RcppGSL/0000755000176200001440000000000013161742552011532 5ustar liggesusersRcppGSL/TODO0000644000176200001440000000027112774327533012231 0ustar liggesusers o [DONE] More STL-like interface for vectors: * RcppGSL::vector<>::operator[] * RcppGSL::vector<>::iterator * RcppGSL::vector<>::begin * RcppGSL::vector<>::end RcppGSL/inst/0000755000176200001440000000000013161737707012515 5ustar liggesusersRcppGSL/inst/examples/0000755000176200001440000000000012774327533014334 5ustar liggesusersRcppGSL/inst/examples/RcppGSLExample/0000755000176200001440000000000012774327533017122 5ustar liggesusersRcppGSL/inst/examples/RcppGSLExample/configure.ac0000644000176200001440000000154612774327533021416 0ustar liggesusers AC_INIT([RcppGSLExample], 0.1.0) ## Use gsl-config to find arguments for compiler and linker flags ## ## Check for non-standard programs: gsl-config(1) AC_PATH_PROG([GSL_CONFIG], [gsl-config]) ## If gsl-config was found, let's use it if test "${GSL_CONFIG}" != ""; then # Use gsl-config for header and linker arguments (without BLAS which we get from R) GSL_CFLAGS=`${GSL_CONFIG} --cflags` GSL_LIBS=`${GSL_CONFIG} --libs` else AC_MSG_ERROR([gsl-config not found, is GSL installed?]) fi ## Use Rscript to query Rcpp for compiler and linker flags ## link flag providing libary as well as path to library, and optionally rpath ##RCPP_LDFLAGS=`${R_HOME}/bin/Rscript -e 'Rcpp:::LdFlags()'` # Now substitute these variables in src/Makevars.in to create src/Makevars AC_SUBST(GSL_CFLAGS) AC_SUBST(GSL_LIBS) ##AC_SUBST(RCPP_LDFLAGS) AC_OUTPUT(src/Makevars) RcppGSL/inst/examples/RcppGSLExample/src/0000755000176200001440000000000012774327533017711 5ustar liggesusersRcppGSL/inst/examples/RcppGSLExample/src/Makevars.in0000644000176200001440000000024412774327533022012 0ustar liggesusers # set by configure GSL_CFLAGS = @GSL_CFLAGS@ GSL_LIBS = @GSL_LIBS@ # combine with standard arguments for R PKG_CPPFLAGS = $(GSL_CFLAGS) PKG_LIBS = $(GSL_LIBS) RcppGSL/inst/examples/RcppGSLExample/src/colNorm.cpp0000644000176200001440000000604412774327533022032 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; tab-width: 4 -*- // // colNorm.cpp: Rcpp and GSL based example of column norm // adapted from `Section 8.4.13 Example programs for matrices' // of the GSL manual // // Copyright (C) 2010 - 2015 Dirk Eddelbuettel and Romain Francois // // This file is part of RcppGSL. // // RcppGSL is free software: you can redistribute it and/or modify it // under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 2 of the License, or // (at your option) any later version. // // RcppGSL is distributed in the hope that it will be useful, but // WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with RcppGSL. If not, see . #include #include #include // old initial implementation, kept for comparison extern "C" SEXP colNorm_old(SEXP sM) { try { RcppGSL::matrix M = sM; // create gsl data structures from SEXP int k = M.ncol(); Rcpp::NumericVector n(k); // to store results for (int j = 0; j < k; j++) { RcppGSL::vector_view colview = gsl_matrix_const_column (M, j); n[j] = gsl_blas_dnrm2(colview); } M.free() ; return n; // return vector } catch( std::exception &ex ) { forward_exception_to_r( ex ); } catch(...) { ::Rf_error( "c++ exception (unknown reason)" ); } return R_NilValue; // -Wall } // newer Attributes-based implementation // [[Rcpp::export]] Rcpp::NumericVector colNorm_old2(Rcpp::NumericMatrix M) { // this conversion involves an allocation RcppGSL::matrix G = Rcpp::as< RcppGSL::matrix >(M); int k = G.ncol(); Rcpp::NumericVector n(k); // to store results for (int j = 0; j < k; j++) { RcppGSL::vector_view colview = gsl_matrix_const_column (G, j); n[j] = gsl_blas_dnrm2(colview); } G.free(); return n; // return vector } // newer Attributes-based simplementation with reference counting // [[Rcpp::export]] Rcpp::NumericVector colNorm_old3(RcppGSL::matrix G) { int k = G.ncol(); Rcpp::NumericVector n(k); // to store results for (int j = 0; j < k; j++) { RcppGSL::vector_view colview = gsl_matrix_column (G, j); n[j] = gsl_blas_dnrm2(colview); } return n; // return vector } // newest version using typedefs and const & // [[Rcpp::export]] Rcpp::NumericVector colNorm(const RcppGSL::Matrix & G) { int k = G.ncol(); Rcpp::NumericVector n(k); // to store results for (int j = 0; j < k; j++) { RcppGSL::VectorView colview = gsl_matrix_const_column (G, j); n[j] = gsl_blas_dnrm2(colview); } return n; // return vector } RcppGSL/inst/examples/RcppGSLExample/src/Makevars.win0000644000176200001440000000023012774327533022174 0ustar liggesusers## This assumes that the LIB_GSL variable points to working GSL libraries PKG_CPPFLAGS=-I$(LIB_GSL)/include PKG_LIBS=-L$(LIB_GSL)/lib -lgsl -lgslcblas RcppGSL/inst/examples/RcppGSLExample/src/RcppExports.cpp0000644000176200001440000000230312774327533022704 0ustar liggesusers// This file was generated by Rcpp::compileAttributes // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include #include using namespace Rcpp; // colNorm_old2 Rcpp::NumericVector colNorm_old2(Rcpp::NumericMatrix M); RcppExport SEXP RcppGSLExample_colNorm_old2(SEXP MSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type M(MSEXP); __result = Rcpp::wrap(colNorm_old2(M)); return __result; END_RCPP } // colNorm_old3 Rcpp::NumericVector colNorm_old3(RcppGSL::matrix G); RcppExport SEXP RcppGSLExample_colNorm_old3(SEXP GSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< RcppGSL::matrix >::type G(GSEXP); __result = Rcpp::wrap(colNorm_old3(G)); return __result; END_RCPP } // colNorm Rcpp::NumericVector colNorm(const RcppGSL::Matrix& G); RcppExport SEXP RcppGSLExample_colNorm(SEXP GSEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< const RcppGSL::Matrix& >::type G(GSEXP); __result = Rcpp::wrap(colNorm(G)); return __result; END_RCPP } RcppGSL/inst/examples/RcppGSLExample/NAMESPACE0000644000176200001440000000010412774327533020334 0ustar liggesusersuseDynLib(RcppGSLExample) importFrom(Rcpp, evalCpp) export(colNorm) RcppGSL/inst/examples/RcppGSLExample/R/0000755000176200001440000000000012774327533017323 5ustar liggesusersRcppGSL/inst/examples/RcppGSLExample/R/colNorm.R0000644000176200001440000000173112774327533021061 0ustar liggesusers## colNorm.R: R wrapper to Rcpp/GSL colNorm implementation ## ## Copyright (C) 2010 Dirk Eddelbuettel and Romain Francois ## ## This file is part of RcppGSL. ## ## RcppGSL is free software: you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 2 of the License, or ## (at your option) any later version. ## ## RcppGSL is distributed in the hope that it will be useful, but ## WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## ## You should have received a copy of the GNU General Public License ## along with RcppGSL. If not, see . ## old call now shadowed by auto-generated colNorm() in RcppExports.R colNorm_old <- function(M) { stopifnot(is.matrix(M)) res <- .Call("colNorm_old", M, package="RcppGSLExample") } RcppGSL/inst/examples/RcppGSLExample/R/RcppExports.R0000644000176200001440000000063712774327533021745 0ustar liggesusers# This file was generated by Rcpp::compileAttributes # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 colNorm_old2 <- function(M) { .Call('RcppGSLExample_colNorm_old2', PACKAGE = 'RcppGSLExample', M) } colNorm_old3 <- function(G) { .Call('RcppGSLExample_colNorm_old3', PACKAGE = 'RcppGSLExample', G) } colNorm <- function(G) { .Call('RcppGSLExample_colNorm', PACKAGE = 'RcppGSLExample', G) } RcppGSL/inst/examples/RcppGSLExample/DESCRIPTION0000644000176200001440000000071212774327533020630 0ustar liggesusersPackage: RcppGSLExample Title: A Really Simple Example of Using RcppGSL in a Package Version: 0.0.3 Date: 2014-06-26 Author: Dirk Eddelbuettel and Romain Francois Maintainer: Dirk Eddelbuettel Description: A complete examples for Seamless R, C++ and GSL integration in an R package is provided. 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This example reprises an example in section 8.4.13 of the GSL reference manual. } \value{ \code{colNorm} returns a vector each element of which corresponds to the vector norm of the corresponding column of \code{M}. } \references{GNU GSL project: \url{http://www.gnu.org/software/gsl}} \author{ The GNU GSL library is being written by team of authors with the overall development, design and implementation lead by Brian Gough and Gerard Jungman. RcppGSL is written by Romain Francois and Dirk Eddelbuettel. } \examples{ ## see Section 8.4.13 of the GSL manual ## create M as a sum of two outer products M <- outer(sin(0:9), rep(1,10), "*") + outer(rep(1, 10), cos(0:9), "*") print(colNorm(M)) ## same result using just R print(apply(M, 2, function(x) sqrt(sum(x^2)))) } RcppGSL/inst/examples/bSpline/0000755000176200001440000000000012774327533015730 5ustar liggesusersRcppGSL/inst/examples/bSpline/bSpline.R0000644000176200001440000000217512774327533017454 0ustar liggesusers ## This example illustrated use of RcppGSL using the 'Rcpp attributes' feature ## ## The example comes from Section 39.7 of the GSL Reference manual, and constructs ## a data set from the curve y(x) = \cos(x) \exp(-x/10) on the interval [0, 15] with ## added Gaussian noise --- which is then fit via linear least squares using a cubic ## B-spline basis functions with uniform breakpoints. ## ## Obviously all this could be done in R too as R can both generate data, and fit ## models including (B-)splines. But the point to be made here is that we can very ## easily translate a given GSL program (thanks to RcppGSL), and get it into R with ## ease thanks to Rcpp and Rcpp attributes. require(Rcpp) # load Rcpp sourceCpp("bSpline.cpp") # compile two functions dat <- genData() # generate the data fit <- fitData(dat) # fit the model, returns matrix and gof measures X <- fit[["X"]] # extract vectors Y <- fit[["Y"]] op <- par(mar=c(3,3,1,1)) plot(dat[,"x"], dat[,"y"], pch=19, col="#00000044") lines(X, Y, col="orange", lwd=2) par(op) RcppGSL/inst/examples/bSpline/bSpline.cpp0000644000176200001440000000662012774327533020034 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; indent-tabs-mode: nil; -*- // [[Rcpp::depends(RcppGSL)]] #include #include #include #include #include #include const int N = 200; // number of data points to fit const int NCOEFFS = 12; // number of fit coefficients */ const int NBREAK = (NCOEFFS - 2); // ncoeffs + 2 - k = ncoeffs - 2 as k = 4 */ // [[Rcpp::export]] Rcpp::List genData() { const size_t n = N; size_t i; RcppGSL::Vector w(n), x(n), y(n); gsl_rng_env_setup(); gsl_rng *r = gsl_rng_alloc(gsl_rng_default); //printf("#m=0,S=0\n"); for (i = 0; i < n; ++i) { /* this is the data to be fitted */ double xi = (15.0 / (N - 1)) * i; double yi = cos(xi) * exp(-0.1 * xi); double sigma = 0.1 * yi; double dy = gsl_ran_gaussian(r, sigma); yi += dy; x[i] = xi; // instead of gsl_vector_set(x, i, xi); y[i] = yi; // instead of gsl_vector_set(y, i, yi); w[i] = 1.0 / (sigma * sigma); // gsl_vector_set(w, i, 1.0 / (sigma * sigma)); //printf("%f %f\n", xi, yi); } gsl_rng_free(r); return Rcpp::DataFrame::create(Rcpp::Named("x") = x, Rcpp::Named("y") = y, Rcpp::Named("w") = w); } // [[Rcpp::export]] Rcpp::List fitData(Rcpp::DataFrame D) { const size_t ncoeffs = NCOEFFS; const size_t nbreak = NBREAK; const size_t n = N; size_t i, j; RcppGSL::Vector y = D["y"]; // access columns by name, RcppGSL::Vector x = D["x"]; // assigning to GSL vectors RcppGSL::Vector w = D["w"]; gsl_bspline_workspace *bw; RcppGSL::Vector B(ncoeffs); RcppGSL::Vector c(ncoeffs); RcppGSL::Matrix X(n, ncoeffs); RcppGSL::Matrix cov(ncoeffs, ncoeffs); gsl_multifit_linear_workspace *mw; double chisq, Rsq, dof, tss; bw = gsl_bspline_alloc(4, nbreak); // allocate a cubic bspline workspace (k = 4) mw = gsl_multifit_linear_alloc(n, ncoeffs); gsl_bspline_knots_uniform(0.0, 15.0, bw); // use uniform breakpoints on [0, 15] for (i = 0; i < n; ++i) { // construct the fit matrix X double xi = x[i]; // gsl_vector_get(x, i); gsl_bspline_eval(xi, B, bw); // compute B_j(xi) for all j for (j = 0; j < ncoeffs; ++j) { // fill in row i of X double Bj = B[j]; // gsl_vector_get(B, j); X(i,j) = Bj; // gsl_matrix_set(X, i, j, Bj); } } gsl_multifit_wlinear(X, w, y, c, cov, &chisq, mw); // do the fit dof = n - ncoeffs; tss = gsl_stats_wtss(w->data, 1, y->data, 1, y->size); Rsq = 1.0 - chisq / tss; Rcpp::NumericVector FX(151), FY(151); // output the smoothed curve double xi, yi, yerr; for (xi = 0.0, i=0; xi < 15.0; xi += 0.1, i++) { gsl_bspline_eval(xi, B, bw); gsl_multifit_linear_est(B, c, cov, &yi, &yerr); FX[i] = xi; FY[i] = yi; } gsl_bspline_free(bw); gsl_multifit_linear_free(mw); return Rcpp::List::create(Rcpp::Named("X") = FX, Rcpp::Named("Y") = FY, Rcpp::Named("chisqdof") = Rcpp::wrap(chisq/dof), Rcpp::Named("rsq") = Rcpp::wrap(Rsq)); } RcppGSL/inst/NEWS.Rd0000644000176200001440000001500113161734470013547 0ustar liggesusers\name{NEWS} \title{News for Package \pkg{RcppGSL}} \newcommand{\ghpr}{\href{https://github.com/eddelbuettel/rcppgsl/pull/#1}{##1}} \newcommand{\ghit}{\href{https://github.com/eddelbuettel/rcppgsl/issues/#1}{##1}} \section{Changes in version 0.3.3 (2017-09-24)}{ \itemize{ \item We also check for \code{gsl-config} at package load. \item The vignette now uses the pinp package in two-column mode. \item Minor other fixes to package and testing infrastructure. } } \section{Changes in version 0.3.2 (2017-03-04)}{ \itemize{ \item In the \code{fastLm} function, \code{.Call} now uses the correct \code{PACKAGE=} argument \item Added file \code{init.c} with calls to \code{R_registerRoutines()} \code{and R_useDynamicSymbols()}; also use \code{.registration=TRUE} in \code{useDynLib} in \code{NAMESPACE} \item The skeleton configuration for created packages was updated. } } \section{Changes in version 0.3.1 (2016-10-02)}{ \itemize{ \item The unit test driver was updated and simplified, (by request of CRAN) no longer leaves files in `/tmp`, and removes two unexported (and unused) test helper functions (PR \ghpr{10}) \item Switched to \code{run.sh} for Travis (PR \ghpr{11}) \item Use canonical CRAN URLs in README.md \item Restored 'boxed' display of code in vignette (PR \ghpr{12}) } } \section{Changes in version 0.3.0 (2015-08-30)}{ \itemize{ \item The RcppGSL matrix and vector class now keep track of object allocation and can therefore automatically free allocated object in the destructor. Explicit \code{x.free()} use is still supported. \item The matrix and vector classes now support const reference semantics in the interfaces (thanks to PR #7 by Dan Dillon) \item The matrix_view and vector_view classes are reorganized to better support const arguments (thanks to PR #8 and #9 by Dan Dillon) \item Shorthand forms such as \code{Rcpp::Matrix} have been added for \code{double} and \code{int} vectors and matrices including views. \item Examples such as \code{fastLm} can now be written in a much cleaner and shorter way as GSL objects can appear in the function signature and without requiring explicit \code{.free()} calls at the end. \item The included examples, as well as the introductory vignette, have been updated accordingly. } } \section{Changes in version 0.2.5 (2015-07-05)}{ \itemize{ \item The \code{colnorm} function in the included example package was rewritten to use Rcpp Attributes, the example package was updated and its version number increased to 0.0.3. \item The unit tests also use the updated version of the example package. \item The package, and the included example package, were updated throughout to conform to the current \code{R CMD check} standards. \item The RcppGSL-intro vignette was updated throughout. \item The Travis CI integration now uses r-cran-* packages which leads to faster tests. } } \section{Changes in version 0.2.4 (2015-01-24)}{ \itemize{ \item Two new helper function to turn the default GSL error handler off (and to restore it) were added. The default handler is now turned off when the package is attached so that GSL will no longer abort an R session on error. Users will have to check the error code. \item The \code{RcppGSL-intro.Rnw} vignette was expanded with a short section on the GSL error handler (thanks to Qiang Kou). } } \section{Changes in version 0.2.3 (2015-01-10)}{ \itemize{ \item The \code{src/Makevars.in} was pruned of GNU make features at the request of the CRAN Maintainers. \item \code{configure.ac} and \code{configure} were updated, and shortened. \item The \code{RcppGSL-intro.Rnw} vignette was updated for its look and feel. } } \section{Changes in version 0.2.2 (2014-05-31)}{ \itemize{ \item A subtle bug (tickled only by clang on some OS versions) in vector and matrix view initialization was corrected by Kevin Ushey } } \section{Changes in version 0.2.1 (2014-05-26)}{ \itemize{ \item Added new example based on B-splines example in GSL manual illustrating simple GSL use via Rcpp attributes \item Vignette compilation has been reverted to using \pkg{highlight} since version 0.4.2 or greater can be used as a vignette engine (with R 3.0.* or later). \item Vignette compilation is now being done by \code{R CMD build} as R 3.0.0 supports different vignette engines, so the vignette build process has been simplified. A convenience helper script has also been added for command-line builds. \item Unit tests now use \code{sourceCpp()} instead of \code{cxxfunction()} from the \pkg{inline} package \item The \code{DESCRIPTION} file now uses \code{Suggests: Rcpp} (instead of \code{Depends: Rcpp}) to permit building of the vignette \item The package now takes advantage of the simplified build process available with Rcpp (>= 0.11.0) \item Similar updates to the build process were made for the example package included with RcppGSL } } \section{Changes in version 0.2.0 (2012-07-22)}{ \itemize{ \item{summary() for fastLm() now displays more information} \item{fastLmPure() now uses same argument order as R's lm.fit()} \item{Added more unit tests for fastLm() and related functions} \item{Export and document S3 methods in NAMESPACE and manual page as such} \item{Vignettes have been moved to the \code{vignettes/} directory} \item{Main vignette renamed to \code{RcppGSL-intro.pdf} to use a filename different from the package reference manual} \item{NEWS file converted to .Rd format} \item{inline plugin support function no longer uses assignInNamespace but deploys a small package-global enviornment} } } \section{Changes in version 0.1.1 (2011-04-05)}{ \itemize{ \item{Unit tests produce a summary vignette as for some of the other packages} \item{The documentation Makefile now uses the $R_HOME environment variable} \item{The documentation Makefile no longer calls clean in the all target} } } \section{Changes in version 0.1.0 (2010-11-30)}{ \itemize{ \item{Initial CRAN release with basic functionality for vectors and matrices} \item{A vignette provides an introduction and documentation about the package} \item{An example package RcppGSLExample provides a complete stanza for creating your own package using RcppGSL (and the GSL and Rcpp)} } } RcppGSL/inst/doc/0000755000176200001440000000000013161737707013262 5ustar 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For footer text lead_author_surname: Eddelbuettel and François # Place DOI URL or CRAN Package URL here doi: "https://cran.r-project.org/package=RcppGSL" # Abstract abstract: | The GNU Scientific Library, or \pkg{GSL}, is a collection of numerical routines for scientific computing \citep{GSL}. It is particularly useful for \proglang{C} and \proglang{C++} programs as it provides a standard \proglang{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 \pkg{RcppGSL} package provides an easy-to-use interface between \pkg{GSL} and \proglang{R}, with a particular focus on matrix and vector data structures. \pkg{RcppGSL} relies on \pkg{Rcpp} \citep{JSS:Rcpp,Eddelbuettel:2013:Rcpp,CRAN:Rcpp} which is itself a package that eases the interfaces between \proglang{R} and C++.