# Romain Francois

#### 31 packages on CRAN

#### 1 packages on GitHub

Syntax highlighter for R code based on the results of the R parser. Rendering in HTML and latex markup. Custom Sweave driver performing syntax highlighting of R code chunks.

Rcpp11 includes a header only C++11 library that facilitates integration between R and modern C++.

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, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013, <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018, <doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details.

Provides a 'tbl_df' class (the 'tibble') that provides stricter checking and better formatting than the traditional data frame.

A fast, consistent tool for working with data frame like objects, both in memory and out of memory.

The goal of 'readr' is to provide a fast and friendly way to read rectangular data (like 'csv', 'tsv', and 'fwf'). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes.

Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.

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 3.3.4). 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 the .C and .Call calling conventions.

High level functions for parallel programming with 'Rcpp'. For example, the 'parallelFor()' function can be used to convert the work of a standard serial "for" loop into a parallel one and the 'parallelReduce()' function can be used for accumulating aggregate or other values.

The encoding of colour can be handled in many different ways, using different colour spaces. As different colour spaces have different uses, efficient conversion between these representations are important. The 'farver' package provides a set of functions that gives access to very fast colour space conversion and comparisons implemented in C++, and offers 100-fold speed improvements over the 'convertColor' function in the 'grDevices' package.

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.

'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. 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.

Miscellaneous functions for SciViews or general use: manage a temporary environment attached to the search path for temporary variables you do not want to save() or load(), test if Aqua, Mac, Win, ... Show progress bar, etc.

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 'JSS' paper (2016, <doi:10.18637/jss.v071.i02>. Either version 2 or 3 of the 'Protocol Buffers' 'API' is supported.

Read hierarchical fixed width files like those commonly used by many census data providers. Also allows for reading of data in chunks, and reading 'gzipped' files without storing the full file in memory.

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.

Provides a progress bar similar to 'dplyr' that can write progress out to a variety of locations, including stdout(), stderr(), or from file(). Useful when using 'knitr' or 'rmarkdown', and you still want to see progress of calculations in the terminal.

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. (2017). Objective Bayesian analysis for the multivariate skew-t model. Statistical Methods & Applications <doi: 10.1007/s10260-017-0404-0>.

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.

'Blaze' is an open-source, high-performance C++ math library for dense and sparse arithmetic. With its state-of-the-art Smart Expression Template implementation 'Blaze' combines the elegance and ease of use of a domain-specific language with 'HPC'-grade performance, making it one of the most intuitive and fastest C++ math libraries available. The 'Blaze' library offers: - high performance through the integration of 'BLAS' libraries and manually tuned 'HPC' math kernels - vectorization by 'SSE', 'SSE2', 'SSE3', 'SSSE3', 'SSE4', 'AVX', 'AVX2', 'AVX-512', 'FMA', and 'SVML' - parallel execution by 'OpenMP', C++11 threads and 'Boost' threads ('Boost' threads are disabled in 'RcppBlaze') - the intuitive and easy to use API of a domain specific language - unified arithmetic with dense and sparse vectors and matrices - thoroughly tested matrix and vector arithmetic - completely portable, high quality C++ source code. The 'RcppBlaze' package includes the header files from the 'Blaze' library with disabling some functionalities related to link to the thread and system libraries which make 'RcppBlaze' be a header-only library. Therefore, users do not need to install 'Blaze' and the dependency 'Boost'. 'Blaze' is licensed under the New (Revised) BSD license, while 'RcppBlaze' (the 'Rcpp' bindings/bridge to 'Blaze') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Note that since 'Blaze' has committed to 'C++14' commit to 'C++14' which does not used by most R users from version 3.0, we will use the version 2.6 of 'Blaze' which is 'C++98' compatible to support the most compilers and system.

Set of tools aimed at wrapping some of the functionalities of the packages tools, utils and codetools into a nicer format so that an IDE can use them.

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.

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 compatibility in the package 'RcppClassic'). This package 'RcppClassicExamples' provides usage examples for the older, deprecated API. There is also a corresponding package 'RcppExamples' with examples for the newer, current API which we strongly recommend as the basis for all new development.

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'.

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'.