are returned from Python to R they are converted back to R types. Compatible with all versions of Python >= 2.7.
Provides a base S4 class for comparative methods, incorporating one or more trees and trait data.
Interface to 'TensorFlow' <https://www.tensorflow.org/>, an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
A collection of fortunes from the R community.
An interface for retrieving and displaying the information returned online by Google Trends is provided. Trends (number of hits) over the time as well as geographic representation of the results can be displayed.
A set of tools for displaying, modeling and analysing multivariate abundance data in community ecology. See 'mvabund-package.Rd' for details of overall package organization. The package is implemented with the Gnu Scientific Library (http://www.gnu.org/software/gsl/) and Rcpp (http://dirk.eddelbuettel.com/code/rcpp.html) R / C++ classes.
Inference in mixed models, including GLMMs with spatial correlations and models with non-Gaussian random effects (e.g., Beta Binomial, or negative-binomial mixed models). Heteroscedasticity can further be fitted by a linear model. The algorithms are currently various Laplace approximations methods for ML or REML, in particular h-likelihood and penalized-likelihood methods.
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.
Allows users to create time series of tropical storm exposure histories for chosen counties for a number of hazard metrics (wind, rain, distance from the storm, etc.). This package interacts with data available through the 'hurricaneexposuredata' package, which is available in a 'drat' repository. To access this data package, run 'install.packages("hurricaneexposuredata", repos = "https://geanders.github.io/drat/", type = "source")'. The size of the 'hurricaneexposuredata' package is approximately 25 MB. This work was supported in part by grants from the National Institute of Environmental Health Sciences (R00ES022631), the National Science Foundation (1331399), and a NASA Applied Sciences Program/Public Health Program Grant (NNX09AV81G).
Access to a family of Gauss error functions for arbitrary complex arguments is provided via the 'Faddeeva' package by Steven G. Johnson (see <http://ab-initio.mit.edu/wiki/index.php/Faddeeva_Package> for more information).
Estimates the multivariate skew-t and nested models, as described in the articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal model: a population Monte Carlo approach. Comput. Statist. Data Anal. <doi:10.1016/j.csda.2013.02.007> and in Parisi, A., Liseo, B. Objective Bayesian analysis for the multivariate skew-t model (to appear).
Can be used to carry out permutation resampling inference in the context of RNA microarray studies.
Offers faster manipulation of dendrogram objects in R. A dendrogram object in R is a list structure with attributes in its nodes and leaves. Working with dendrogram objects often require a function to recursively go through all (or most) element in the list object. Naturally, such function are rather slow in R, but can become much faster thanks to 'Rcpp'.