} # Font size of the document, values of 9pt (default), 10pt, 11pt and 12pt fontsize: 9pt # Optional: Force one-column layout, default is two-column one_column: false # Optional: Enable section numbering, default is unnumbered numbersections: true # Optional: Specify the depth of section number, default is 5 secnumdepth: 5 # Optional: Bibliography bibliography: Rcpp # Customize footer, eg by referencing the vignette footer_contents: "RcppGSL Vignette" # Produce a pinp document output: pinp::pinp # Extra definitions in header header-includes: > \newcommand{\proglang}[1]{\textsf{#1}} \newcommand{\pkg}[1]{\textbf{#1}} # Include bibliography material directly (from .bbl file) include-after: | \begin{thebibliography}{12} \newcommand{\enquote}[1]{``#1''} \providecommand{\natexlab}[1]{#1} \providecommand{\url}[1]{\texttt{#1}} \providecommand{\urlprefix}{URL } \expandafter\ifx\csname urlstyle\endcsname\relax \providecommand{\doi}[1]{doi:\discretionary{}{}{}#1}\else \providecommand{\doi}{doi:\discretionary{}{}{}\begingroup \urlstyle{rm}\Url}\fi \providecommand{\eprint}[2][]{\url{#2}} \bibitem[{Allaire \emph{et~al.}(2017)Allaire, Eddelbuettel, and Fran\c{c}ois}]{CRAN:Rcpp:Attributes} Allaire JJ, Eddelbuettel D, Fran\c{c}ois R (2017). \newblock \emph{{Rcpp} Attributes}. \newblock Vignette included in R package Rcpp, \urlprefix\url{http://CRAN.R-Project.org/package=Rcpp}. \bibitem[{Bates and Eddelbuettel(2013)}]{JSS:RcppEigen} Bates D, Eddelbuettel D (2013). \newblock \enquote{Fast and Elegant Numerical Linear Algebra Using the {RcppEigen} Package.} \newblock \emph{Journal of Statistical Software}, \textbf{52}(5), 1--24. \newblock \urlprefix\url{http://www.jstatsoft.org/v52/i05/}. \bibitem[{Bates \emph{et~al.}(2016)Bates, Fran\c{c}ois, and Eddelbuettel}]{CRAN:RcppEigen} Bates D, Fran\c{c}ois R, Eddelbuettel D (2016). \newblock \emph{RcppEigen: Rcpp integration for the Eigen templated linear algebra library}. \newblock {R} package version 0.3.2.9.0, \urlprefix\url{http://CRAN.R-Project.org/package=RcppEigen}. \bibitem[{Eddelbuettel(2013)}]{Eddelbuettel:2013:Rcpp} Eddelbuettel D (2013). \newblock \emph{Seamless R and C++ Integration with Rcpp}. \newblock Use R! Springer, New York. \newblock ISBN 978-1-4614-6867-7. \bibitem[{Eddelbuettel and Fran\c{c}ois(2011)}]{JSS:Rcpp} Eddelbuettel D, Fran\c{c}ois R (2011). \newblock \enquote{{Rcpp}: Seamless {R} and {C++} Integration.} \newblock \emph{Journal of Statistical Software}, \textbf{40}(8), 1--18. \newblock \urlprefix\url{http://www.jstatsoft.org/v40/i08/}. \bibitem[{Eddelbuettel \emph{et~al.}(2017)Eddelbuettel, Fran\c{c}ois, Allaire, Ushey, Kou, Russel, Chambers, and Bates}]{CRAN:Rcpp} Eddelbuettel D, Fran\c{c}ois R, Allaire J, Ushey K, Kou Q, Russel N, Chambers J, Bates D (2017). \newblock \emph{{Rcpp}: Seamless {R} and {C++} Integration}. \newblock R package version 0.12.12, \urlprefix\url{http://CRAN.R-Project.org/package=Rcpp}. \bibitem[{Eddelbuettel \emph{et~al.}(2016)Eddelbuettel, Fran\c{c}ois, and Bates}]{CRAN:RcppArmadillo} Eddelbuettel D, Fran\c{c}ois R, Bates D (2016). \newblock \emph{RcppArmadillo: Rcpp integration for Armadillo templated linear algebra library}. \newblock R package version 0.7.400.2.0, \urlprefix\url{http://CRAN.R-Project.org/package=RcppArmadillo}. \bibitem[{Eddelbuettel and Sanderson(2014)}]{Eddelbuettel+Sanderson:2013:RcppArmadillo} Eddelbuettel D, Sanderson C (2014). \newblock \enquote{{RcppArmadillo}: Accelerating {R} with High-Performance {C++} Linear Algebra.} \newblock \emph{Computational Statistics and Data Analysis}, \textbf{71}, 1054--1063. \newblock \doi{10.1016/j.csda.2013.02.005}. \newblock \urlprefix\url{http://dx.doi.org/10.1016/j.csda.2013.02.005}. \bibitem[{Galassi \emph{et~al.}(2010)Galassi, Davies, Theiler, Gough, Jungman, Alken, Booth, and Rossi}]{GSL} Galassi M, Davies J, Theiler J, Gough B, Jungman G, Alken P, Booth M, Rossi F (2010). \newblock \emph{{GNU} {S}cientific {L}ibrary {R}eference {M}anual}, 3rd edition. \newblock Version 1.14. {ISBN} 0954612078, \urlprefix\url{http://www.gnu.org/software/gsl}. \bibitem[{{R Core Team}(2017)}]{R:Main} {R Core Team} (2017). \newblock \emph{R: A Language and Environment for Statistical Computing}. \newblock R Foundation for Statistical Computing, Vienna, Austria. \newblock \urlprefix\url{https://www.R-project.org/}. \bibitem[{Sanderson(2010)}]{Sanderson:2010:Armadillo} Sanderson C (2010). \newblock \enquote{{Armadillo}: {An} open source {C++} Algebra Library for Fast Prototyping and Computationally Intensive Experiments.} \newblock \emph{Technical report}, {NICTA}. \newblock \urlprefix\url{http://arma.sf.net}. \bibitem[{Sklyar \emph{et~al.}(2015)Sklyar, Murdoch, Smith, Eddelbuettel, and Fran\c{c}ois}]{CRAN:inline} Sklyar O, Murdoch D, Smith M, Eddelbuettel D, Fran\c{c}ois R (2015). \newblock \emph{inline: Inline C, C++, Fortran function calls from R}. \newblock R package version 0.3.14, \urlprefix\url{http://CRAN.R-Project.org/package=inline}. \end{thebibliography} # Required: Vignette metadata for inclusion in a package. vignette: > %\VignetteIndexEntry{RcppGSL} %\VignetteKeywords{R,GSL,Rcpp,data transfer} %\VignetteDepends{RcppGSL} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} --- \section{Introduction} The GNU Scientific Library, or \pkg{GSL}, is a collection of numerical routines for scientific computing \citep{GSL}. It is a rigourously developed and tested library providing support for a wide range of scientific or numerical tasks. Among the topics covered in the \pkg{GSL} are complex numbers, roots of polynomials, special functions, vector and matrix data structures, permutations, combinations, sorting, BLAS support, linear algebra, 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 least-squares fitting, minimization, physical constants, basis splines and wavelets. Support for \proglang{C} programming with the \pkg{GSL} is available as the \pkg{GSL} itself is written in \proglang{C}, and provides a \proglang{C}-language Application Programming Interface (API). Access from \proglang{C++} is possible, albeit not at an abstraction level that could be offered by dedicated \proglang{C++} implementations. Several \proglang{C++} wrappers for the \pkg{GSL} have been written over the years; none reached a state of completion comparable to the \pkg{GSL} itself. The \pkg{GSL} combines broad coverage of scientific topics, serious implementation effort, and the use of the well-known GNU General Public License (GPL). This has lead to fairly wide usage of the library. As a concrete example, we can consider the Comprehensive R Archive Network (CRAN) repository network for the \proglang{R} language and environment \citep{R:Main}. CRAN contains over three dozen packages interfacing the \pkg{GSL}. Of these more than half interface the vector or matrix classes as shown in Table \ref{tab:useOfGSLatCRAN}. This provides empirical evidence indicating that the \pkg{GSL} is popular among programmers using either the \proglang{C} or \proglang{C++} language for solving problems applied science. \begin{table} \centering \begin{small} \begin{tabular}{lccc} \toprule Package & Any \texttt{gsl} header & \texttt{gsl\_vector.h} & \texttt{gsl\_matrix.h} \\ \midrule abn & $\star$ & $\star$ & $\star$ \\ BayesLogit & $\star$ & & \\ BayesSAE & $\star$ & $\star$ & $\star$ \\ BayesVarSel & $\star$ & $\star$ & $\star$ \\ BH & $\star$ & $\star$ & \\ bnpmr & $\star$ & & \\ BNSP & $\star$ & $\star$ & $\star$ \\ cghseg & $\star$ & $\star$ & $\star$ \\ cit & $\star$ & & \\ diversitree & $\star$ & & $\star$ \\ eco & $\star$ & & \\ geoCount & $\star$ & & \\ graphscan & $\star$ & & \\ gsl & $\star$ & $\star$ & \\ gstat & $\star$ & & \\ hgm & $\star$ & & \\ HiCseg & $\star$ & & $\star$ \\ igraph & $\star$ & & \\ KFKSDS & $\star$ & $\star$ & $\star$ \\ libamtrack & $\star$ & & \\ mixcat & $\star$ & $\star$ & $\star$ \\ mvabund & $\star$ & $\star$ & $\star$ \\ outbreaker & $\star$ & $\star$ & $\star$ \\ R2GUESS & $\star$ & $\star$ & $\star$ \\ RCA & $\star$ & & \\ RcppGSL & $\star$ & $\star$ & $\star$ \\ RcppSMC & $\star$ & & \\ RcppZiggurat & $\star$ & & \\ RDieHarder & $\star$ & $\star$ & $\star$ \\ ridge & $\star$ & $\star$ & $\star$ \\ Rlibeemd & $\star$ & $\star$ & \\ Runuran & $\star$ & & \\ SemiCompRisks & $\star$ & & $\star$ \\ simplexreg & $\star$ & $\star$ & $\star$ \\ stsm & $\star$ & $\star$ & $\star$ \\ survSNP & $\star$ & & \\ TKF & $\star$ & $\star$ & $\star$ \\ topicmodels & $\star$ & $\star$ & $\star$ \\ VBLPCM & $\star$ & $\star$ & \\ VBmix & $\star$ & $\star$ & $\star$ \\ \bottomrule \end{tabular} \end{small} \caption{CRAN Package Usage of \pkg{GSL} outright, for vectors and for matrices.} \label{tab:useOfGSLatCRAN} \begin{flushleft} \footnotesize \textsl{Note:} Data gathered in late July 2015 by use of \texttt{grep} searching (recursively) for inclusion of any GSL header, or the vector and matrix headers specifically, within the \texttt{src/} or \texttt{inst/include/} directories of expanded source code archives of the CRAN network. Convenient (temporary) shell access to such an expanded code archive via WU Vienna is gratefully acknowledged. \end{flushleft} \end{table} At the same time, the \pkg{Rcpp} package \citep{JSS:Rcpp,Eddelbuettel:2013:Rcpp,CRAN:Rcpp} offers a higher-level interface between \proglang{R} and \proglang{C++}. \pkg{Rcpp} permits \proglang{R} objects like vectors, matrices, lists, functions, environments, $\ldots$, to be manipulated directly at the \proglang{C++} level, and alleviates the needs for complicated and error-prone parameter passing and memory allocation. It also allows compact vectorised expressions similar to what can be written in \proglang{R} directly at the \proglang{C++} level. The \pkg{RcppGSL} package discussed here aims to close the gap. It offers access to \pkg{GSL} functions, in particular via the vector and matrix data structures used throughout the \pkg{GSL}, while staying closer to the `whole object model' familar to the \proglang{R} programmer. The rest of paper is organised as follows. The next section shows a motivating example of a fast linear model fit routine using \pkg{GSL} functions. The following section discusses support for \pkg{GSL} vector types, which is followed by a section on matrices. The following two section discusses error handling, and then use of \pkg{RcppGSL} in your own package. This is followed by short discussions of how to use \pkg{RcppGSL} with \pkg{inline} and \textsl{Rcpp Attributes}, respectively, before a short concluding summary. \section{Motivation: fastLm} Fitting linear models is a key building block of analysing and modeling data. \proglang{R} has a very complete and feature-rich function in \texttt{lm()} which provides a model fit as well as a number of diagnostic measure, either directly or via the \texttt{summary()} method for linear model fits. The \texttt{lm.fit()} function provides a faster alternative (which is however recommend only for for advanced users) which provides estimates only and fewer statistics for inference. This may lead to user requests for a routine which is both fast and featureful enough. The \texttt{fastLm} routine shown here provides such an implementation as part of the \pkg{RcppGSL} package. It uses the \pkg{GSL} for the least-squares fitting functions and provides a nice example for \pkg{GSL} integration with \proglang{R}. ```cpp #include #include #include // declare a dependency on the RcppGSL package; // also activates plugin (but not needed when // 'LinkingTo: RcppGSL' is used with a package) // // [[Rcpp::depends(RcppGSL)]] // tell Rcpp to turn this into a callable // function called 'fastLm' // // [[Rcpp::export]] Rcpp::List fastLm(const RcppGSL::Matrix & X, const RcppGSL::Vector & y) { // row and column dimension int n = X.nrow(), k = X.ncol(); double chisq; // to hold the coefficient vector RcppGSL::Vector coef(k); // and the covariance matrix RcppGSL::Matrix cov(k,k); // the actual fit requires working memory // which we allocate and then free gsl_multifit_linear_workspace *work = gsl_multifit_linear_alloc (n, k); gsl_multifit_linear (X, y, coef, cov, &chisq, work); gsl_multifit_linear_free (work); // assign diagonal to a vector, then take // square roots to get std.error Rcpp::NumericVector std_err; // need two step decl. and assignment std_err = gsl_matrix_diagonal(cov); // sqrt() is an Rcpp sugar function std_err = Rcpp::sqrt(std_err); return Rcpp::List::create( Rcpp::Named("coefficients") = coef, Rcpp::Named("stderr") = std_err, Rcpp::Named("df.residual") = n - k); } ``` The function interface defines two \pkg{RcppGSL} variables: a matrix and a vector. Both use the standard numeric type \texttt{double} as discussed below. The \pkg{GSL} supports other types ranging from lower precision floating point to signed and unsigned integers as well as complex numbers. The vector and matrix classes are templated for use with all these \proglang{C} / \proglang{C++} types---though \proglang{R} uses only \texttt{double} and \texttt{int}. For these latter two, we offer a shorthand definition via a \texttt{typedef} which allows a shorter non-template use. Having extracted the row and column dimentions, we then reserve another vector and matrix to hold the resulting coefficient estimates as well as the estimate of the covariance matrix. Next, we allocate workspace using a \pkg{GSL} routine, fit the linear model and free the just-allocated workspace. The next step involves extracting the diagonal element from the covariance matrix, and taking the square root (using a vectorised function from \pkg{Rcpp}). Finally we create a named list with the return values. In earlier version of the \pkg{RcppGSL} package, we also explicitly called \texttt{free()} to return temporary memory allocation to the operating system. This step had to be done as the underlying objects are managed as \proglang{C} objects. They conform to the \pkg{GSL} interface, and work without any automatic memory management. But as we provide a \proglang{C++} data structure for matrix and vector objects, we can manage them using \proglang{C++} facilities. In particular, the destructor can free the memory when the object goes out of scope. Explicit \texttt{free()} calls are still permitted as we keep track the object status so that memory cannot accidentally be released more than once. Another more recent addition permits use of \texttt{const \&} in the interface. This instructs the compiler that values of the corresponding variable will not be altered, and are passed into the function by reference rather than by value. We note that \pkg{RcppArmadillo} \citep{CRAN:RcppArmadillo,Eddelbuettel+Sanderson:2013:RcppArmadillo} implements a matching \texttt{fastLm} function using the Armadillo library by \cite{Sanderson:2010:Armadillo}, and can do so with even more compact code due to \proglang{C++} features. Moreover, \pkg{RcppEigen} \citep{CRAN:RcppEigen,JSS:RcppEigen} provides a \texttt{fastLm} implementation with a comprehensive comparison of matrix decomposition methods. # Vectors This section details the different vector represenations, starting with their definition inside the \pkg{GSL}. We then discuss our layering before showing how the two types map. A discussion of read-only `vector view' classes concludes the section. ## \pkg{GSL} Vectors \pkg{GSL} defines various vector types to manipulate one-dimensionnal data, similar to \proglang{R} arrays. For example the \verb|gsl_vector| and \verb|gsl_vector_int| structs are defined as: ```cpp typedef struct{ size_t size; size_t stride; double * data; gsl_block * block; int owner; } gsl_vector; typedef struct { size_t size; size_t stride; int * data; gsl_block_int * block; int owner; } gsl_vector_int; ``` A typical use of the \verb|gsl_vector| struct is given below: ```cpp int i; // allocate a gsl_vector of size 3 gsl_vector *v = gsl_vector_alloc(3); // fill the vector for (i = 0; i < 3; i++) { gsl_vector_set(v, i, 1.23 + i); } // access elements double sum = 0.0; for (i = 0; i < 3; i++) { sum += gsl_vector_set(v, i); } // free the memory gsl_vector_free(v); ``` Note that we have to explicitly free the allocated memory at the end. With \proglang{C}-style programming, this step is always the responsibility of the programmer. ## RcppGSL::vector \pkg{RcppGSL} defines the template \texttt{RcppGSL::vector} to manipulate \verb|gsl_vector| pointers taking advantage of C++ templates. Using this template type, the previous example now becomes: ```cpp int i; // allocate a gsl_vector of size 3 RcppGSL::vector v(3); // fill the vector for (i = 0; i < 3; i++) { v[i] = 1.23 + i; } // access elements double sum = 0.0; for (i = 0; i < 3; i++) { sum += v[i]; } // (optionally) free the memory // also automatic when out of scope v.free(); ``` The class \texttt{RcppGSL::vector} is a smart pointer which can be deployed anywhere where a raw pointer \verb|gsl_vector| can be used, such as the \verb|gsl_vector_set| and \verb|gsl_vector_get| functions above. Beyond the convenience of a nicer syntax for allocation (and of course the managed release of memory either via \texttt{free()} or when going out of scope), the \texttt{RcppGSL::vector} template faciliates interchange of \pkg{GSL} vectors with \pkg{Rcpp} objects, and hence \pkg{R} objects. The following example defines a \texttt{.Call} compatible function called \verb|sum_gsl_vector_int| that operates on a \verb|gsl_vector_int| through the \texttt{RcppGSL::vector} template specialization: ```cpp // [[Rcpp::export]] int sum_gsl_vector_int(const RcppGSL::vector & vec) { int res = std::accumulate(vec.begin(), vec.end(), 0); return res; } ``` Here we no longer need to call \texttt{free()} explicitly as the \texttt{vec} allocation is returned automatically at the end of the function body when the variable goes out of scope. Once the function has created via \texttt{sourceCpp()} or \texttt{cppFunction()} from \textsl{Rcpp Attributes} (see section \ref{sec:attributes} for more on this), it can then be called from \proglang{R} : ```{r inlineex1} fx <- Rcpp::cppFunction(" int sum_gsl_vector_int(RcppGSL::vector vec) { int res = std::accumulate(vec.begin(), vec.end(), 0); return res; }", depends="RcppGSL") sum_gsl_vector_int(1:10) ``` A second example shows a simple function that grabs elements of an R list as \verb|gsl_vector| objects using implicit conversion mechanisms of \pkg{Rcpp} ```cpp // [[Rcpp::export]] double gsl_vector_sum_2(const Rcpp::List & data) { // grab "x" as a gsl_vector through the // RcppGSL::vector class const RcppGSL::vector x = data["x"]; // grab "y" as a gsl_vector through the // RcppGSL::vector class const RcppGSL::vector y = data["y"]; double res = 0.0; for (size_t i=0; i< x->size; i++) { res += x[i] * y[i]; } // return result, memory freed automatically return res; } ``` called from \proglang{R}: ```{r inlinexex2} Rcpp::cppFunction(" double gsl_vector_sum_2(Rcpp::List data) { RcppGSL::vector x = data[\"x\"]; RcppGSL::vector y = data[\"y\"]; double res = 0.0; for (size_t i=0; i< x->size; i++) { res += x[i] * y[i]; } return res; }", depends= "RcppGSL") data <- list( x = seq(0,1,length=10), y = 1:10 ) gsl_vector_sum_2(data) ``` ## Mapping Table \ref{tab:mappingVectors} shows the mapping between types defined by the \pkg{GSL} and their corresponding types in the \pkg{RcppGSL} package. \begin{table*}[htb] \centering \begin{small} \begin{tabular}{ll} \toprule GSL vector & RcppGSL \\ \midrule \texttt{gsl\_vector} & \texttt{RcppGSL::vector} as well as \texttt{RcppGSL::Vector} \\ \texttt{gsl\_vector\_int} & \texttt{RcppGSL::vector} as well as \texttt{RcppGSL::IntVector} \\ \texttt{gsl\_vector\_float} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_long} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_char} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_complex} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_complex\_float} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_complex\_long\_double} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_long\_double} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_short} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_uchar} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_uint} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_ushort} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_ulong} & \texttt{RcppGSL::vector} \\ \bottomrule \end{tabular} \end{small} \caption{Correspondance between \pkg{GSL} vector types and templates defined in \pkg{RcppGSL}.} \label{tab:mappingVectors} \end{table*} As shown, we also define two convenient shortcuts for the very common case of \texttt{double} and \texttt{int} vectors. First, \texttt{RcppGSL::Vector} is a short-hand for the \texttt{RcppGSL::vector} template instantiation. Second, \texttt{RcppGSL::IntVector} does the same for integer-valued vectors. Other types still require explicit templates. ## Vector Views Several \pkg{GSL} algorithms return \pkg{GSL} vector views as their result type. \pkg{RcppGSL} defines the template class \texttt{RcppGSL::vector\_view} to handle vector views using \proglang{C++} syntax. ```cpp // [[Rcpp::export]] Rcpp::List test_gsl_vector_view() { int n = 10; RcppGSL::vector v(n); for (int i=0 ; i v_even = gsl_vector_subvector_with_stride(v,0,2,n/2); const RcppGSL::vector_view v_odd = gsl_vector_subvector_with_stride(v,1,2,n/2); return Rcpp::List::create( Rcpp::Named("even") = v_even, Rcpp::Named("odd" ) = v_odd); } ``` As with vectors, \proglang{C++} objects of type \texttt{RcppGSL::vector\_view} can be converted implicitly to their associated \pkg{GSL} view type. Table \ref{tab:mappingVectorViews} displays the pairwise correspondance so that the \proglang{C++} objects can be passed to compatible \pkg{GSL} algorithms. \begin{table*}[htb] \centering \begin{small} \begin{tabular}{ll} \toprule gsl vector views & RcppGSL \\ \midrule \texttt{gsl\_vector\_view} & \texttt{RcppGSL::vector\_view}; \texttt{RcppGSL::VectorView} \\ \texttt{gsl\_vector\_view\_int} & \texttt{RcppGSL::vector\_view}; \texttt{RcppGSL::IntVectorView} \\ \texttt{gsl\_vector\_view\_float} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_long} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_char} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_complex} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_complex\_float} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_complex\_long\_double} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_long\_double} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_short} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_uchar} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_uint} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_ushort} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_ulong} & \texttt{RcppGSL::vector\_view} \\ \bottomrule \end{tabular} \end{small} \caption{Correspondance between \pkg{GSL} vector view types and templates defined in \pkg{RcppGSL}.} \label{tab:mappingVectorViews} \end{table*} The vector view class also contains a conversion operator to automatically transform the data of the view object to a \pkg{GSL} vector object. This enables use of vector views where \pkg{GSL} would expect a vector. And as before, \texttt{double} and \texttt{int} types can be accessed via the \texttt{typedef} variants \texttt{RcppGSL::VectorView} and \texttt{RcppGSL::IntVectorView}, respectively. Lastly, in order to support \texttt{const \&} behaviour, all \texttt{gsl\_vector\_XXX\_const\_view} variants are also supported (where \texttt{XXX} stands for any of the atomistic \proglang{C} and \proglang{C++} data types). # Matrices The \pkg{GSL} also defines a set of matrix data types : \texttt{gsl\_matrix}, \texttt{gsl\_matrix\_int} etc ... for which \pkg{RcppGSL} defines a corresponding convenience \proglang{C++} wrapper generated by the \texttt{RcppGSL::matrix} template. ## Creating matrices The \texttt{RcppGSL::matrix} template exposes three constructors. ```cpp // convert an R matrix to a GSL matrix matrix(SEXP x) throw(::Rcpp::not_compatible) // encapsulate a GSL matrix pointer matrix(gsl_matrix* x) // create a new matrix with the given // number of rows and columns matrix(int nrow, int ncol) ``` ## Implicit conversion \texttt{RcppGSL::matrix} defines an implicit conversion to a pointer to the associated \pkg{GSL} matrix type, as well as dereferencing operators. This makes the class \texttt{RcppGSL::matrix} look and feel like a pointer to a \pkg{GSL} matrix type. ```cpp gsltype* data; operator gsltype*() { return data; } gsltype* operator->() const { return data; } gsltype& operator*() const { return *data; } ``` ## Indexing Indexing of \pkg{GSL} matrices is usually the task of the functions \texttt{gsl\_matrix\_get}, \texttt{gsl\_matrix\_int\_get}, ... and \texttt{gsl\_matrix\_set}, \texttt{gsl\_matrix\_int\_set}, ... \pkg{RcppGSL} takes advantage of both operator overloading and templates to make indexing a \pkg{GSL} matrix much more convenient. ```cpp // create a matrix of size 10x10 RcppGSL::matrix mat(10,10); // fill the diagonal, no need for setter function for (int i=0; i<10: i++) { mat(i,i) = i; } ``` ## Methods The \texttt{RcppGSL::matrix} type also defines the following member functions: \begin{quote} \begin{itemize} \item[\texttt{nrow}] extracts the number of rows \item[\texttt{ncol}] extract the number of columns \item[\texttt{size}] extracts the number of elements \item[\texttt{free}] releases the memory (also called via destructor) \end{itemize} \end{quote} ## Matrix views Similar to the vector views discussed above, the \pkg{RcppGSL} also provides an implicit conversion operator which returns the underlying matrix stored in the matrix view class. ## Error handler When input values for \pkg{GSL} functions are invalid, the default error handler will abort the program after printing an error message. This leads \proglang{R} to an abortion error. To avoid this behaviour, one needs to avoid it first by using \texttt{gsl\_set\_error\_handler\_off()}, and then detect error conditions by checking whether the result is \texttt{NAN} or not. ```cpp // close the GSL error handler gsl_set_error_handler_off(); // call GSL function with some invalid values double res = gsl_sf_hyperg_2F1(1, 1, 1.1467003, 1); // detect the result is NAN or not if (ISNAN(res)) { Rcpp::Rcout << "Invalid input found!" << std::endl; } ``` See \url{http://thread.gmane.org/gmane.comp.lang.r.rcpp/7905} for a longer discussion of the related issues. Starting with release 0.2.4, two new functions are available: \texttt{gslSetErrorHandlerOff()} and \texttt{gslResetErrorHandler()} which allow to turn off the error handler (as discussed above), and to reset to the prior (default) value. In addition, the package now also calls \texttt{gslSetErrorHandlerOff()} when being attached, ensuring that the \pkg{GSL} error handler is turned off by default. # Using \pkg{RcppGSL} in your package The \pkg{RcppGSL} package contains a complete example package providing a single function \texttt{colNorm} which computes a norm for each column of a supplied matrix. This example adapts a matrix example from the \pkg{GSL} manual that has been chosen primarily as a means to showing how to set up a package to use \pkg{RcppGSL}. Needless to say, we could compute such a matrix norm easily in \proglang{R} using existing facilities. One such possibility is a simple \verb|apply(M, 2, function(x) sqrt(sum(x^2)))| as shown on the corresponding help page in the example package inside \pkg{RcppGSL}. One point in favour of using the \pkg{GSL} code is that it employs a BLAS function so on sufficiently large matrices, and with suitable BLAS libraries installed, this variant could be faster due to the optimised code in high-performance BLAS libraries and/or the inherent parallelism a multi-core BLAS variant which compute compute the vector norm in parallel. On all `reasonable' matrix sizes, however, the performance difference should be neglible. ## The \texttt{configure} script ### Using autoconf Using \pkg{RcppGSL} means employing both the \pkg{GSL} and \proglang{R}. We may need to find the location of the \pkg{GSL} headers and library, and this done easily from a \texttt{configure} source script which \texttt{autoconf} generates from a \texttt{configure.in} source file such as the following: ```sh AC_INIT([RcppGSLExample], 0.1.0) ## Use gsl-config to find arguments for ## compiler and linker flags ## ## Check for non-standard programs: gsl-config(1) AC_PATH_PROG([GSL_CONFIG], [gsl-config]) ## If gsl-config was found, let's use it if test "${GSL_CONFIG}" != ""; then # Use gsl-config for header and linker args # (without BLAS which we get from R) GSL_CFLAGS=`${GSL_CONFIG} --cflags` GSL_LIBS=`${GSL_CONFIG} --libs-without-cblas` else AC_MSG_ERROR([gsl-config not found, is GSL installed?]) fi # Now substitute these variables in src/Makevars.in to create src/Makevars AC_SUBST(GSL_CFLAGS) AC_SUBST(GSL_LIBS) AC_OUTPUT(src/Makevars) ``` A source file such as this \texttt{configure.in} gets converted into a script \texttt{configure} by invoking the \texttt{autoconf} program. We note that many other libraries use a similar (but somewhat newer and by-now fairly standard) scripting frontend called \texttt{pkg-config} which be deployed in a very similar by other packages. Calls such as the following two can be used from \texttt{configure} in a very similar manner: ```sh pkg-config --cflags libpng pkg-config --libs libpng ``` where \texttt{libpng} (for the png image format) is used just for illustration. ### Using functions provided by RcppGSL \pkg{RcppGSL} provides R functions (in the file \texttt{R/inline.R}) that allow us to retrieve the same information. Therefore the configure script can also be written as: ```sh #!/bin/sh GSL_CFLAGS=`${R_HOME}/bin/Rscript -e \ "RcppGSL:::CFlags()"` GSL_LIBS=`${R_HOME}/bin/Rscript -e \ "RcppGSL:::LdFlags()"` sed -e "s|@GSL_LIBS@|${GSL_LIBS}|" \ -e "s|@GSL_CFLAGS@|${GSL_CFLAGS}|" \ src/Makevars.in > src/Makevars ``` Similarly, the configure.win for windows can be written as: ```sh GSL_CFLAGS=`${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe\ -e "RcppGSL:::CFlags()"` GSL_LIBS=`${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe \ -e "RcppGSL:::LdFlags()"` sed -e "s|@GSL_LIBS@|${GSL_LIBS}|" \ -e "s|@GSL_CFLAGS@|${GSL_CFLAGS}|" \ src/Makevars.in > src/Makevars.win ``` This allows for a simpler and more direct way to just set the compile and link options, taking advantage of the installed \pkg{RcppGSL} package. See the \pkg{RcppZiggurat} package for an example. ## The \texttt{src} directory The \proglang{C++} source file takes the matrix supplied from \proglang{R} and applies the \pkg{GSL} function to each column. ```cpp #include #include #include // [[Rcpp::export]] Rcpp::NumericVector colNorm(const RcppGSL::Matrix & G) { int k = G.ncol(); Rcpp::NumericVector n(k); // results for (int j = 0; j < k; j++) { RcppGSL::vector_view colview = gsl_matrix_const_column(G, j); n[j] = gsl_blas_dnrm2(colview); } return n; // return } ``` The \proglang{Makevars.in} file governs the compilation and uses the values supplied by \texttt{configure} during build-time: ```sh # set by configure GSL_CFLAGS = @GSL_CFLAGS@ GSL_LIBS = @GSL_LIBS@ # combine with standard arguments for R PKG_CPPFLAGS = $(GSL_CFLAGS) PKG_LIBS = $(GSL_LIBS) ``` The variables surrounded by \@ will be filled by \texttt{configure} during package build-time. As discussed above, this can either rely on \texttt{autoconf} or a possibly-simpler \texttt{Rscript}. ## The \texttt{R} directory The \proglang{R} source is very simply: it contains a single file created by the \texttt{Rcpp::compileAttributes()} function implementing the wrapper to the \texttt{colNorm()} function. # Using \pkg{RcppGSL} with \pkg{inline} The \pkg{inline} package \citep{CRAN:inline} is very helpful for prototyping code in \proglang{C}, \proglang{C++} or \proglang{Fortran} as it takes care of code compilation, linking and dynamic loading directly from \proglang{R}. It has been used extensively by \pkg{Rcpp}, for example in the numerous unit tests. The example below shows how \pkg{inline} can be deployed with \pkg{RcppGSL}. We implement the same column norm example, but this time as an \proglang{R} script which is compiled, linked and loaded on-the-fly. Compared to standard use of \pkg{inline}, we have to make sure to add a short section declaring which header files from \pkg{GSL} we need to use; the \pkg{RcppGSL} then communicates with \pkg{inline} to tell it about the location and names of libraries used to build code against \pkg{GSL}. ```cpp require(inline) inctxt=' #include #include ' bodytxt=' // create data structures from SEXP RcppGSL::matrix M = sM; int k = M.ncol(); // to store results Rcpp::NumericVector n(k); for (int j = 0; j < k; j++) { RcppGSL::vector_view colview = gsl_matrix_column (M, j); n[j] = gsl_blas_dnrm2(colview); } return n; ' foo <- cxxfunction( signature(sM="numeric"), body=bodytxt, inc=inctxt, plugin="RcppGSL") # see Section 8.4.13 of the GSL manual: # create M as a sum of two outer products M <- outer(sin(0:9), rep(1,10), "*") + outer(rep(1, 10), cos(0:9), "*") foo(M) ``` The \texttt{RcppGSL} inline plugin supports creation of a package skeleton based on the inline function. ```{r exskel, eval=FALSE} package.skeleton("mypackage", foo) ``` # Using \pkg{RcppGSL} with Rcpp Attributes \label{sec:attributes} \textsl{Rcpp Attributes} \citep{CRAN:Rcpp:Attributes} builds on the features of the \pkg{inline} package described in previous section, and streamlines the compilation, loading and linking process even further. It leverages the existing plugins for \pkg{inline}. We already showed the corresponding function in the previous section. Here, we show it again as a self-contained example used via \texttt{sourceCpp()}. We stress that usage is \texttt{sourceCpp()} is meant for interactive work at the R command-prompt, but is not the recommended practice in a package. ```cpp #include #include #include // declare a dependency on the RcppGSL package; // also activates plugin // // [[Rcpp::depends(RcppGSL)]] // declare the function to be 'exported' to R // // [[Rcpp::export]] Rcpp::NumericVector colNorm(const RcppGSL::Matrix & M) { int k = M.ncol(); Rcpp::NumericVector n(k); // results for (int j = 0; j < k; j++) { RcppGSL::VectorView colview = gsl_matrix_const_column (M, j); n[j] = gsl_blas_dnrm2(colview); } return n; // return } /*** R ## see Section 8.4.13 of the GSL manual: ## create M as a sum of two outer products M <- outer(sin(0:9), rep(1,10), "*") + outer(rep(1, 10), cos(0:9), "*") colNorm(M) */ ``` With the code above stored in a file, say, ``gslNorm.cpp'' one can simply call \texttt{sourceCpp()} to have the wrapper code added, and all of the compilation, linking and loading done --- including the execution of the short \proglang{R} segment at the end: ```{r exnorm, eval=FALSE} sourceCpp("gslNorm.cpp") ``` The function \texttt{cppFunction()} is also available to convert a simple character string argument containing a valid C++ function into a eponymous R function. And like \texttt{sourceCpp()}, it can also use plugins. See the vignette ``Rcpp-attributes'' \citep{CRAN:Rcpp:Attributes} of the \pkg{Rcpp} package \citep{CRAN:Rcpp} for full details. # Summary The GNU Scientific Library (GSL) by \citet{GSL} offers a very comprehensive collection of rigorously developed and tested functions for applied scientific computing under a widely-used and well-understood Open Source license. This has lead to widespread deployment of \pkg{GSL} among a number of disciplines. Using the automatic wrapping and converters offered by the \pkg{RcppGSL} package presented here, \proglang{R} users and programmers can now deploy algorithmns provided by the \pkg{GSL} with greater ease. \newpage RcppGSL/inst/doc/RcppGSL-intro.R0000644000176200001440000000170113161737653016007 0ustar liggesusers## ----inlineex1----------------------------------------------------------- fx <- Rcpp::cppFunction(" int sum_gsl_vector_int(RcppGSL::vector vec) { int res = std::accumulate(vec.begin(), vec.end(), 0); return res; }", depends="RcppGSL") sum_gsl_vector_int(1:10) ## ----inlinexex2---------------------------------------------------------- Rcpp::cppFunction(" double gsl_vector_sum_2(Rcpp::List data) { RcppGSL::vector x = data[\"x\"]; RcppGSL::vector y = data[\"y\"]; double res = 0.0; for (size_t i=0; i< x->size; i++) { res += x[i] * y[i]; } return res; }", depends= "RcppGSL") data <- list( x = seq(0,1,length=10), y = 1:10 ) gsl_vector_sum_2(data) ## ----exskel, eval=FALSE-------------------------------------------------- # package.skeleton("mypackage", foo) ## ----exnorm, eval=FALSE-------------------------------------------------- # sourceCpp("gslNorm.cpp") RcppGSL/inst/doc/RcppGSL-unitTests.Rnw0000644000176200001440000000362712774327533017235 0ustar liggesusers\documentclass[10pt]{article} %\VignetteIndexEntry{RcppGSL-unitTests} %\VignetteKeywords{R,GSL,Rcpp,unit tests} %\VignettePackage{RcppGSL} \usepackage{vmargin} \setmargrb{0.75in}{0.75in}{0.75in}{0.75in} \RequirePackage{ae,mathpple} % ae as a default font pkg works with Sweave \RequirePackage[T1]{fontenc} <>= require(RcppGSL) prettyVersion <- packageDescription("RcppGSL")$Version prettyDate <- format(Sys.Date(), "%B %e, %Y") library(RUnit) @ \usepackage[colorlinks]{hyperref} \author{Dirk Eddelbuettel \and Romain Fran\c{c}ois} \title{\texttt{RcppGSL}: Unit testing results} \date{\texttt{RcppGSL} version \Sexpr{prettyVersion} as of \Sexpr{prettyDate}} \begin{document} \maketitle \section*{Test Execution} <>= pkg <- "RcppGSL" if (file.exists("unitTests-results")) unlink("unitTests-results", recursive = TRUE) dir.create("unitTests-results") path <- system.file("unitTests", package=pkg) testSuite <- defineTestSuite(name=paste(pkg, "unit testing"), dirs=path) tests <- runTestSuite(testSuite) err <- getErrors(tests) if (err$nFail > 0) stop(sprintf("unit test problems: %d failures", err$nFail)) if (err$nErr > 0) stop( sprintf("unit test problems: %d errors", err$nErr)) printHTMLProtocol(tests, fileName= sprintf("unitTests-results/%s-unitTests.html", pkg)) printTextProtocol(tests, fileName= sprintf("unitTests-results/%s-unitTests.txt" , pkg)) #if (file.exists("/tmp")) { # invisible(sapply(c("txt", "html"), function(ext) { # fname <- sprintf("unitTests-results/%s-unitTests.%s", pkg, ext) # file.copy(fname, "/tmp", overwrite=TRUE) # })) #} @ \section*{Test Results} \begin{verbatim} <>= results <- "unitTests-results/RcppGSL-unitTests.txt" if (file.exists(results)) { writeLines(readLines(results)) } else{ writeLines( "unit test results not available" ) } @ \end{verbatim} \end{document} RcppGSL/inst/doc/RcppGSL-unitTests.R0000644000176200001440000000321313161737707016656 0ustar liggesusers### R code from vignette source 'RcppGSL-unitTests.Rnw' ################################################### ### code chunk number 1: RcppGSL-unitTests.Rnw:12-16 ################################################### require(RcppGSL) prettyVersion <- packageDescription("RcppGSL")$Version prettyDate <- format(Sys.Date(), "%B %e, %Y") library(RUnit) ################################################### ### code chunk number 2: unitTesting ################################################### pkg <- "RcppGSL" if (file.exists("unitTests-results")) unlink("unitTests-results", recursive = TRUE) dir.create("unitTests-results") path <- system.file("unitTests", package=pkg) testSuite <- defineTestSuite(name=paste(pkg, "unit testing"), dirs=path) tests <- runTestSuite(testSuite) err <- getErrors(tests) if (err$nFail > 0) stop(sprintf("unit test problems: %d failures", err$nFail)) if (err$nErr > 0) stop( sprintf("unit test problems: %d errors", err$nErr)) printHTMLProtocol(tests, fileName= sprintf("unitTests-results/%s-unitTests.html", pkg)) printTextProtocol(tests, fileName= sprintf("unitTests-results/%s-unitTests.txt" , pkg)) #if (file.exists("/tmp")) { # invisible(sapply(c("txt", "html"), function(ext) { # fname <- sprintf("unitTests-results/%s-unitTests.%s", pkg, ext) # file.copy(fname, "/tmp", overwrite=TRUE) # })) #} ################################################### ### code chunk number 3: importResults ################################################### results <- "unitTests-results/RcppGSL-unitTests.txt" if (file.exists(results)) { writeLines(readLines(results)) } else{ writeLines( "unit test results not available" ) } RcppGSL/inst/unitTests/0000755000176200001440000000000012774327533014520 5ustar liggesusersRcppGSL/inst/unitTests/runit.fastLm.R0000644000176200001440000001117112774327533017232 0ustar liggesusers#!/usr/bin/r -t # Emacs make this -*- mode: R; tab-width: 4 -*- # # Copyright (C) 2010 - 2015 Romain Francois and Dirk Eddelbuettel # # This file is part of RcppGSL. # # RcppGSL is free software: you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # # RcppGSL is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with RcppGSL. If not, see . test.fastLm <- function() { data(trees, package="datasets") flm <- .Call("RcppGSL_fastLm", cbind(1, log(trees$Girth)), log(trees$Volume), PACKAGE="RcppGSL") fit <- lm(log(Volume) ~ log(Girth), data=trees) checkEquals( as.numeric(flm$coef), as.numeric(coef(fit)), msg="fastLm.coef") checkEquals( as.numeric(flm$stderr), as.numeric(coef(summary(fit))[,2]), msg="fastLm.stderr") checkEquals(as.numeric(flm$df.residual), as.numeric(fit$df.residual), msg="fastLm.df.residual") } test.fastLm.default <- function() { data(trees, package="datasets") flm <- RcppGSL:::fastLm.default(cbind(1, log(trees$Girth)), log(trees$Volume)) fit <- lm(log(Volume) ~ log(Girth), data=trees) checkEquals(as.numeric(flm$coefficients), as.numeric(coef(fit)), msg="fastLm.default.coef") checkEquals(as.numeric(flm$stderr), as.numeric(coef(summary(fit))[,2]), msg="fastLm.default.stderr") checkEquals(as.numeric(flm$df.residual), as.numeric(fit$df.residual), msg="fastLm.default.df.residual") checkEquals(as.numeric(flm$residuals), as.numeric(fit$residuals), msg="fastLm.default.residuals") checkEquals(as.numeric(flm$fitted.values), as.numeric(fit$fitted.values), msg="fastLm.default.fitted.values") } test.summary.fastLm <- function() { data(trees, package="datasets") sflm <- summary(fastLm(log(Volume) ~ log(Girth), data=trees)) sfit <- summary(lm(log(Volume) ~ log(Girth), data=trees)) checkEquals(as.numeric(coef(sflm)), as.numeric(coef(sfit)), msg="summary.fastLm.coef") checkEquals(sflm$r.squared, sfit$r.squared, msg="summary.fastLm.r.squared") checkEquals(sflm$adj.r.squared, sfit$adj.r.squared, msg="summary.fastLm.r.squared") checkEquals(sflm$sigma, sfit$sigma, msg="summary.fastLm.sigma") ## no intercept case sflm <- summary(fastLm(log(Volume) ~ log(Girth) - 1, data=trees)) sfit <- summary(lm(log(Volume) ~ log(Girth) - 1, data=trees)) checkEquals(as.numeric(coef(sflm)), as.numeric(coef(sfit)), msg="summary.fastLm.coef.noint") checkEquals(sflm$r.squared, sfit$r.squared, msg="summary.fastLm.r.squared.noint") checkEquals(sflm$adj.r.squared, sfit$adj.r.squared, msg="summary.fastLm.r.squared.noint") checkEquals(sflm$sigma, sfit$sigma, msg="summary.fastLm.sigma.noint") ## non-formula use sflm <- summary(fastLm(log(trees$Girth), log(trees$Volume))) sfit <- summary(lm(log(Volume) ~ log(Girth) - 1, data=trees)) checkEquals(as.numeric(coef(sflm)), as.numeric(coef(sfit)), msg="summary.fastLm.coef.nonform") checkEquals(sflm$r.squared, sfit$r.squared, msg="summary.fastLm.r.squared.nonform") checkEquals(sflm$adj.r.squared, sfit$adj.r.squared, msg="summary.fastLm.r.squared.nonform") checkEquals(sflm$sigma, sfit$sigma, msg="summary.fastLm.sigma.nonform") } test.fastLm.formula <- function() { data(trees, package="datasets") flm <- fastLm(log(Volume) ~ log(Girth), data=trees) fit <- lm(log(Volume) ~ log(Girth), data=trees) checkEquals(flm$coefficients, coef(fit), msg="fastLm.formula.coef") checkEquals(as.numeric(flm$stderr), as.numeric(coef(summary(fit))[,2]), msg="fastLm.formula.stderr") checkEquals(as.numeric(flm$df.residual), as.numeric(fit$df.residual), msg="fastLm.formula.df.residual") checkEquals(as.numeric(flm$residuals), as.numeric(fit$residuals), msg="fastLm.formula.residuals") checkEquals(as.numeric(flm$fitted.values), as.numeric(fit$fitted.values), msg="fastLm.formula.fitted.values") } RcppGSL/inst/unitTests/cpp/0000755000176200001440000000000012774327533015302 5ustar liggesusersRcppGSL/inst/unitTests/cpp/gsl.cpp0000644000176200001440000003126112774327533016576 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; indent-tabs-mode: nil; -*- // // gsl.cpp: RcppGSL R integration of GSL via Rcpp -- unit tests // // Copyright (C) 2010 - 2013 Romain Francois and Dirk Eddelbuettel // // This file is part of RcppGSL. // // RcppGSL is free software: you can redistribute it and/or modify it // under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 2 of the License, or // (at your option) any later version. // // RcppGSL is distributed in the hope that it will be useful, but // WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with RcppGSL. If not, see . #include using namespace Rcpp; // [[Rcpp::depends(RcppGSL)]] // [[Rcpp::export]] List test_gsl_vector_wrapper() { RcppGSL::vector x_double( 10 ); RcppGSL::vector x_float( 10 ); RcppGSL::vector x_int( 10 ); //RcppGSL::vector x_long( 10 ); RcppGSL::vector x_char( 10 ); RcppGSL::vector x_long_double( 10 ); RcppGSL::vector x_short( 10 ); RcppGSL::vector x_uchar( 10 ); RcppGSL::vector x_uint( 10 ); RcppGSL::vector x_ushort( 10 ); //RcppGSL::vector x_ulong( 10 ); RcppGSL::vector x_complex( 10 ); RcppGSL::vector x_complex_float( 10 ); RcppGSL::vector x_complex_long_double( 10 ); List res = List::create(_["gsl_vector"] = x_double, _["gsl_vector_float"] = x_float, _["gsl_vector_int"] = x_int, //_["gsl_vector_long"] = x_long, _["gsl_vector_char"] = x_char, _["gsl_vector_complex"] = x_complex, _["gsl_vector_complex_float"] = x_complex_float, _["gsl_vector_complex_long_double"] = x_complex_long_double, _["gsl_vector_long_double"] = x_long_double, _["gsl_vector_short"] = x_short, _["gsl_vector_uchar"] = x_uchar, _["gsl_vector_uint"] = x_uint, _["gsl_vector_ushort"] = x_ushort //,_["gsl_vector_ulong"] = x_ulong ); x_double.free(); x_float.free(); x_int.free(); //x_long.free(); x_char.free(); x_long_double.free(); x_short.free(); x_uchar.free(); x_uint.free(); x_ushort.free(); //x_ulong.free(); x_complex.free(); x_complex_float.free(); x_complex_long_double.free(); return res; } // [[Rcpp::export]] List test_gsl_vector() { gsl_vector * x_double = gsl_vector_calloc (10); gsl_vector_float * x_float = gsl_vector_float_calloc(10); gsl_vector_int * x_int = gsl_vector_int_calloc(10); //gsl_vector_long * x_long = gsl_vector_long_calloc(10); gsl_vector_char * x_char = gsl_vector_char_calloc(10); gsl_vector_complex * x_complex = gsl_vector_complex_calloc(10); gsl_vector_complex_float * x_complex_float = gsl_vector_complex_float_calloc(10); gsl_vector_complex_long_double * x_complex_long_double = gsl_vector_complex_long_double_calloc(10); gsl_vector_long_double * x_long_double = gsl_vector_long_double_calloc(10); gsl_vector_short * x_short = gsl_vector_short_calloc(10); gsl_vector_uchar * x_uchar = gsl_vector_uchar_calloc(10); gsl_vector_uint * x_uint = gsl_vector_uint_calloc(10); gsl_vector_ushort * x_ushort = gsl_vector_ushort_calloc(10); //gsl_vector_ulong * x_ulong = gsl_vector_ulong_calloc(10); /* create an R list containing copies of gsl data */ List res = List::create(_["gsl_vector"] = *x_double, _["gsl_vector_float"] = *x_float, _["gsl_vector_int"] = *x_int, //_["gsl_vector_long"] = *x_long, _["gsl_vector_char"] = *x_char, _["gsl_vector_complex"] = *x_complex, _["gsl_vector_complex_float"] = *x_complex_float, _["gsl_vector_complex_long_double"] = *x_complex_long_double, _["gsl_vector_long_double"] = *x_long_double, _["gsl_vector_short"] = *x_short, _["gsl_vector_uchar"] = *x_uchar, _["gsl_vector_uint"] = *x_uint, _["gsl_vector_ushort"] = *x_ushort //,_["gsl_vector_ulong"] = *x_ulong ); /* cleanup gsl data */ gsl_vector_free(x_double); gsl_vector_float_free( x_float); gsl_vector_int_free( x_int ); //gsl_vector_long_free( x_long ); gsl_vector_char_free( x_char ); gsl_vector_complex_free( x_complex ); gsl_vector_complex_float_free( x_complex_float ); gsl_vector_complex_long_double_free( x_complex_long_double ); gsl_vector_long_double_free( x_long_double ); gsl_vector_short_free( x_short ); gsl_vector_uchar_free( x_uchar ); gsl_vector_uint_free( x_uint ); gsl_vector_ushort_free( x_ushort ); //gsl_vector_ulong_free( x_ulong ); return res; } // [[Rcpp::export]] List test_gsl_matrix() { gsl_matrix * x_double = gsl_matrix_alloc(5, 2); gsl_matrix_set_identity( x_double ); gsl_matrix_float * x_float = gsl_matrix_float_alloc(5,2); gsl_matrix_float_set_identity( x_float ); gsl_matrix_int * x_int = gsl_matrix_int_alloc(5,2); gsl_matrix_int_set_identity( x_int ); //gsl_matrix_long * x_long = gsl_matrix_long_alloc(5,2); //gsl_matrix_long_set_identity( x_long ); gsl_matrix_char * x_char = gsl_matrix_char_alloc(5,2); gsl_matrix_char_set_identity( x_char ); gsl_matrix_complex * x_complex = gsl_matrix_complex_alloc(5,2); gsl_matrix_complex_set_identity( x_complex ); gsl_matrix_complex_float * x_complex_float = gsl_matrix_complex_float_alloc(5,2); gsl_matrix_complex_float_set_identity( x_complex_float ); gsl_matrix_complex_long_double * x_complex_long_double = gsl_matrix_complex_long_double_alloc(5,2); gsl_matrix_complex_long_double_set_identity( x_complex_long_double ); gsl_matrix_long_double * x_long_double = gsl_matrix_long_double_alloc(5,2); gsl_matrix_long_double_set_identity( x_long_double ); gsl_matrix_short * x_short = gsl_matrix_short_alloc(5,2); gsl_matrix_short_set_identity( x_short ); gsl_matrix_uchar * x_uchar = gsl_matrix_uchar_alloc(5,2); gsl_matrix_uchar_set_identity( x_uchar ); gsl_matrix_uint * x_uint = gsl_matrix_uint_alloc(5,2); gsl_matrix_uint_set_identity( x_uint); gsl_matrix_ushort * x_ushort = gsl_matrix_ushort_alloc(5,2); gsl_matrix_ushort_set_identity( x_ushort ); //gsl_matrix_ulong * x_ulong = gsl_matrix_ulong_alloc(5,2); //gsl_matrix_ulong_set_identity( x_ulong ); List res = List::create(_["gsl_matrix"] = *x_double , _["gsl_matrix_float"] = *x_float, _["gsl_matrix_int"] = *x_int, //_["gsl_matrix_long"] = *x_long, _["gsl_matrix_char"] = *x_char, _["gsl_matrix_complex"] = *x_complex, _["gsl_matrix_complex_float"] = *x_complex_float, _["gsl_matrix_complex_long_double"] = *x_complex_long_double, _["gsl_matrix_long_double"] = *x_long_double, _["gsl_matrix_short"] = *x_short, _["gsl_matrix_uchar"] = *x_uchar, _["gsl_matrix_uint"] = *x_uint, _["gsl_matrix_ushort"] = *x_ushort //,_["gsl_matrix_ulong"] = *x_ulong ); gsl_matrix_free( x_double ); gsl_matrix_float_free( x_float); gsl_matrix_int_free( x_int ); //gsl_matrix_long_free( x_long ); gsl_matrix_char_free( x_char ); gsl_matrix_complex_free( x_complex ); gsl_matrix_complex_float_free( x_complex_float ); gsl_matrix_complex_long_double_free( x_complex_long_double ); gsl_matrix_long_double_free( x_long_double ); gsl_matrix_short_free( x_short ); gsl_matrix_uchar_free( x_uchar ); gsl_matrix_uint_free( x_uint ); gsl_matrix_ushort_free( x_ushort ); //gsl_matrix_ulong_free( x_ulong ); return res; } // [[Rcpp::export]] List test_gsl_vector_view() { int n = 10; gsl_vector *v = gsl_vector_calloc (n); for( int i=0; i vec = as< RcppGSL::vector >(vec_); int n = vec->size; double res = 0.0; for( int i=0; i mat = as< RcppGSL::matrix >( mat_); int nr = mat->size1; double res = 0.0; for( int i=0; i vec(10); for( int i=0; i<10; i++){ gsl_vector_int_set( vec, i, i ); } Rcpp::IntegerVector x; x = vec; return x; } // [[Rcpp::export]] NumericVector test_gsl_vector_indexing(NumericVector vec_) { RcppGSL::vector vec = as< RcppGSL::vector >(vec_); for( size_t i=0; i< vec.size(); i++){ vec[i] = vec[i] + 1.0; } NumericVector res = Rcpp::wrap( vec ); vec.free(); return res; } // [[Rcpp::export]] double test_gsl_vector_iterating(NumericVector vec_) { RcppGSL::vector vec = as< RcppGSL::vector >(vec_); double res= std::accumulate( vec.begin(), vec.end(), 0.0 ); vec.free(); return res; } // [[Rcpp::export]] NumericVector test_gsl_vector_iterator_transform(NumericVector vec_) { RcppGSL::vector vec = as< RcppGSL::vector >(vec_); NumericVector res(vec.size()); std::transform(vec.begin(), vec.end(), res.begin(), ::sqrt); vec.free(); return res; } // [[Rcpp::export]] NumericMatrix test_gsl_matrix_indexing(NumericMatrix mat_) { RcppGSL::matrix mat= as< RcppGSL::matrix >( mat_ ); for( size_t i=0; i< mat.nrow(); i++){ for( size_t j=0; j< mat.ncol(); j++){ mat(i,j) = mat(i,j) + 1.0; } } Rcpp::NumericMatrix res = Rcpp::wrap(mat); mat.free(); return res; } // [[Rcpp::export]] List test_gsl_vector_view_wrapper() { int n = 10; RcppGSL::vector vec( 10 ); for( int i=0; i v_even = gsl_vector_const_subvector_with_stride(vec, 0, 2, n/2); RcppGSL::vector_view v_odd = gsl_vector_const_subvector_with_stride(vec, 1, 2, n/2); List res = List::create(_["even"] = v_even, _["odd" ] = v_odd); vec.free(); return res; } // [[Rcpp::export]] List test_gsl_matrix_view_wrapper() { int nrow = 4; int ncol = 6; RcppGSL::matrix m(nrow, ncol); int k =0; for( int i=0; i x = gsl_matrix_const_submatrix(m, 2, 2, 2, 2 ); List res = List::create(_["full"] = m, _["view"] = x); m.free(); return res; } // [[Rcpp::export]] double test_gsl_vector_view_iterating(NumericVector vec_) { RcppGSL::vector vec = as< RcppGSL::vector >(vec_); int n = vec.size(); RcppGSL::vector_view v_even = gsl_vector_const_subvector_with_stride(vec, 0, 2, n/2); double res = std::accumulate( v_even.begin(), v_even.end(), 0.0 ); return res; } // [[Rcpp::export]] double test_gsl_matrix_view_indexing() { int nr = 10; int nc = 10; RcppGSL::matrix mat( nr, nc ); int k = 0; for( size_t i=0; i< mat.nrow(); i++){ for( size_t j=0; j< mat.ncol(); j++, k++){ mat(i,j) = k; } } RcppGSL::matrix_view x = gsl_matrix_const_submatrix(mat, 2, 2, 2, 2 ); double res = 0.0; for( size_t i=0; i. .setUp <- function() { if (!exists("pathRcppTests")) pathRcppTests <- system.file("unitTests", package="RcppGSL") sourceCpp(file.path(pathRcppTests, "cpp/gsl.cpp")) } test.gsl.vector.wrappers <- function(){ fx <- test_gsl_vector_wrapper res <- fx() checkEquals(res, list("gsl_vector" = numeric(10), "gsl_vector_float" = numeric(10), "gsl_vector_int" = integer(10), ##"gsl_vector_long" = numeric(10), "gsl_vector_char" = raw(10), "gsl_vector_complex" = complex(10), "gsl_vector_complex_float" = complex(10), "gsl_vector_complex_long_double" = complex(10), "gsl_vector_long_double" = numeric(10), "gsl_vector_short" = integer(10), "gsl_vector_uchar" = raw(10), "gsl_vector_uint" = integer(10), "gsl_vector_ushort" = integer(10) ##,"gsl_vector_ulong" = numeric(10) ), msg = "wrap( gsl_vector )" ) } test.gsl.vector <- function(){ fx <- test_gsl_vector res <- fx() checkEquals(res, list("gsl_vector" = numeric(10), "gsl_vector_float" = numeric(10), "gsl_vector_int" = integer(10), ##"gsl_vector_long" = numeric(10), "gsl_vector_char" = raw(10), "gsl_vector_complex" = complex(10), "gsl_vector_complex_float" = complex(10), "gsl_vector_complex_long_double" = complex(10), "gsl_vector_long_double" = numeric(10), "gsl_vector_short" = integer(10), "gsl_vector_uchar" = raw(10), "gsl_vector_uint" = integer(10), "gsl_vector_ushort" = integer(10) ##,"gsl_vector_ulong" = numeric(10) ), msg = "wrap( gsl_vector )" ) } test.gsl.matrix <- function(){ helper <- function(what){ as.what <- get( paste( "as.", deparse(substitute(what)), sep = "" ) ) x <- what(10) x[1] <- as.what(1) x[7] <- as.what(1) dim( x ) <- c(5,2) x } fx <- test_gsl_matrix res <- fx() checkEquals(res, list("gsl_matrix" = helper( numeric ), "gsl_matrix_float" = helper( numeric ), "gsl_matrix_int" = helper( integer ), ##"gsl_matrix_long" = helper( numeric ), "gsl_matrix_char" = helper( raw ), "gsl_matrix_complex" = helper( complex ), "gsl_matrix_complex_float" = helper( complex ), "gsl_matrix_complex_long_double" = helper( complex ), "gsl_matrix_long_double" = helper( numeric ), "gsl_matrix_short" = helper( integer ), "gsl_matrix_uchar" = helper( raw ), "gsl_matrix_uint" = helper( integer ), "gsl_matrix_ushort" = helper( integer ) ##,"gsl_matrix_ulong" = helper( numeric ) ), msg = "wrap( gsl_matrix )" ) } test.gsl.vector.view <- function(){ fx <- test_gsl_vector_view res <- fx() checkEquals(res, list( even = 2.0 * 0:4, odd = 2.0 * 0:4 + 1.0 ), msg = "wrap( gsl.vector.view )" ) fx <- test_gsl_vector_view_wrapper res <- fx() checkEquals( res, list( even = 2.0 * 0:4, odd = 2.0 * 0:4 + 1.0 ), msg = "wrap( gsl.vector.view.wrapper )" ) } test.gsl.matrix.view <- function(){ fx <- test_gsl_matrix_view res <- fx() checkEquals( res$full[3:4, 3:4], res$view, msg = "wrap(gsl.matrix.view)" ) fx <- test_gsl_matrix_view_wrapper res <- fx() checkEquals( res$full[3:4, 3:4], res$view, msg = "wrap(gsl.matrix.view.wrapper)" ) } test.gsl.vector.input.SEXP <- function(){ x <- rnorm( 10 ) fx <- test_gsl_vector_input res <- fx(x) checkEquals( res, sum(x), msg = "RcppGSL::vector(SEXP)" ) } test.gsl.matrix.input.SEXP <- function(){ x <- matrix( rnorm(20), nc = 4 ) fx <- test_gsl_matrix_input res <- fx( x) checkEquals( res, sum(x[,1]), msg = "RcppGSL::matrix(SEXP)" ) } test.gsl.RcppGSL.vector <- function(){ fx <- test_gsl_vector_conv res <- fx() checkEquals( res, 0:9, msg = "RcppGSL::vector -> IntegerVector" ) } test.gsl.RcppGSL.vector.indexing <- function(){ fx <- test_gsl_vector_indexing res <- fx( seq(0.5, 10.5) ) checkEquals( res, seq( 1.5, 11.5 ) ) } test.gsl.RcppGSL.vector.iterating <- function(){ x <- seq(0.5, 10.5) fx <- test_gsl_vector_iterating res <- fx(x) checkEquals( res, sum(x) ) } test.gsl.RcppGSL.vector.iterator.transform <- function() { x <- seq(0.5, 10.5) fx <- test_gsl_vector_iterator_transform res <- fx(x) checkEquals(res, sqrt(x)) } test.gsl.RcppGSL.matrix.indexing <- function(){ m <- matrix( 1:16+.5, nr = 4 ) fx <- test_gsl_matrix_indexing res <- fx(m) checkEquals( res, m+1 ) } test.gsl.RcppGSL.vector.view.iterating <- function(){ x <- seq(1.5, 10.5) fx <- test_gsl_vector_view_iterating res <- fx(x) checkEquals( res, sum( x[ seq(1, length(x), by = 2 ) ] ) ) } test.gsl.RcppGSL.matrix.view.indexing <- function(){ fx <- test_gsl_matrix_view_indexing res <- fx() checkEquals( res, 110.0 ) } RcppGSL/inst/unitTests/runit.client.package.R0000644000176200001440000000321712774327533020656 0ustar liggesusers#!/usr/bin/r -t # # Copyright (C) 2010 Romain Francois and Dirk Eddelbuettel # Copyright (C) 2014 - 2015 Dirk Eddelbuettel # # This file is part of RcppGSL. # # RcppGSL is free software: you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # # RcppGSL is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with RcppGSL. If not, see . test.client.package <- function(){ pkg <- "RcppGSLExample" td <- tempfile() cwd <- getwd() dir.create(td) file.copy(system.file("examples", pkg, package = "RcppGSL" ) , td, recursive = TRUE) Sys.chmod(file.path(td, "RcppGSLExample", "configure"), "0755") setwd(td) on.exit( { setwd(cwd) unlink(td, recursive = TRUE) } ) R <- shQuote(file.path(R.home(component = "bin" ), "R")) cmd <- paste(R , "CMD build", pkg) system(cmd) dir.create("templib") install.packages(paste(pkg, "_0.0.3.tar.gz", sep = ""), "templib", repos = NULL, type = "source") require(Rcpp) require(pkg, "templib", character.only = TRUE) m <- matrix(1:16, nc = 4) res <- colNorm(m) val <- apply(m, 2, function(x) sqrt(sum(x^2))) unlink("templib", recursive = TRUE) checkEquals(res, val, msg = "colNorm in client package") } RcppGSL/inst/skeleton/0000755000176200001440000000000012774327533014342 5ustar liggesusersRcppGSL/inst/skeleton/Makevars.in0000644000176200001440000000025013036762600016426 0ustar liggesusers GSL_CFLAGS = @GSL_CFLAGS@ GSL_LIBS = @GSL_LIBS@ # combine with standard arguments for R PKG_CPPFLAGS = -W $(GSL_CFLAGS) -I../inst/include PKG_LIBS += $(GSL_LIBS) RcppGSL/inst/skeleton/configure0000755000176200001440000000036213036762530016242 0ustar liggesusers#!/bin/sh GSL_CFLAGS=`${R_HOME}/bin/Rscript -e "RcppGSL:::CFlags()"` GSL_LIBS=`${R_HOME}/bin/Rscript -e "RcppGSL:::LdFlags()"` sed -e "s|@GSL_LIBS@|${GSL_LIBS}|" \ -e "s|@GSL_CFLAGS@|${GSL_CFLAGS}|" \ src/Makevars.in > src/Makevars RcppGSL/inst/skeleton/configure.win0000644000176200001440000000041613036762553017040 0ustar liggesusersGSL_CFLAGS=`${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe -e "RcppGSL:::CFlags()"` GSL_LIBS=`${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe -e "RcppGSL:::LdFlags()"` sed -e "s|@GSL_LIBS@|${GSL_LIBS}|" \ -e "s|@GSL_CFLAGS@|${GSL_CFLAGS}|" \ src/Makevars.in > src/Makevars.win RcppGSL/inst/skeleton/Makevars.win0000644000176200001440000000014013036762740016620 0ustar liggesusersPKG_CPPFLAGS=-I$(LIB_GSL)/include -I../inst/include PKG_LIBS=-L$(LIB_GSL)/lib -lgsl -lgslcblas RcppGSL/inst/include/0000755000176200001440000000000012774327533014141 5ustar liggesusersRcppGSL/inst/include/RcppGSLForward.h0000644000176200001440000002330112774327533017110 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; indent-tabs-mode: nil; -*- // // RcppGSLForward.h: Forward Declarations for Seamless R and GSL Integration // // Copyright (C) 2010 - 2015 Romain Francois and Dirk Eddelbuettel // // This file is part of RcppGSL. // // RcppGSL is free software: you can redistribute it and/or modify it // under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 2 of the License, or // (at your option) any later version. // // RcppGSL is distributed in the hope that it will be useful, but // WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with RcppGSL. If not, see . #ifndef RCPPGSL__RCPPGSLFORWARD_H #define RCPPGSL__RCPPGSLFORWARD_H #include #include #include namespace Rcpp { namespace traits { /* support for gsl_complex */ template<> struct r_sexptype_traits { enum { rtype = CPLXSXP }; }; template<> struct wrap_type_traits { typedef wrap_type_primitive_tag wrap_category; }; template<> struct r_type_traits { typedef r_type_primitive_tag r_category; }; template<> struct r_type_traits< std::pair > { typedef r_type_primitive_tag r_category; }; /* support for gsl_complex_float */ template<> struct r_sexptype_traits { enum { rtype = CPLXSXP }; }; template<> struct wrap_type_traits { typedef wrap_type_primitive_tag wrap_category; }; template<> struct r_type_traits { typedef r_type_primitive_tag r_category; }; template<> struct r_type_traits< std::pair > { typedef r_type_primitive_tag r_category; }; /* support for gsl_complex_long_double */ template<> struct r_sexptype_traits { enum { rtype = CPLXSXP }; }; template<> struct wrap_type_traits { typedef wrap_type_primitive_tag wrap_category; }; template<> struct r_type_traits { typedef r_type_primitive_tag r_category; }; template<> struct r_type_traits< std::pair > { typedef r_type_primitive_tag r_category; }; } namespace internal { template<> inline gsl_complex caster(Rcomplex from) { gsl_complex x; GSL_REAL(x) = from.r; GSL_IMAG(x) = from.i; return x; } template<> inline Rcomplex caster(gsl_complex from) { Rcomplex x; x.r = GSL_REAL(from); x.i = GSL_IMAG(from); return x; } template<> inline gsl_complex_float caster(Rcomplex from) { gsl_complex_float x; GSL_REAL(x) = static_cast(from.r); GSL_IMAG(x) = static_cast(from.i); return x; } template<> inline Rcomplex caster(gsl_complex_float from) { Rcomplex x; x.r = static_cast(GSL_REAL(from)); x.i = static_cast(GSL_IMAG(from)); return x; } template<> inline gsl_complex_long_double caster(Rcomplex from) { gsl_complex_long_double x; GSL_REAL(x) = static_cast(from.r); GSL_IMAG(x) = static_cast(from.i); return x; } template<> inline Rcomplex caster(gsl_complex_long_double from) { Rcomplex x; x.r = static_cast(GSL_REAL(from)); x.i = static_cast(GSL_IMAG(from)); return x; } } } namespace RcppGSL { template class vector; template class matrix; template struct vector_view_type; template struct matrix_view_type; #include "RcppGSL_types.h" _RCPPGSL_SPEC_NOSUFFIX(double , double ) _RCPPGSL_SPEC(float , _float , float ) _RCPPGSL_SPEC(int , _int , int ) //_RCPPGSL_SPEC(long , _long , long ) _RCPPGSL_SPEC(long double , _long_double , long double ) _RCPPGSL_SPEC(short , _short , short ) _RCPPGSL_SPEC(unsigned char , _uchar , unsigned char ) _RCPPGSL_SPEC(unsigned int , _uint , unsigned int ) _RCPPGSL_SPEC(unsigned short , _ushort , unsigned short ) //_RCPPGSL_SPEC(unsigned long , _ulong , unsigned long ) _RCPPGSL_SPEC(char , _char , unsigned char ) _RCPPGSL_SPEC(gsl_complex , _complex , gsl_complex ) _RCPPGSL_SPEC(gsl_complex_float , _complex_float , gsl_complex_float ) _RCPPGSL_SPEC(gsl_complex_long_double , _complex_long_double , gsl_complex_long_double) #undef _RCPPGSL_SPEC #undef _RCPPGSL_SPEC_NOSUFFIX template class vector_view { public: struct internal_view { const gsl_vector vector; inline internal_view(const gsl_vector &v) : vector(v) {} }; typedef typename vector::type type; typedef typename vector::const_iterator const_iterator; typedef typename vector::gsltype gsltype; typedef typename vector_view_type::type view_type; typedef typename vector_view_type::const_type const_view_type; typedef typename vector::ConstProxy ConstProxy; vector_view(view_type v) : view(v.vector) {} vector_view(const_view_type v) : view(v.vector) {} inline ConstProxy operator[](int i) { return ConstProxy(&view.vector, i); } inline const_iterator begin() const { return const_iterator(ConstProxy(&view.vector, 0)); } inline const_iterator end() const { return const_iterator(ConstProxy(&view.vector, view.vector.size)); } inline size_t size() const { return view.vector.size; } inline operator const gsltype*() { return &view.vector; } internal_view view; }; template class matrix_view { public: struct internal_view { const gsl_matrix matrix; inline internal_view(const gsl_matrix &m) : matrix(m) {} }; typedef typename matrix::type type; typedef typename matrix::gsltype gsltype; typedef typename matrix_view_type::type view_type; typedef typename matrix_view_type::const_type const_view_type; typedef typename matrix::ConstProxy ConstProxy; matrix_view(view_type v) : view(v.matrix) {} matrix_view(const_view_type v) : view(v.matrix) {} inline ConstProxy operator()(int row, int col) { return ConstProxy(&view.matrix, row, col); } inline size_t nrow() const { return view.matrix.size1; } inline size_t ncol() const { return view.matrix.size2; } inline size_t size() const { return view.matrix.size1 * view.matrix.size2; } inline operator const gsltype*() { return &view.matrix; } internal_view view; }; } /* forward declarations */ namespace Rcpp { #undef _RCPPGSL_WRAPDEF #define _RCPPGSL_WRAPDEF(__SUFFIX__) \ template<> inline SEXP wrap(const gsl_vector##__SUFFIX__&); \ template<> inline SEXP wrap(const gsl_vector##__SUFFIX__##_view&); \ template<> inline SEXP wrap(const gsl_vector##__SUFFIX__##_const_view&); \ template<> inline SEXP wrap(const gsl_matrix##__SUFFIX__&); \ template<> inline SEXP wrap(const gsl_matrix##__SUFFIX__##_view&); \ template<> inline SEXP wrap(const gsl_matrix##__SUFFIX__##_const_view&); _RCPPGSL_WRAPDEF(_int) _RCPPGSL_WRAPDEF(_float) _RCPPGSL_WRAPDEF(_long) _RCPPGSL_WRAPDEF(_char) _RCPPGSL_WRAPDEF(_complex) _RCPPGSL_WRAPDEF(_complex_float) _RCPPGSL_WRAPDEF(_complex_long_double) _RCPPGSL_WRAPDEF(_long_double) _RCPPGSL_WRAPDEF(_short) _RCPPGSL_WRAPDEF(_uchar) _RCPPGSL_WRAPDEF(_uint) _RCPPGSL_WRAPDEF(_ushort) _RCPPGSL_WRAPDEF(_ulong) template<> inline SEXP wrap(const gsl_vector&); template<> inline SEXP wrap(const gsl_vector_view&); template<> inline SEXP wrap(const gsl_vector_const_view&); template<> inline SEXP wrap(const gsl_matrix&); template<> inline SEXP wrap(const gsl_matrix_view&); template<> inline SEXP wrap(const gsl_matrix_const_view&); template SEXP wrap(const ::RcppGSL::vector&); template SEXP wrap(const ::RcppGSL::matrix&); template SEXP wrap(const ::RcppGSL::vector_view&); template SEXP wrap(const ::RcppGSL::matrix_view&); } #endif RcppGSL/inst/include/RcppGSL_matrix_view.h0000644000176200001440000000461312774327533020206 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; indent-tabs-mode: nil; -*- // // RcppGSL_matrix_view.h: Matrix view class for Seamless R and GSL Integration // // Copyright (C) 2010 - 2015 Romain Francois and Dirk Eddelbuettel // // This file is part of RcppGSL. // // RcppGSL is free software: you can redistribute it and/or modify it // under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 2 of the License, or // (at your option) any later version. // // RcppGSL is distributed in the hope that it will be useful, but // WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with RcppGSL. If not, see . #ifndef RCPPGSL__RCPPGSL_MATRIX_VIEW_H #define RCPPGSL__RCPPGSL_MATRIX_VIEW_H #include #include namespace Rcpp{ #define RCPPGSL_VIEW(SUFFIX) \ template <> SEXP wrap(const gsl_matrix##SUFFIX##_view& x) { \ return wrap(x.matrix); \ } \ template <> SEXP wrap( const gsl_matrix##SUFFIX##_const_view& x ){ \ return wrap(x.matrix) ; \ } RCPPGSL_VIEW(_int) RCPPGSL_VIEW(_float) RCPPGSL_VIEW(_long) RCPPGSL_VIEW(_char) RCPPGSL_VIEW(_complex) RCPPGSL_VIEW(_complex_float) RCPPGSL_VIEW(_complex_long_double) RCPPGSL_VIEW(_long_double) RCPPGSL_VIEW(_short) RCPPGSL_VIEW(_uchar) RCPPGSL_VIEW(_uint) RCPPGSL_VIEW(_ushort) RCPPGSL_VIEW(_ulong) #undef RCPPGSL_VIEW #define RCPPGSL_VIEW(SUFFIX) template <> SEXP wrap(const gsl_matrix_view& x) { return wrap(x.matrix); } template <> SEXP wrap(const gsl_matrix_const_view& x) { return wrap(x.matrix); } template SEXP wrap(const ::RcppGSL::matrix_view& x) { return wrap(x.view.matrix); } } #endif RcppGSL/inst/include/RcppGSL_types.h0000644000176200001440000006402012774327533017012 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; indent-tabs-mode: nil; -*- // // RcppGSL_types.h: Type macros for Seamless R and GSL Integration // // Copyright (C) 2010 - 2015 Romain Francois and Dirk Eddelbuettel // // This file is part of RcppGSL. // // RcppGSL is free software: you can redistribute it and/or modify it // under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 2 of the License, or // (at your option) any later version. // // RcppGSL is distributed in the hope that it will be useful, but // WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with RcppGSL. If not, see . #ifndef RCPPGSL_RCPPGSL_SPEC_H #define RCPPGSL_RCPPGSL_SPEC_H #undef _RCPPGSL_SPEC #undef _RCPPGSL_SPEC_NOSUFFIX #define _RCPPGSL_SPEC(__T__,__SUFFIX__,__CAST__) \ template <> struct vector_view_type<__T__> { \ typedef gsl_vector##__SUFFIX__##_view type; \ typedef gsl_vector##__SUFFIX__##_const_view const_type; \ }; \ template <> struct matrix_view_type<__T__> { \ typedef gsl_matrix##__SUFFIX__##_view type; \ typedef gsl_matrix##__SUFFIX__##_const_view const_type; \ }; \ template <> class vector<__T__> { \ public: \ typedef __T__ type; \ typedef __T__* pointer; \ typedef gsl_vector##__SUFFIX__ gsltype; \ gsltype* data; \ class Proxy { \ public: \ Proxy(gsltype* data_, int index_) : \ index(index_), parent(data_) {} \ Proxy& operator=(type x) { \ gsl_vector##__SUFFIX__##_set(parent, index, x); \ return *this; \ } \ inline operator type() { \ return gsl_vector##__SUFFIX__##_get(parent, index); \ } \ inline operator const type() const { \ return gsl_vector##__SUFFIX__##_get(parent, index); \ } \ int index; \ gsltype* parent; \ inline void move(int d) { index += d; } \ }; \ class ConstProxy { \ public: \ ConstProxy(const gsltype* data_, int index_) : \ index(index_), parent(data_) {} \ inline operator type() { \ return gsl_vector##__SUFFIX__##_get(parent, index); \ } \ inline operator const type() const { \ return gsl_vector##__SUFFIX__##_get(parent, index); \ } \ int index; \ const gsltype* parent; \ inline void move(int d) { index += d; } \ }; \ typedef ::Rcpp::internal::Proxy_Iterator iterator; \ typedef ::Rcpp::internal::Proxy_Iterator const_iterator;\ const static int RTYPE = \ ::Rcpp::traits::r_sexptype_traits::rtype; \ vector(SEXP x) throw(::Rcpp::not_compatible) : \ data(0), isAllocated(true) { \ SEXP y = ::Rcpp::r_cast(x); \ int size = ::Rf_length(y); \ data = gsl_vector##__SUFFIX__##_calloc(size); \ ::Rcpp::internal::export_range<__CAST__*>(y, \ reinterpret_cast<__CAST__*>(data->data)); \ } \ vector(gsltype* x) : data(x), isAllocated(true) {} \ vector(int size) : \ data(gsl_vector##__SUFFIX__##_calloc(size)), \ isAllocated(true) {} \ ~vector() {} \ operator gsltype*() { return data; } \ operator const gsltype*() const { return data; } \ gsltype* operator->() const { return data; } \ gsltype& operator*() const { return *data; } \ vector(const vector& x) : data(x.data), isAllocated(true) {} \ vector& operator=(const vector& other) { \ data = other.data; \ isAllocated = other.isAllocated; \ return *this; \ } \ inline Proxy operator[](int i) { \ return Proxy(data, i); \ } \ inline ConstProxy operator[](int i) const { \ return ConstProxy(data, i); \ } \ inline iterator begin() { return iterator(Proxy(*this, 0)); } \ inline iterator end() { return iterator(Proxy(*this,data->size)); } \ inline const_iterator begin() const { \ return const_iterator(ConstProxy(*this, 0)); \ } \ inline const_iterator end() const { \ return const_iterator(ConstProxy(*this, data->size)); \ } \ inline size_t size() const { return data->size; } \ inline void free() { \ if (isAllocated) { \ gsl_vector##__SUFFIX__##_free(data); \ isAllocated = false; \ } \ } \ private: \ bool isAllocated; \ }; \ template <> class matrix<__T__> { \ public: \ typedef __T__ type; \ typedef __T__* pointer; \ typedef gsl_matrix##__SUFFIX__ gsltype; \ gsltype* data; \ const static int RTYPE = \ ::Rcpp::traits::r_sexptype_traits::rtype; \ class Proxy { \ public: \ Proxy(gsltype* data_, int row_, int col_) : \ row(row_), col(col_), parent(data_) {} \ Proxy& operator=(type x) { \ gsl_matrix##__SUFFIX__##_set(parent, row, col, x); \ return *this; \ } \ inline operator type() { \ return gsl_matrix##__SUFFIX__##_get(parent, row, col); \ } \ inline operator const type() const { \ return gsl_matrix##__SUFFIX__##_get(parent, row, col); \ } \ int row; \ int col; \ gsltype* parent; \ }; \ class ConstProxy { \ public: \ ConstProxy(const gsltype* data_, int row_, int col_) : \ row(row_), col(col_), parent(data_) {} \ inline operator type() { \ return gsl_matrix##__SUFFIX__##_get(parent, row, col); \ } \ inline operator const type() const { \ return gsl_matrix##__SUFFIX__##_get(parent, row, col); \ } \ int row; \ int col; \ const gsltype* parent; \ }; \ matrix(SEXP x) throw(::Rcpp::not_compatible) : \ data(0), isAllocated(true) { import(x); } \ matrix(gsltype* x) : data(x), isAllocated(true) {} \ matrix(int nrow, int ncol) : \ data(gsl_matrix##__SUFFIX__##_alloc(nrow, ncol)), \ isAllocated(true) {} \ ~matrix() {} \ operator gsltype*() { return data; } \ operator const gsltype*() const { return data; } \ gsltype* operator->() const { return data; } \ gsltype& operator*() const { return *data; } \ matrix(const matrix& x) : data(x.data), isAllocated(true) {} \ matrix& operator=(const matrix& other) { \ data = other.data; \ isAllocated = other.isAllocated; \ return *this; \ } \ inline size_t nrow() const { return data->size1; } \ inline size_t ncol() const { return data->size2; } \ inline size_t size() const { return data->size1 * data->size2; } \ inline Proxy operator()(int row, int col){ \ return Proxy( *this, row, col); \ } \ inline ConstProxy operator()(int row, int col) const { \ return ConstProxy( *this, row, col); \ } \ void free(){ \ if (isAllocated) { \ gsl_matrix##__SUFFIX__##_free(data); \ isAllocated = false; \ } \ } \ private: \ inline void import(SEXP x) throw(::Rcpp::not_compatible); \ bool isAllocated; \ }; \ #define _RCPPGSL_SPEC_NOSUFFIX(__T__,__CAST__) \ template <> struct vector_view_type<__T__> { \ typedef gsl_vector_view type; \ typedef gsl_vector_const_view const_type; \ }; \ template <> struct matrix_view_type<__T__> { \ typedef gsl_matrix_view type; \ typedef gsl_matrix_const_view const_type; \ }; \ template <> class vector<__T__> { \ public: \ typedef __T__ type; \ typedef __T__* pointer; \ typedef gsl_vector gsltype; \ gsltype* data; \ class Proxy { \ public: \ Proxy(gsltype* data_, int index_) : \ index(index_), parent(data_) {} \ Proxy& operator=(type x) { \ gsl_vector_set(parent, index, x); \ return *this; \ } \ inline operator type() { \ return gsl_vector_get(parent, index); \ } \ inline operator const type() const { \ return gsl_vector_get(parent, index); \ } \ int index; \ gsltype* parent; \ inline void move(int d) { index += d; } \ }; \ class ConstProxy { \ public: \ ConstProxy(const gsltype* data_, int index_) : \ index(index_), parent(data_) {} \ inline operator type() { \ return gsl_vector_get(parent, index); \ } \ inline operator const type() const { \ return gsl_vector_get(parent, index); \ } \ int index; \ const gsltype* parent; \ inline void move(int d) { index += d; } \ }; \ typedef ::Rcpp::internal::Proxy_Iterator iterator; \ typedef ::Rcpp::internal::Proxy_Iterator const_iterator; \ const static int RTYPE = \ ::Rcpp::traits::r_sexptype_traits::rtype; \ vector(SEXP x) throw(::Rcpp::not_compatible) : \ data(0), isAllocated(true) { \ SEXP y = ::Rcpp::r_cast(x); \ int size = ::Rf_length(y); \ data = gsl_vector_calloc(size); \ ::Rcpp::internal::export_range<__CAST__*>(y, \ reinterpret_cast<__CAST__*>(data->data)); \ } \ vector(gsltype* x) : data(x), isAllocated(true) {} \ vector(int size) : \ data(gsl_vector_calloc(size)), isAllocated(true) {} \ ~vector() {} \ operator gsltype*() { return data; } \ operator const gsltype*() const { return data; } \ gsltype* operator->() const { return data; } \ gsltype& operator*() const { return *data; } \ vector(const vector& x) : data(x.data), isAllocated(true) {} \ vector& operator=(const vector& other) { \ data = other.data; \ isAllocated = other.isAllocated; \ return *this; \ } \ inline Proxy operator[](int i) { \ return Proxy(data, i); \ } \ inline ConstProxy operator[](int i) const { \ return ConstProxy(data, i); \ } \ inline iterator begin() { return iterator(Proxy(*this, 0)); } \ inline iterator end() { return iterator(Proxy(*this, data->size)); } \ inline const_iterator begin() const { \ return const_iterator(ConstProxy(*this, 0)); \ } \ inline const_iterator end() const { \ return const_iterator(ConstProxy(*this, data->size)); \ } \ inline size_t size() const { return data->size; } \ inline void free() { \ if (isAllocated) { \ gsl_vector_free(data); \ isAllocated = false; \ } \ } \ private: \ bool isAllocated; \ }; \ template <> class matrix<__T__> { \ public: \ typedef __T__ type; \ typedef __T__* pointer; \ typedef gsl_matrix gsltype; \ gsltype* data; \ const static int RTYPE = \ ::Rcpp::traits::r_sexptype_traits::rtype; \ class Proxy { \ public: \ Proxy(gsltype* data_, int row_, int col_) : \ row(row_), col(col_), parent(data_) {} \ Proxy& operator=(type x) { \ gsl_matrix_set(parent, row, col, x); \ return *this; \ } \ inline operator type() { \ return gsl_matrix_get(parent, row, col); \ } \ inline operator const type() const { \ return gsl_matrix_get(parent, row, col); \ } \ int row; \ int col; \ gsltype* parent; \ }; \ class ConstProxy { \ public: \ ConstProxy(const gsltype* data_, int row_, int col_) : \ row(row_), col(col_), parent(data_) {} \ inline operator type() { \ return gsl_matrix_get(parent, row, col); \ } \ inline operator const type() const { \ return gsl_matrix_get(parent, row, col); \ } \ int row; \ int col; \ const gsltype* parent; \ }; \ matrix(SEXP x) throw(::Rcpp::not_compatible) : \ data(0), isAllocated(true) { import(x); } \ matrix(gsltype* x) : data(x), isAllocated(true) {} \ matrix(int nrow, int ncol) : \ data(gsl_matrix_alloc(nrow, ncol)), isAllocated(true) {} \ ~matrix() {} \ operator gsltype*() { return data; } \ operator const gsltype*() const { return data; } \ gsltype* operator->() const { return data; } \ gsltype& operator*() const { return *data; } \ matrix(const matrix& x) : data(x.data), isAllocated(true) {} \ matrix& operator=(const matrix& other) { \ data = other.data; \ isAllocated = other.isAllocated; \ return *this; \ } \ inline size_t nrow() const { return data->size1; } \ inline size_t ncol() const { return data->size2; } \ inline size_t size() const { return data->size1 * data->size2; } \ inline Proxy operator()(int row, int col) { \ return Proxy(*this, row, col); \ } \ inline ConstProxy operator()(int row, int col) const { \ return ConstProxy(*this, row, col); \ } \ void free() { \ if (isAllocated) { \ gsl_matrix_free(data); \ isAllocated = false; \ } \ } \ private: \ inline void import(SEXP x) throw(::Rcpp::not_compatible); \ bool isAllocated; \ }; \ #endif RcppGSL/inst/include/RcppGSL_typedef.h0000644000176200001440000000245312774327533017310 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; indent-tabs-mode: nil; -*- // // RcppGSL_typedef.h: Shorthand Definitions for Seamless R and GSL Integration // // Copyright (C) 2015 Dirk Eddelbuettel // // This file is part of RcppGSL. // // RcppGSL is free software: you can redistribute it and/or modify it // under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 2 of the License, or // (at your option) any later version. // // RcppGSL is distributed in the hope that it will be useful, but // WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with RcppGSL. If not, see . #ifndef RCPPGSL__TYPEDEF_H #define RCPPGSL__TYPEDEF_H namespace RcppGSL { typedef matrix Matrix; typedef vector Vector; typedef matrix_view MatrixView; typedef vector_view VectorView; typedef matrix IntMatrix; typedef vector IntVector; typedef matrix_view IntMatrixView; typedef vector_view IntVectorView; } #endif RcppGSL/inst/include/RcppGSL_vector.h0000644000176200001440000000633312774327533017153 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; indent-tabs-mode: nil; -*- // // RcppGSL_vector.h: Vector class for Seamless R and GSL Integration // // Copyright (C) 2010 - 2015 Romain Francois and Dirk Eddelbuettel // // This file is part of RcppGSL. // // RcppGSL is free software: you can redistribute it and/or modify it // under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 2 of the License, or // (at your option) any later version. // // RcppGSL is distributed in the hope that it will be useful, but // WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with RcppGSL. If not, see . #ifndef RCPPGSL__RCPPGSL_VECTOR_H #define RCPPGSL__RCPPGSL_VECTOR_H #include #include namespace RcppGSL { template class gslvector_importer { public: typedef T r_import_type; /* this is important */ private: r_import_type* data; int stride; int n; public: gslvector_importer(T* data_, int stride_, int n_) : data(data_), stride(stride_), n(n_) {} inline r_import_type get(int i) const { return data[ i * stride ]; } inline int size() const { return n; } }; } namespace Rcpp { #define RCPPGSL_WRAP(__TYPE__,__DATA__) \ template <> SEXP wrap(const __TYPE__& x) { \ return wrap(RcppGSL::gslvector_importer<__DATA__>(x.data, x.stride, x.size ) ); \ } #define RCPPGSL_WRAP_CAST(__TYPE__,__DATA__,__CAST__) \ template <> SEXP wrap(const __TYPE__& x) { \ return wrap(RcppGSL::gslvector_importer<__DATA__>(reinterpret_cast<__CAST__>(x.data), x.stride, x.size ) ); \ } RCPPGSL_WRAP(gsl_vector ,double) RCPPGSL_WRAP(gsl_vector_float ,float) RCPPGSL_WRAP(gsl_vector_int ,int) RCPPGSL_WRAP(gsl_vector_long ,long) RCPPGSL_WRAP(gsl_vector_long_double ,long double) RCPPGSL_WRAP(gsl_vector_short ,short) RCPPGSL_WRAP(gsl_vector_uchar ,unsigned char) RCPPGSL_WRAP(gsl_vector_uint ,unsigned int) RCPPGSL_WRAP(gsl_vector_ushort ,unsigned short) RCPPGSL_WRAP(gsl_vector_ulong ,unsigned long) RCPPGSL_WRAP_CAST(gsl_vector_char ,unsigned char ,Rbyte* const) RCPPGSL_WRAP_CAST(gsl_vector_complex ,gsl_complex ,gsl_complex*) RCPPGSL_WRAP_CAST(gsl_vector_complex_float ,gsl_complex_float ,gsl_complex_float*) RCPPGSL_WRAP_CAST(gsl_vector_complex_long_double,gsl_complex_long_double,gsl_complex_long_double*) template SEXP wrap(const ::RcppGSL::vector& x) { return wrap(*(x.data)); } #undef RCPPGSL_WRAP_CAST #undef RCPPGSL_WRAP } #endif RcppGSL/inst/include/RcppGSL_vector_view.h0000644000176200001440000000447412774327533020211 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; indent-tabs-mode: nil; -*- // // RcppGSL_vector_view.h: Vector view class for Seamless R and GSL Integration // // Copyright (C) 2010 - 2015 Romain Francois and Dirk Eddelbuettel // // This file is part of RcppGSL. // // RcppGSL is free software: you can redistribute it and/or modify it // under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 2 of the License, or // (at your option) any later version. // // RcppGSL is distributed in the hope that it will be useful, but // WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with RcppGSL. If not, see . #ifndef RCPPGSL__RCPPGSL_VECTOR_VIEW_H #define RCPPGSL__RCPPGSL_VECTOR_VIEW_H #include #include namespace Rcpp{ #define RCPPGSL_VIEW(SUFFIX) \ template <> SEXP wrap(const gsl_vector##SUFFIX##_view& x) { \ return wrap(x.vector); \ } \ template <> SEXP wrap(const gsl_vector##SUFFIX##_const_view& x) { \ return wrap(x.vector); \ } RCPPGSL_VIEW(_int) RCPPGSL_VIEW(_float) RCPPGSL_VIEW(_long) RCPPGSL_VIEW(_char) RCPPGSL_VIEW(_complex) RCPPGSL_VIEW(_complex_float) RCPPGSL_VIEW(_complex_long_double) RCPPGSL_VIEW(_long_double) RCPPGSL_VIEW(_short) RCPPGSL_VIEW(_uchar) RCPPGSL_VIEW(_uint) RCPPGSL_VIEW(_ushort) RCPPGSL_VIEW(_ulong) #undef RCPPGSL_VIEW template <> SEXP wrap(const gsl_vector_view& x){ return wrap(x.vector) ; } template <> SEXP wrap(const gsl_vector_const_view& x ){ return wrap(x.vector) ; } template SEXP wrap(const ::RcppGSL::vector_view& x){ return wrap( x.view.vector ) ; } } #endif RcppGSL/inst/include/RcppGSL.h0000644000176200001440000000222512774327533015565 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; indent-tabs-mode: nil; -*- // // RcppGSL.h: Seamless R and GSL Integration via Rcpp // // Copyright (C) 2010 - 2015 Romain Francois and Dirk Eddelbuettel // // This file is part of RcppGSL. // // RcppGSL is free software: you can redistribute it and/or modify it // under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 2 of the License, or // (at your option) any later version. // // RcppGSL is distributed in the hope that it will be useful, but // WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with RcppGSL. If not, see . #ifndef RCPPGSL__RCPPGSL_H #define RCPPGSL__RCPPGSL_H #include #include #include #include #include #include #include #endif RcppGSL/inst/include/RcppGSL_matrix.h0000644000176200001440000002301612774327533017152 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; indent-tabs-mode: nil; -*- // // RcppGSL_matrix.h: Matrix class for Seamless R and GSL Integration // // Copyright (C) 2010 - 2015 Romain Francois and Dirk Eddelbuettel // // This file is part of RcppGSL. // // RcppGSL is free software: you can redistribute it and/or modify it // under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 2 of the License, or // (at your option) any later version. // // RcppGSL is distributed in the hope that it will be useful, but // WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with RcppGSL. If not, see . #ifndef RCPPGSL__RCPPGSL_MATRIX_H #define RCPPGSL__RCPPGSL_MATRIX_H #include #include namespace RcppGSL { template class gslmatrix_importer { public: typedef T r_import_type; /* this is important */ private: r_import_type* ptr; int size1; int size2; int tda; public: gslmatrix_importer(r_import_type* ptr_, int size1_, int size2_, int tda_) : ptr(ptr_), size1(size1_), size2(size2_), tda(tda_) {}; inline int size() const { return size1 * size2; }; r_import_type get(int i) const { int col = (int)(i / size1); int row = i - col * size1; return ptr[ row * tda + col ]; } }; } namespace Rcpp { #define RCPPGSL_WRAP(__TYPE__,__DATA__) \ template <> SEXP wrap(const __TYPE__& x) { \ SEXP res = PROTECT(wrap(RcppGSL::gslmatrix_importer<__DATA__>( \ x.data, x.size1, x.size2, x.tda))); \ SEXP dims = PROTECT(::Rf_allocVector(INTSXP, 2)); \ INTEGER(dims)[0] = x.size1; \ INTEGER(dims)[1] = x.size2; \ ::Rf_setAttrib(res, R_DimSymbol, dims); \ UNPROTECT(2); \ return res; \ } #define RCPPGSL_WRAP_CAST(__TYPE__,__DATA__) \ template <> SEXP wrap(const __TYPE__& x) { \ SEXP res = PROTECT(wrap(RcppGSL::gslmatrix_importer<__DATA__>( \ reinterpret_cast<__DATA__*>(x.data), \ x.size1, x.size2, x.tda))); \ SEXP dims = PROTECT(::Rf_allocVector(INTSXP, 2)); \ INTEGER(dims)[0] = x.size1; \ INTEGER(dims)[1] = x.size2; \ ::Rf_setAttrib(res, R_DimSymbol, dims); \ UNPROTECT(2); \ return res; \ } RCPPGSL_WRAP(gsl_matrix , double) RCPPGSL_WRAP(gsl_matrix_float , float) RCPPGSL_WRAP(gsl_matrix_int , int) RCPPGSL_WRAP(gsl_matrix_long , long) RCPPGSL_WRAP(gsl_matrix_long_double , long double) RCPPGSL_WRAP(gsl_matrix_short , short) RCPPGSL_WRAP(gsl_matrix_uchar , unsigned char) RCPPGSL_WRAP(gsl_matrix_uint , unsigned int) RCPPGSL_WRAP(gsl_matrix_ushort , unsigned short) RCPPGSL_WRAP(gsl_matrix_ulong , unsigned long) RCPPGSL_WRAP_CAST(gsl_matrix_char ,Rbyte ) RCPPGSL_WRAP_CAST(gsl_matrix_complex ,gsl_complex ) RCPPGSL_WRAP_CAST(gsl_matrix_complex_float ,gsl_complex_float ) RCPPGSL_WRAP_CAST(gsl_matrix_complex_long_double,gsl_complex_long_double) #undef RCPPGSL_WRAP #undef RCPPGSL_WRAP_CAST } namespace RcppGSL { #undef _RCPPGSL_DEF #define _RCPPGSL_DEF(__T__,__SUFFIX__) \ inline void matrix<__T__>::import(SEXP x) throw(::Rcpp::not_compatible) { \ Rcpp::Matrix mat(x); \ int nc = mat.ncol(); \ int nr = mat.nrow(); \ int i = 0, j = 0; \ data = gsl_matrix##__SUFFIX__##_alloc(nr, nc); \ Rcpp::Matrix::iterator it = mat.begin(); \ for (; j::import(SEXP x) throw(::Rcpp::not_compatible) { \ Rcpp::Matrix mat(x); \ int nc = mat.ncol(); \ int nr = mat.nrow(); \ int i = 0, j = 0; \ data = gsl_matrix##__SUFFIX__##_alloc(nr, nc); \ Rcpp::Matrix::iterator it = mat.begin(); \ typedef Rcpp::traits::storage_type::type STORAGE; \ for (; j(*it)); \ } \ } \ } inline void matrix::import(SEXP x) throw(::Rcpp::not_compatible) { Rcpp::Matrix mat(x); int nc = mat.ncol(); int nr = mat.nrow(); int i = 0, j = 0; data = gsl_matrix_alloc(nr, nc); Rcpp::Matrix::iterator it = mat.begin(); for (; j::import(SEXP x) throw(::Rcpp::not_compatible) { Rcpp::Matrix mat(x); int nc = mat.ncol(); int nr = mat.nrow(); int i = 0, j = 0; data = gsl_matrix_char_alloc(nr, nc); Rcpp::Matrix::iterator it = mat.begin(); for (; j(*it)); } } } #undef _RCPPGSL_DEF #undef _RCPPGSL_DEF_CAST } namespace Rcpp { template SEXP wrap(const ::RcppGSL::matrix& x) { return wrap(*(x.data)); } } #endif RcppGSL/configure.ac0000644000176200001440000000173212774327533014032 0ustar liggesusers ## Process this file with autoconf to produce a configure script. ## ## Configure.ac for RcppGSL ## ## Copyright (C) 2010 Romain Francois and Dirk Eddelbuettel ## Copyright (C) 2014 - 2015 Dirk Eddelbuettel ## ## Licensed under GNU GPL 2 or later # The version set here will propagate to other files from here AC_INIT([RcppGSL], 0.2.3) # Checks for common programs using default macros AC_PROG_CC ## Use gsl-config to find arguments for compiler and linker flags ## ## Check for non-standard programs: gsl-config(1) AC_PATH_PROG([GSL_CONFIG], [gsl-config]) ## If gsl-config was found, let's use it if test "${GSL_CONFIG}" != ""; then # Use gsl-config for header and linker arguments GSL_CFLAGS=`${GSL_CONFIG} --cflags` GSL_LIBS=`${GSL_CONFIG} --libs` else AC_MSG_ERROR([gsl-config not found, is GSL installed?]) fi # Now substitute these variables in src/Makevars.in to create src/Makevars AC_SUBST(GSL_CFLAGS) AC_SUBST(GSL_LIBS) AC_OUTPUT(src/Makevars) RcppGSL/tests/0000755000176200001440000000000013161734325012673 5ustar liggesusersRcppGSL/tests/doRUnit.R0000644000176200001440000000241613161737523014410 0ustar liggesusers## doRUnit.R --- Run RUnit tests ## ## with credits to package fUtilities in RMetrics ## which credits Gregor Gojanc's example in CRAN package 'gdata' ## as per the (now deceased) R Wiki http://wiki.r-project.org/rwiki/doku.php?id=developers:runit ## and changed further by Martin Maechler ## and usage across several Rcpp* package ## and more changes by Murray Stokely in HistogramTools ## ## Dirk Eddelbuettel, 2010 - 2017 if (requireNamespace("RUnit", quietly=TRUE) && requireNamespace("RcppGSL", quietly=TRUE)) { library(RUnit) library(RcppGSL) ## Set a seed to make the test deterministic set.seed(42) ## Define tests testSuite <- defineTestSuite(name="RcppGSL Unit Tests", dirs=system.file("unitTests", package = "RcppGSL"), testFuncRegexp = "^[Tt]est.+") Sys.setenv("R_TESTS"="") # without this, we get (or used to get) unit test failures tests <- runTestSuite(testSuite) # run tests printTextProtocol(tests) # print results ## Return success or failure to R CMD CHECK if (getErrors(tests)$nFail > 0) stop("TEST FAILED!") if (getErrors(tests)$nErr > 0) stop("TEST HAD ERRORS!") if (getErrors(tests)$nTestFunc < 1) stop("NO TEST FUNCTIONS RUN!") } RcppGSL/src/0000755000176200001440000000000013161737707012327 5ustar liggesusersRcppGSL/src/fastLm.cpp0000644000176200001440000000367213161737707014271 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; indent-tabs-mode: nil; -*- // // fastLm.cpp: Rcpp and GSL based implementation of lm // // Copyright (C) 2010 - 2015 Dirk Eddelbuettel and Romain Francois // // This file is part of RcppGSL. // // RcppGSL is free software: you can redistribute it and/or modify it // under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 2 of the License, or // (at your option) any later version. // // RcppGSL is distributed in the hope that it will be useful, but // WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with RcppGSL. If not, see . #include #include #include // [[Rcpp::export]] Rcpp::List fastLm(const RcppGSL::Matrix &X, const RcppGSL::Vector &y) { int n = X.nrow(), k = X.ncol(); double chisq; RcppGSL::Vector coef(k); // to hold the coefficient vector RcppGSL::Matrix cov(k,k); // and the covariance matrix // the actual fit requires working memory we allocate and free gsl_multifit_linear_workspace *work = gsl_multifit_linear_alloc (n, k); gsl_multifit_linear (X, y, coef, cov, &chisq, work); gsl_multifit_linear_free (work); // assign diagonal to a vector, then take square roots to get std.error Rcpp::NumericVector std_err; std_err = gsl_matrix_diagonal(cov); // need two step decl. and assignment std_err = Rcpp::sqrt(std_err); // sqrt() is an Rcpp sugar function return Rcpp::List::create(Rcpp::Named("coefficients") = coef, Rcpp::Named("stderr") = std_err, Rcpp::Named("df.residual") = n - k); } RcppGSL/src/setErrorHandler.cpp0000644000176200001440000000261213161737707016137 0ustar liggesusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; tab-width: 4 -*- // // setErrorHandler.cpp: Set the GSL error handler // // Copyright (C) 2015 Dirk Eddelbuettel // // This file is part of RcppGSL. // // RcppGSL is free software: you can redistribute it and/or modify it // under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 2 of the License, or // (at your option) any later version. // // RcppGSL is distributed in the hope that it will be useful, but // WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with RcppGSL. If not, see . #include #include // See Chapter 3 of the GSL Reference manual on this // Keep a pointer around in case someone wants to reset static gsl_error_handler_t *ptr_gsl_error_handler_t = NULL; // [[Rcpp::export]] bool gslSetErrorHandlerOff() { ptr_gsl_error_handler_t = gsl_set_error_handler_off(); return true; } // [[Rcpp::export]] bool gslResetErrorHandler() { if (ptr_gsl_error_handler_t == NULL) { return false; } ptr_gsl_error_handler_t = gsl_set_error_handler(ptr_gsl_error_handler_t); return true; } RcppGSL/src/Makevars.in0000644000176200001440000000026713161737707014435 0ustar liggesusers # set by configure GSL_CFLAGS = @GSL_CFLAGS@ GSL_LIBS = @GSL_LIBS@ # combine with standard arguments for R PKG_CPPFLAGS = $(GSL_CFLAGS) -I../inst/include PKG_LIBS = $(GSL_LIBS) RcppGSL/src/Makevars.win0000644000176200001440000000025313161737707014617 0ustar liggesusers## This assumes that the LIB_GSL variable points to working GSL libraries PKG_CPPFLAGS=-I$(LIB_GSL)/include -I../inst/include PKG_LIBS=-L$(LIB_GSL)/lib -lgsl -lgslcblas RcppGSL/src/init.c0000644000176200001440000000142013161737707013433 0ustar liggesusers#include #include #include // for NULL #include /* FIXME: Check these declarations against the C/Fortran source code. */ /* .Call calls */ extern SEXP RcppGSL_fastLm(SEXP, SEXP); extern SEXP RcppGSL_gslResetErrorHandler(); extern SEXP RcppGSL_gslSetErrorHandlerOff(); static const R_CallMethodDef CallEntries[] = { {"RcppGSL_fastLm", (DL_FUNC) &RcppGSL_fastLm, 2}, {"RcppGSL_gslResetErrorHandler", (DL_FUNC) &RcppGSL_gslResetErrorHandler, 0}, {"RcppGSL_gslSetErrorHandlerOff", (DL_FUNC) &RcppGSL_gslSetErrorHandlerOff, 0}, {NULL, NULL, 0} }; void R_init_RcppGSL(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } RcppGSL/src/RcppExports.cpp0000644000176200001440000000222313161737707015323 0ustar liggesusers// This file was generated by Rcpp::compileAttributes // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include "../inst/include/RcppGSL.h" #include using namespace Rcpp; // fastLm Rcpp::List fastLm(const RcppGSL::matrix &X, const RcppGSL::vector &y); RcppExport SEXP RcppGSL_fastLm(SEXP XSEXP, SEXP ySEXP) { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; Rcpp::traits::input_parameter< const RcppGSL::matrix & >::type X(XSEXP); Rcpp::traits::input_parameter< const RcppGSL::vector &>::type y(ySEXP); __result = Rcpp::wrap(fastLm(X, y)); return __result; END_RCPP } // gslSetErrorHandlerOff bool gslSetErrorHandlerOff(); RcppExport SEXP RcppGSL_gslSetErrorHandlerOff() { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; __result = Rcpp::wrap(gslSetErrorHandlerOff()); return __result; END_RCPP } // gslResetErrorHandler bool gslResetErrorHandler(); RcppExport SEXP RcppGSL_gslResetErrorHandler() { BEGIN_RCPP Rcpp::RObject __result; Rcpp::RNGScope __rngScope; __result = Rcpp::wrap(gslResetErrorHandler()); return __result; END_RCPP } RcppGSL/NAMESPACE0000644000176200001440000000062713070437421012751 0ustar liggesusersuseDynLib("RcppGSL", .registration=TRUE) importFrom("Rcpp", "evalCpp") importFrom("stats", "model.frame", "model.matrix", "model.response", "fitted", "coef", "printCoefmat", "pt") export(fastLmPure, fastLm, LdFlags, CFlags) S3method(fastLm, default) S3method(fastLm, formula) S3method(predict, fastLm) S3method(print, fastLm) S3method(summary, fastLm) S3method(print, summary.fastLm) RcppGSL/R/0000755000176200001440000000000013070437421011726 5ustar liggesusersRcppGSL/R/inline.R0000644000176200001440000000455213070437421013335 0ustar liggesusers## Copyright (C) 2010 - 2012 Dirk Eddelbuettel and Romain Francois ## Copyright (C) 2014 - 2017 Dirk Eddelbuettel ## ## This file is part of RcppGSL. ## ## RcppGSL is free software: you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 2 of the License, or ## (at your option) any later version. ## ## RcppGSL is distributed in the hope that it will be useful, but ## WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## ## You should have received a copy of the GNU General Public License ## along with RcppGSL. If not, see . .pkgenv <- new.env(parent=emptyenv()) .onLoad <- function(libname, pkgname) { if (.Platform$OS.type=="windows") { LIB_GSL <- Sys.getenv("LIB_GSL") .pkgenv[["gsl_cflags"]] <- sprintf("-I%s/include", LIB_GSL) .pkgenv[["gsl_libs"]] <- sprintf("-L%s/lib -lgsl -lgslcblas", LIB_GSL) } else { if (unname(Sys.which("gsl-config")) != "") { .pkgenv[["gsl_cflags"]] <- system("gsl-config --cflags", intern = TRUE) .pkgenv[["gsl_libs"]] <- system("gsl-config --libs" , intern = TRUE) } else { .pkgenv[["gsl_cflags"]] <- "" .pkgenv[["gsl_libs"]] <- "" warning("No 'gsl-config' config script found, limiting extensibility.", call. = FALSE) } } } LdFlags <- function(print = TRUE) { if (print) cat(.pkgenv$gsl_libs) else .pkgenv$gsl_libs } CFlags <- function(print = TRUE) { if (print) cat(.pkgenv$gsl_cflags) else .pkgenv$gsl_cflags } inlineCxxPlugin <- function(...) { plugin <- Rcpp::Rcpp.plugin.maker( include.before = "#include ", libs = sprintf( "%s $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)", LdFlags(FALSE)), package = "RcppGSL", Makevars = NULL, Makevars.win = NULL ) settings <- plugin() settings$env$PKG_CPPFLAGS <- CFlags(FALSE) settings$configure <- readLines(system.file("skeleton", "configure", package="RcppGSL")) settings$configure.win <- readLines(system.file("skeleton", "configure.win", package="RcppGSL")) settings$Makevars.in <- readLines(system.file("skeleton", "Makevars.in", package = "RcppGSL")) settings } RcppGSL/R/init.R0000644000176200001440000000162712774327533013036 0ustar liggesusers## Copyright (C) 2015 Dirk Eddelbuettel ## ## This file is part of RcppGSL. ## ## RcppGSL is free software: you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 2 of the License, or ## (at your option) any later version. ## ## RcppGSL is distributed in the hope that it will be useful, but ## WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## ## You should have received a copy of the GNU General Public License ## along with RcppGSL. If not, see . .onAttach <- function(libname, pkgname) { ## turn the GSL error handler off so that GSL will not abort R ## users will have to check return codes (see vignette) gslSetErrorHandlerOff() } RcppGSL/R/RcppExports.R0000644000176200001440000000062312774327533014357 0ustar liggesusers# This file was generated by Rcpp::compileAttributes # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 fastLm <- function(X, y) { .Call('RcppGSL_fastLm', PACKAGE = 'RcppGSL', X, y) } gslSetErrorHandlerOff <- function() { .Call('RcppGSL_gslSetErrorHandlerOff', PACKAGE = 'RcppGSL') } gslResetErrorHandler <- function() { .Call('RcppGSL_gslResetErrorHandler', PACKAGE = 'RcppGSL') } RcppGSL/R/fastLm.R0000644000176200001440000000760713070437421013311 0ustar liggesusers ## fastLm.R: Rcpp/GSL implementation of lm() ## ## Copyright (C) 2010 - 2017 Dirk Eddelbuettel and Romain Francois ## ## This file is part of RcppGSL. ## ## RcppGSL is free software: you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 2 of the License, or ## (at your option) any later version. ## ## RcppGSL is distributed in the hope that it will be useful, but ## WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## ## You should have received a copy of the GNU General Public License ## along with RcppGSL. If not, see . fastLmPure <- function(X, y) { stopifnot(is.matrix(X), is.numeric(y), nrow(y)==nrow(X)) res <- .Call("RcppGSL_fastLm", X, y, PACKAGE="RcppGSL") } fastLm <- function(X, ...) UseMethod("fastLm") fastLm.default <- function(X, y, ...) { X <- as.matrix(X) y <- as.numeric(y) res <- fastLmPure(X, y) names(res$coefficients) <- colnames(X) res$fitted.values <- as.vector(X %*% res$coefficients) res$residuals <- y - res$fitted.values res$call <- match.call() res$intercept <- any(apply(X, 2, function(x) all(x == x[1]))) class(res) <- "fastLm" res } print.fastLm <- function(x, ...) { cat("\nCall:\n") print(x$call) cat("\nCoefficients:\n") print(x$coefficients, digits=5) } summary.fastLm <- function(object, ...) { se <- object$stderr tval <- coef(object)/se TAB <- cbind(Estimate = coef(object), StdErr = se, t.value = tval, p.value = 2*pt(-abs(tval), df=object$df)) # why do I need this here? rownames(TAB) <- names(object$coefficients) colnames(TAB) <- c("Estimate", "StdErr", "t.value", "p.value") ## cf src/stats/R/lm.R and case with no weights and an intercept f <- object$fitted.values r <- object$residuals #mss <- sum((f - mean(f))^2) mss <- if (object$intercept) sum((f - mean(f))^2) else sum(f^2) rss <- sum(r^2) r.squared <- mss/(mss + rss) df.int <- if (object$intercept) 1L else 0L n <- length(f) rdf <- object$df adj.r.squared <- 1 - (1 - r.squared) * ((n - df.int)/rdf) res <- list(call=object$call, coefficients=TAB, r.squared=r.squared, adj.r.squared=adj.r.squared, sigma=sqrt(sum((object$residuals)^2)/rdf), df=object$df, residSum=summary(object$residuals, digits=5)[-4]) class(res) <- "summary.fastLm" res } print.summary.fastLm <- function(x, ...) { cat("\nCall:\n") print(x$call) cat("\nResiduals:\n") print(x$residSum) cat("\n") printCoefmat(x$coefficients, P.values=TRUE, has.Pvalue=TRUE) digits <- max(3, getOption("digits") - 3) cat("\nResidual standard error: ", formatC(x$sigma, digits=digits), " on ", formatC(x$df), " degrees of freedom\n", sep="") cat("Multiple R-squared: ", formatC(x$r.squared, digits=digits), ",\tAdjusted R-squared: ",formatC(x$adj.r.squared, digits=digits), "\n", sep="") invisible(x) } fastLm.formula <- function(formula, data=list(), ...) { mf <- model.frame(formula=formula, data=data) X <- model.matrix(attr(mf, "terms"), data=mf) y <- model.response(mf) res <- fastLm.default(X, y, ...) res$call <- match.call() res$formula <- formula res$intercept <- attr(attr(mf, "terms"), "intercept") res } predict.fastLm <- function(object, newdata=NULL, ...) { if (is.null(newdata)) { y <- fitted(object) } else { if (!is.null(object$formula)) { x <- model.matrix(object$formula, newdata) } else { x <- newdata } y <- as.vector(x %*% coef(object)) } y } RcppGSL/vignettes/0000755000176200001440000000000013161737707013550 5ustar liggesusersRcppGSL/vignettes/RcppGSL-intro.Rmd0000644000176200001440000012243613161516164016617 0ustar liggesusers--- title: | | \pkg{RcppGSL}: Easier \pkg{GSL} use from \proglang{R} via \pkg{Rcpp} # Use letters for affiliations author: - name: Dirk Eddelbuettel affiliation: a - name: Romain François affiliation: b address: - code: a address: \url{http://dirk.eddelbuettel.com} - code: b address: \url{https://romain.rbind.io/} # For footer text lead_author_surname: Eddelbuettel and François # Place DOI URL or CRAN Package URL here doi: "https://cran.r-project.org/package=RcppGSL" # Abstract abstract: | The GNU Scientific Library, or \pkg{GSL}, is a collection of numerical routines for scientific computing \citep{GSL}. It is particularly useful for \proglang{C} and \proglang{C++} programs as it provides a standard \proglang{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 \pkg{RcppGSL} package provides an easy-to-use interface between \pkg{GSL} and \proglang{R}, with a particular focus on matrix and vector data structures. \pkg{RcppGSL} relies on \pkg{Rcpp} \citep{JSS:Rcpp,Eddelbuettel:2013:Rcpp,CRAN:Rcpp} which is itself a package that eases the interfaces between \proglang{R} and C++.} # Font size of the document, values of 9pt (default), 10pt, 11pt and 12pt fontsize: 9pt # Optional: Force one-column layout, default is two-column one_column: false # Optional: Enable section numbering, default is unnumbered numbersections: true # Optional: Specify the depth of section number, default is 5 secnumdepth: 5 # Optional: Bibliography bibliography: Rcpp # Customize footer, eg by referencing the vignette footer_contents: "RcppGSL Vignette" # Produce a pinp document output: pinp::pinp # Extra definitions in header header-includes: > \newcommand{\proglang}[1]{\textsf{#1}} \newcommand{\pkg}[1]{\textbf{#1}} # Include bibliography material directly (from .bbl file) include-after: | \begin{thebibliography}{12} \newcommand{\enquote}[1]{``#1''} \providecommand{\natexlab}[1]{#1} \providecommand{\url}[1]{\texttt{#1}} \providecommand{\urlprefix}{URL } \expandafter\ifx\csname urlstyle\endcsname\relax \providecommand{\doi}[1]{doi:\discretionary{}{}{}#1}\else \providecommand{\doi}{doi:\discretionary{}{}{}\begingroup \urlstyle{rm}\Url}\fi \providecommand{\eprint}[2][]{\url{#2}} \bibitem[{Allaire \emph{et~al.}(2017)Allaire, Eddelbuettel, and Fran\c{c}ois}]{CRAN:Rcpp:Attributes} Allaire JJ, Eddelbuettel D, Fran\c{c}ois R (2017). \newblock \emph{{Rcpp} Attributes}. \newblock Vignette included in R package Rcpp, \urlprefix\url{http://CRAN.R-Project.org/package=Rcpp}. \bibitem[{Bates and Eddelbuettel(2013)}]{JSS:RcppEigen} Bates D, Eddelbuettel D (2013). \newblock \enquote{Fast and Elegant Numerical Linear Algebra Using the {RcppEigen} Package.} \newblock \emph{Journal of Statistical Software}, \textbf{52}(5), 1--24. \newblock \urlprefix\url{http://www.jstatsoft.org/v52/i05/}. \bibitem[{Bates \emph{et~al.}(2016)Bates, Fran\c{c}ois, and Eddelbuettel}]{CRAN:RcppEigen} Bates D, Fran\c{c}ois R, Eddelbuettel D (2016). \newblock \emph{RcppEigen: Rcpp integration for the Eigen templated linear algebra library}. \newblock {R} package version 0.3.2.9.0, \urlprefix\url{http://CRAN.R-Project.org/package=RcppEigen}. \bibitem[{Eddelbuettel(2013)}]{Eddelbuettel:2013:Rcpp} Eddelbuettel D (2013). \newblock \emph{Seamless R and C++ Integration with Rcpp}. \newblock Use R! Springer, New York. \newblock ISBN 978-1-4614-6867-7. \bibitem[{Eddelbuettel and Fran\c{c}ois(2011)}]{JSS:Rcpp} Eddelbuettel D, Fran\c{c}ois R (2011). \newblock \enquote{{Rcpp}: Seamless {R} and {C++} Integration.} \newblock \emph{Journal of Statistical Software}, \textbf{40}(8), 1--18. \newblock \urlprefix\url{http://www.jstatsoft.org/v40/i08/}. \bibitem[{Eddelbuettel \emph{et~al.}(2017)Eddelbuettel, Fran\c{c}ois, Allaire, Ushey, Kou, Russel, Chambers, and Bates}]{CRAN:Rcpp} Eddelbuettel D, Fran\c{c}ois R, Allaire J, Ushey K, Kou Q, Russel N, Chambers J, Bates D (2017). \newblock \emph{{Rcpp}: Seamless {R} and {C++} Integration}. \newblock R package version 0.12.12, \urlprefix\url{http://CRAN.R-Project.org/package=Rcpp}. \bibitem[{Eddelbuettel \emph{et~al.}(2016)Eddelbuettel, Fran\c{c}ois, and Bates}]{CRAN:RcppArmadillo} Eddelbuettel D, Fran\c{c}ois R, Bates D (2016). \newblock \emph{RcppArmadillo: Rcpp integration for Armadillo templated linear algebra library}. \newblock R package version 0.7.400.2.0, \urlprefix\url{http://CRAN.R-Project.org/package=RcppArmadillo}. \bibitem[{Eddelbuettel and Sanderson(2014)}]{Eddelbuettel+Sanderson:2013:RcppArmadillo} Eddelbuettel D, Sanderson C (2014). \newblock \enquote{{RcppArmadillo}: Accelerating {R} with High-Performance {C++} Linear Algebra.} \newblock \emph{Computational Statistics and Data Analysis}, \textbf{71}, 1054--1063. \newblock \doi{10.1016/j.csda.2013.02.005}. \newblock \urlprefix\url{http://dx.doi.org/10.1016/j.csda.2013.02.005}. \bibitem[{Galassi \emph{et~al.}(2010)Galassi, Davies, Theiler, Gough, Jungman, Alken, Booth, and Rossi}]{GSL} Galassi M, Davies J, Theiler J, Gough B, Jungman G, Alken P, Booth M, Rossi F (2010). \newblock \emph{{GNU} {S}cientific {L}ibrary {R}eference {M}anual}, 3rd edition. \newblock Version 1.14. {ISBN} 0954612078, \urlprefix\url{http://www.gnu.org/software/gsl}. \bibitem[{{R Core Team}(2017)}]{R:Main} {R Core Team} (2017). \newblock \emph{R: A Language and Environment for Statistical Computing}. \newblock R Foundation for Statistical Computing, Vienna, Austria. \newblock \urlprefix\url{https://www.R-project.org/}. \bibitem[{Sanderson(2010)}]{Sanderson:2010:Armadillo} Sanderson C (2010). \newblock \enquote{{Armadillo}: {An} open source {C++} Algebra Library for Fast Prototyping and Computationally Intensive Experiments.} \newblock \emph{Technical report}, {NICTA}. \newblock \urlprefix\url{http://arma.sf.net}. \bibitem[{Sklyar \emph{et~al.}(2015)Sklyar, Murdoch, Smith, Eddelbuettel, and Fran\c{c}ois}]{CRAN:inline} Sklyar O, Murdoch D, Smith M, Eddelbuettel D, Fran\c{c}ois R (2015). \newblock \emph{inline: Inline C, C++, Fortran function calls from R}. \newblock R package version 0.3.14, \urlprefix\url{http://CRAN.R-Project.org/package=inline}. \end{thebibliography} # Required: Vignette metadata for inclusion in a package. vignette: > %\VignetteIndexEntry{RcppGSL} %\VignetteKeywords{R,GSL,Rcpp,data transfer} %\VignetteDepends{RcppGSL} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} --- \section{Introduction} The GNU Scientific Library, or \pkg{GSL}, is a collection of numerical routines for scientific computing \citep{GSL}. It is a rigourously developed and tested library providing support for a wide range of scientific or numerical tasks. Among the topics covered in the \pkg{GSL} are complex numbers, roots of polynomials, special functions, vector and matrix data structures, permutations, combinations, sorting, BLAS support, linear algebra, 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 least-squares fitting, minimization, physical constants, basis splines and wavelets. Support for \proglang{C} programming with the \pkg{GSL} is available as the \pkg{GSL} itself is written in \proglang{C}, and provides a \proglang{C}-language Application Programming Interface (API). Access from \proglang{C++} is possible, albeit not at an abstraction level that could be offered by dedicated \proglang{C++} implementations. Several \proglang{C++} wrappers for the \pkg{GSL} have been written over the years; none reached a state of completion comparable to the \pkg{GSL} itself. The \pkg{GSL} combines broad coverage of scientific topics, serious implementation effort, and the use of the well-known GNU General Public License (GPL). This has lead to fairly wide usage of the library. As a concrete example, we can consider the Comprehensive R Archive Network (CRAN) repository network for the \proglang{R} language and environment \citep{R:Main}. CRAN contains over three dozen packages interfacing the \pkg{GSL}. Of these more than half interface the vector or matrix classes as shown in Table \ref{tab:useOfGSLatCRAN}. This provides empirical evidence indicating that the \pkg{GSL} is popular among programmers using either the \proglang{C} or \proglang{C++} language for solving problems applied science. \begin{table} \centering \begin{small} \begin{tabular}{lccc} \toprule Package & Any \texttt{gsl} header & \texttt{gsl\_vector.h} & \texttt{gsl\_matrix.h} \\ \midrule abn & $\star$ & $\star$ & $\star$ \\ BayesLogit & $\star$ & & \\ BayesSAE & $\star$ & $\star$ & $\star$ \\ BayesVarSel & $\star$ & $\star$ & $\star$ \\ BH & $\star$ & $\star$ & \\ bnpmr & $\star$ & & \\ BNSP & $\star$ & $\star$ & $\star$ \\ cghseg & $\star$ & $\star$ & $\star$ \\ cit & $\star$ & & \\ diversitree & $\star$ & & $\star$ \\ eco & $\star$ & & \\ geoCount & $\star$ & & \\ graphscan & $\star$ & & \\ gsl & $\star$ & $\star$ & \\ gstat & $\star$ & & \\ hgm & $\star$ & & \\ HiCseg & $\star$ & & $\star$ \\ igraph & $\star$ & & \\ KFKSDS & $\star$ & $\star$ & $\star$ \\ libamtrack & $\star$ & & \\ mixcat & $\star$ & $\star$ & $\star$ \\ mvabund & $\star$ & $\star$ & $\star$ \\ outbreaker & $\star$ & $\star$ & $\star$ \\ R2GUESS & $\star$ & $\star$ & $\star$ \\ RCA & $\star$ & & \\ RcppGSL & $\star$ & $\star$ & $\star$ \\ RcppSMC & $\star$ & & \\ RcppZiggurat & $\star$ & & \\ RDieHarder & $\star$ & $\star$ & $\star$ \\ ridge & $\star$ & $\star$ & $\star$ \\ Rlibeemd & $\star$ & $\star$ & \\ Runuran & $\star$ & & \\ SemiCompRisks & $\star$ & & $\star$ \\ simplexreg & $\star$ & $\star$ & $\star$ \\ stsm & $\star$ & $\star$ & $\star$ \\ survSNP & $\star$ & & \\ TKF & $\star$ & $\star$ & $\star$ \\ topicmodels & $\star$ & $\star$ & $\star$ \\ VBLPCM & $\star$ & $\star$ & \\ VBmix & $\star$ & $\star$ & $\star$ \\ \bottomrule \end{tabular} \end{small} \caption{CRAN Package Usage of \pkg{GSL} outright, for vectors and for matrices.} \label{tab:useOfGSLatCRAN} \begin{flushleft} \footnotesize \textsl{Note:} Data gathered in late July 2015 by use of \texttt{grep} searching (recursively) for inclusion of any GSL header, or the vector and matrix headers specifically, within the \texttt{src/} or \texttt{inst/include/} directories of expanded source code archives of the CRAN network. Convenient (temporary) shell access to such an expanded code archive via WU Vienna is gratefully acknowledged. \end{flushleft} \end{table} At the same time, the \pkg{Rcpp} package \citep{JSS:Rcpp,Eddelbuettel:2013:Rcpp,CRAN:Rcpp} offers a higher-level interface between \proglang{R} and \proglang{C++}. \pkg{Rcpp} permits \proglang{R} objects like vectors, matrices, lists, functions, environments, $\ldots$, to be manipulated directly at the \proglang{C++} level, and alleviates the needs for complicated and error-prone parameter passing and memory allocation. It also allows compact vectorised expressions similar to what can be written in \proglang{R} directly at the \proglang{C++} level. The \pkg{RcppGSL} package discussed here aims to close the gap. It offers access to \pkg{GSL} functions, in particular via the vector and matrix data structures used throughout the \pkg{GSL}, while staying closer to the `whole object model' familar to the \proglang{R} programmer. The rest of paper is organised as follows. The next section shows a motivating example of a fast linear model fit routine using \pkg{GSL} functions. The following section discusses support for \pkg{GSL} vector types, which is followed by a section on matrices. The following two section discusses error handling, and then use of \pkg{RcppGSL} in your own package. This is followed by short discussions of how to use \pkg{RcppGSL} with \pkg{inline} and \textsl{Rcpp Attributes}, respectively, before a short concluding summary. \section{Motivation: fastLm} Fitting linear models is a key building block of analysing and modeling data. \proglang{R} has a very complete and feature-rich function in \texttt{lm()} which provides a model fit as well as a number of diagnostic measure, either directly or via the \texttt{summary()} method for linear model fits. The \texttt{lm.fit()} function provides a faster alternative (which is however recommend only for for advanced users) which provides estimates only and fewer statistics for inference. This may lead to user requests for a routine which is both fast and featureful enough. The \texttt{fastLm} routine shown here provides such an implementation as part of the \pkg{RcppGSL} package. It uses the \pkg{GSL} for the least-squares fitting functions and provides a nice example for \pkg{GSL} integration with \proglang{R}. ```cpp #include #include #include // declare a dependency on the RcppGSL package; // also activates plugin (but not needed when // 'LinkingTo: RcppGSL' is used with a package) // // [[Rcpp::depends(RcppGSL)]] // tell Rcpp to turn this into a callable // function called 'fastLm' // // [[Rcpp::export]] Rcpp::List fastLm(const RcppGSL::Matrix & X, const RcppGSL::Vector & y) { // row and column dimension int n = X.nrow(), k = X.ncol(); double chisq; // to hold the coefficient vector RcppGSL::Vector coef(k); // and the covariance matrix RcppGSL::Matrix cov(k,k); // the actual fit requires working memory // which we allocate and then free gsl_multifit_linear_workspace *work = gsl_multifit_linear_alloc (n, k); gsl_multifit_linear (X, y, coef, cov, &chisq, work); gsl_multifit_linear_free (work); // assign diagonal to a vector, then take // square roots to get std.error Rcpp::NumericVector std_err; // need two step decl. and assignment std_err = gsl_matrix_diagonal(cov); // sqrt() is an Rcpp sugar function std_err = Rcpp::sqrt(std_err); return Rcpp::List::create( Rcpp::Named("coefficients") = coef, Rcpp::Named("stderr") = std_err, Rcpp::Named("df.residual") = n - k); } ``` The function interface defines two \pkg{RcppGSL} variables: a matrix and a vector. Both use the standard numeric type \texttt{double} as discussed below. The \pkg{GSL} supports other types ranging from lower precision floating point to signed and unsigned integers as well as complex numbers. The vector and matrix classes are templated for use with all these \proglang{C} / \proglang{C++} types---though \proglang{R} uses only \texttt{double} and \texttt{int}. For these latter two, we offer a shorthand definition via a \texttt{typedef} which allows a shorter non-template use. Having extracted the row and column dimentions, we then reserve another vector and matrix to hold the resulting coefficient estimates as well as the estimate of the covariance matrix. Next, we allocate workspace using a \pkg{GSL} routine, fit the linear model and free the just-allocated workspace. The next step involves extracting the diagonal element from the covariance matrix, and taking the square root (using a vectorised function from \pkg{Rcpp}). Finally we create a named list with the return values. In earlier version of the \pkg{RcppGSL} package, we also explicitly called \texttt{free()} to return temporary memory allocation to the operating system. This step had to be done as the underlying objects are managed as \proglang{C} objects. They conform to the \pkg{GSL} interface, and work without any automatic memory management. But as we provide a \proglang{C++} data structure for matrix and vector objects, we can manage them using \proglang{C++} facilities. In particular, the destructor can free the memory when the object goes out of scope. Explicit \texttt{free()} calls are still permitted as we keep track the object status so that memory cannot accidentally be released more than once. Another more recent addition permits use of \texttt{const \&} in the interface. This instructs the compiler that values of the corresponding variable will not be altered, and are passed into the function by reference rather than by value. We note that \pkg{RcppArmadillo} \citep{CRAN:RcppArmadillo,Eddelbuettel+Sanderson:2013:RcppArmadillo} implements a matching \texttt{fastLm} function using the Armadillo library by \cite{Sanderson:2010:Armadillo}, and can do so with even more compact code due to \proglang{C++} features. Moreover, \pkg{RcppEigen} \citep{CRAN:RcppEigen,JSS:RcppEigen} provides a \texttt{fastLm} implementation with a comprehensive comparison of matrix decomposition methods. # Vectors This section details the different vector represenations, starting with their definition inside the \pkg{GSL}. We then discuss our layering before showing how the two types map. A discussion of read-only `vector view' classes concludes the section. ## \pkg{GSL} Vectors \pkg{GSL} defines various vector types to manipulate one-dimensionnal data, similar to \proglang{R} arrays. For example the \verb|gsl_vector| and \verb|gsl_vector_int| structs are defined as: ```cpp typedef struct{ size_t size; size_t stride; double * data; gsl_block * block; int owner; } gsl_vector; typedef struct { size_t size; size_t stride; int * data; gsl_block_int * block; int owner; } gsl_vector_int; ``` A typical use of the \verb|gsl_vector| struct is given below: ```cpp int i; // allocate a gsl_vector of size 3 gsl_vector *v = gsl_vector_alloc(3); // fill the vector for (i = 0; i < 3; i++) { gsl_vector_set(v, i, 1.23 + i); } // access elements double sum = 0.0; for (i = 0; i < 3; i++) { sum += gsl_vector_set(v, i); } // free the memory gsl_vector_free(v); ``` Note that we have to explicitly free the allocated memory at the end. With \proglang{C}-style programming, this step is always the responsibility of the programmer. ## RcppGSL::vector \pkg{RcppGSL} defines the template \texttt{RcppGSL::vector} to manipulate \verb|gsl_vector| pointers taking advantage of C++ templates. Using this template type, the previous example now becomes: ```cpp int i; // allocate a gsl_vector of size 3 RcppGSL::vector v(3); // fill the vector for (i = 0; i < 3; i++) { v[i] = 1.23 + i; } // access elements double sum = 0.0; for (i = 0; i < 3; i++) { sum += v[i]; } // (optionally) free the memory // also automatic when out of scope v.free(); ``` The class \texttt{RcppGSL::vector} is a smart pointer which can be deployed anywhere where a raw pointer \verb|gsl_vector| can be used, such as the \verb|gsl_vector_set| and \verb|gsl_vector_get| functions above. Beyond the convenience of a nicer syntax for allocation (and of course the managed release of memory either via \texttt{free()} or when going out of scope), the \texttt{RcppGSL::vector} template faciliates interchange of \pkg{GSL} vectors with \pkg{Rcpp} objects, and hence \pkg{R} objects. The following example defines a \texttt{.Call} compatible function called \verb|sum_gsl_vector_int| that operates on a \verb|gsl_vector_int| through the \texttt{RcppGSL::vector} template specialization: ```cpp // [[Rcpp::export]] int sum_gsl_vector_int(const RcppGSL::vector & vec) { int res = std::accumulate(vec.begin(), vec.end(), 0); return res; } ``` Here we no longer need to call \texttt{free()} explicitly as the \texttt{vec} allocation is returned automatically at the end of the function body when the variable goes out of scope. Once the function has created via \texttt{sourceCpp()} or \texttt{cppFunction()} from \textsl{Rcpp Attributes} (see section \ref{sec:attributes} for more on this), it can then be called from \proglang{R} : ```{r inlineex1} fx <- Rcpp::cppFunction(" int sum_gsl_vector_int(RcppGSL::vector vec) { int res = std::accumulate(vec.begin(), vec.end(), 0); return res; }", depends="RcppGSL") sum_gsl_vector_int(1:10) ``` A second example shows a simple function that grabs elements of an R list as \verb|gsl_vector| objects using implicit conversion mechanisms of \pkg{Rcpp} ```cpp // [[Rcpp::export]] double gsl_vector_sum_2(const Rcpp::List & data) { // grab "x" as a gsl_vector through the // RcppGSL::vector class const RcppGSL::vector x = data["x"]; // grab "y" as a gsl_vector through the // RcppGSL::vector class const RcppGSL::vector y = data["y"]; double res = 0.0; for (size_t i=0; i< x->size; i++) { res += x[i] * y[i]; } // return result, memory freed automatically return res; } ``` called from \proglang{R}: ```{r inlinexex2} Rcpp::cppFunction(" double gsl_vector_sum_2(Rcpp::List data) { RcppGSL::vector x = data[\"x\"]; RcppGSL::vector y = data[\"y\"]; double res = 0.0; for (size_t i=0; i< x->size; i++) { res += x[i] * y[i]; } return res; }", depends= "RcppGSL") data <- list( x = seq(0,1,length=10), y = 1:10 ) gsl_vector_sum_2(data) ``` ## Mapping Table \ref{tab:mappingVectors} shows the mapping between types defined by the \pkg{GSL} and their corresponding types in the \pkg{RcppGSL} package. \begin{table*}[htb] \centering \begin{small} \begin{tabular}{ll} \toprule GSL vector & RcppGSL \\ \midrule \texttt{gsl\_vector} & \texttt{RcppGSL::vector} as well as \texttt{RcppGSL::Vector} \\ \texttt{gsl\_vector\_int} & \texttt{RcppGSL::vector} as well as \texttt{RcppGSL::IntVector} \\ \texttt{gsl\_vector\_float} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_long} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_char} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_complex} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_complex\_float} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_complex\_long\_double} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_long\_double} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_short} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_uchar} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_uint} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_ushort} & \texttt{RcppGSL::vector} \\ \texttt{gsl\_vector\_ulong} & \texttt{RcppGSL::vector} \\ \bottomrule \end{tabular} \end{small} \caption{Correspondance between \pkg{GSL} vector types and templates defined in \pkg{RcppGSL}.} \label{tab:mappingVectors} \end{table*} As shown, we also define two convenient shortcuts for the very common case of \texttt{double} and \texttt{int} vectors. First, \texttt{RcppGSL::Vector} is a short-hand for the \texttt{RcppGSL::vector} template instantiation. Second, \texttt{RcppGSL::IntVector} does the same for integer-valued vectors. Other types still require explicit templates. ## Vector Views Several \pkg{GSL} algorithms return \pkg{GSL} vector views as their result type. \pkg{RcppGSL} defines the template class \texttt{RcppGSL::vector\_view} to handle vector views using \proglang{C++} syntax. ```cpp // [[Rcpp::export]] Rcpp::List test_gsl_vector_view() { int n = 10; RcppGSL::vector v(n); for (int i=0 ; i v_even = gsl_vector_subvector_with_stride(v,0,2,n/2); const RcppGSL::vector_view v_odd = gsl_vector_subvector_with_stride(v,1,2,n/2); return Rcpp::List::create( Rcpp::Named("even") = v_even, Rcpp::Named("odd" ) = v_odd); } ``` As with vectors, \proglang{C++} objects of type \texttt{RcppGSL::vector\_view} can be converted implicitly to their associated \pkg{GSL} view type. Table \ref{tab:mappingVectorViews} displays the pairwise correspondance so that the \proglang{C++} objects can be passed to compatible \pkg{GSL} algorithms. \begin{table*}[htb] \centering \begin{small} \begin{tabular}{ll} \toprule gsl vector views & RcppGSL \\ \midrule \texttt{gsl\_vector\_view} & \texttt{RcppGSL::vector\_view}; \texttt{RcppGSL::VectorView} \\ \texttt{gsl\_vector\_view\_int} & \texttt{RcppGSL::vector\_view}; \texttt{RcppGSL::IntVectorView} \\ \texttt{gsl\_vector\_view\_float} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_long} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_char} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_complex} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_complex\_float} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_complex\_long\_double} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_long\_double} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_short} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_uchar} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_uint} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_ushort} & \texttt{RcppGSL::vector\_view} \\ \texttt{gsl\_vector\_view\_ulong} & \texttt{RcppGSL::vector\_view} \\ \bottomrule \end{tabular} \end{small} \caption{Correspondance between \pkg{GSL} vector view types and templates defined in \pkg{RcppGSL}.} \label{tab:mappingVectorViews} \end{table*} The vector view class also contains a conversion operator to automatically transform the data of the view object to a \pkg{GSL} vector object. This enables use of vector views where \pkg{GSL} would expect a vector. And as before, \texttt{double} and \texttt{int} types can be accessed via the \texttt{typedef} variants \texttt{RcppGSL::VectorView} and \texttt{RcppGSL::IntVectorView}, respectively. Lastly, in order to support \texttt{const \&} behaviour, all \texttt{gsl\_vector\_XXX\_const\_view} variants are also supported (where \texttt{XXX} stands for any of the atomistic \proglang{C} and \proglang{C++} data types). # Matrices The \pkg{GSL} also defines a set of matrix data types : \texttt{gsl\_matrix}, \texttt{gsl\_matrix\_int} etc ... for which \pkg{RcppGSL} defines a corresponding convenience \proglang{C++} wrapper generated by the \texttt{RcppGSL::matrix} template. ## Creating matrices The \texttt{RcppGSL::matrix} template exposes three constructors. ```cpp // convert an R matrix to a GSL matrix matrix(SEXP x) throw(::Rcpp::not_compatible) // encapsulate a GSL matrix pointer matrix(gsl_matrix* x) // create a new matrix with the given // number of rows and columns matrix(int nrow, int ncol) ``` ## Implicit conversion \texttt{RcppGSL::matrix} defines an implicit conversion to a pointer to the associated \pkg{GSL} matrix type, as well as dereferencing operators. This makes the class \texttt{RcppGSL::matrix} look and feel like a pointer to a \pkg{GSL} matrix type. ```cpp gsltype* data; operator gsltype*() { return data; } gsltype* operator->() const { return data; } gsltype& operator*() const { return *data; } ``` ## Indexing Indexing of \pkg{GSL} matrices is usually the task of the functions \texttt{gsl\_matrix\_get}, \texttt{gsl\_matrix\_int\_get}, ... and \texttt{gsl\_matrix\_set}, \texttt{gsl\_matrix\_int\_set}, ... \pkg{RcppGSL} takes advantage of both operator overloading and templates to make indexing a \pkg{GSL} matrix much more convenient. ```cpp // create a matrix of size 10x10 RcppGSL::matrix mat(10,10); // fill the diagonal, no need for setter function for (int i=0; i<10: i++) { mat(i,i) = i; } ``` ## Methods The \texttt{RcppGSL::matrix} type also defines the following member functions: \begin{quote} \begin{itemize} \item[\texttt{nrow}] extracts the number of rows \item[\texttt{ncol}] extract the number of columns \item[\texttt{size}] extracts the number of elements \item[\texttt{free}] releases the memory (also called via destructor) \end{itemize} \end{quote} ## Matrix views Similar to the vector views discussed above, the \pkg{RcppGSL} also provides an implicit conversion operator which returns the underlying matrix stored in the matrix view class. ## Error handler When input values for \pkg{GSL} functions are invalid, the default error handler will abort the program after printing an error message. This leads \proglang{R} to an abortion error. To avoid this behaviour, one needs to avoid it first by using \texttt{gsl\_set\_error\_handler\_off()}, and then detect error conditions by checking whether the result is \texttt{NAN} or not. ```cpp // close the GSL error handler gsl_set_error_handler_off(); // call GSL function with some invalid values double res = gsl_sf_hyperg_2F1(1, 1, 1.1467003, 1); // detect the result is NAN or not if (ISNAN(res)) { Rcpp::Rcout << "Invalid input found!" << std::endl; } ``` See \url{http://thread.gmane.org/gmane.comp.lang.r.rcpp/7905} for a longer discussion of the related issues. Starting with release 0.2.4, two new functions are available: \texttt{gslSetErrorHandlerOff()} and \texttt{gslResetErrorHandler()} which allow to turn off the error handler (as discussed above), and to reset to the prior (default) value. In addition, the package now also calls \texttt{gslSetErrorHandlerOff()} when being attached, ensuring that the \pkg{GSL} error handler is turned off by default. # Using \pkg{RcppGSL} in your package The \pkg{RcppGSL} package contains a complete example package providing a single function \texttt{colNorm} which computes a norm for each column of a supplied matrix. This example adapts a matrix example from the \pkg{GSL} manual that has been chosen primarily as a means to showing how to set up a package to use \pkg{RcppGSL}. Needless to say, we could compute such a matrix norm easily in \proglang{R} using existing facilities. One such possibility is a simple \verb|apply(M, 2, function(x) sqrt(sum(x^2)))| as shown on the corresponding help page in the example package inside \pkg{RcppGSL}. One point in favour of using the \pkg{GSL} code is that it employs a BLAS function so on sufficiently large matrices, and with suitable BLAS libraries installed, this variant could be faster due to the optimised code in high-performance BLAS libraries and/or the inherent parallelism a multi-core BLAS variant which compute compute the vector norm in parallel. On all `reasonable' matrix sizes, however, the performance difference should be neglible. ## The \texttt{configure} script ### Using autoconf Using \pkg{RcppGSL} means employing both the \pkg{GSL} and \proglang{R}. We may need to find the location of the \pkg{GSL} headers and library, and this done easily from a \texttt{configure} source script which \texttt{autoconf} generates from a \texttt{configure.in} source file such as the following: ```sh AC_INIT([RcppGSLExample], 0.1.0) ## Use gsl-config to find arguments for ## compiler and linker flags ## ## Check for non-standard programs: gsl-config(1) AC_PATH_PROG([GSL_CONFIG], [gsl-config]) ## If gsl-config was found, let's use it if test "${GSL_CONFIG}" != ""; then # Use gsl-config for header and linker args # (without BLAS which we get from R) GSL_CFLAGS=`${GSL_CONFIG} --cflags` GSL_LIBS=`${GSL_CONFIG} --libs-without-cblas` else AC_MSG_ERROR([gsl-config not found, is GSL installed?]) fi # Now substitute these variables in src/Makevars.in to create src/Makevars AC_SUBST(GSL_CFLAGS) AC_SUBST(GSL_LIBS) AC_OUTPUT(src/Makevars) ``` A source file such as this \texttt{configure.in} gets converted into a script \texttt{configure} by invoking the \texttt{autoconf} program. We note that many other libraries use a similar (but somewhat newer and by-now fairly standard) scripting frontend called \texttt{pkg-config} which be deployed in a very similar by other packages. Calls such as the following two can be used from \texttt{configure} in a very similar manner: ```sh pkg-config --cflags libpng pkg-config --libs libpng ``` where \texttt{libpng} (for the png image format) is used just for illustration. ### Using functions provided by RcppGSL \pkg{RcppGSL} provides R functions (in the file \texttt{R/inline.R}) that allow us to retrieve the same information. Therefore the configure script can also be written as: ```sh #!/bin/sh GSL_CFLAGS=`${R_HOME}/bin/Rscript -e \ "RcppGSL:::CFlags()"` GSL_LIBS=`${R_HOME}/bin/Rscript -e \ "RcppGSL:::LdFlags()"` sed -e "s|@GSL_LIBS@|${GSL_LIBS}|" \ -e "s|@GSL_CFLAGS@|${GSL_CFLAGS}|" \ src/Makevars.in > src/Makevars ``` Similarly, the configure.win for windows can be written as: ```sh GSL_CFLAGS=`${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe\ -e "RcppGSL:::CFlags()"` GSL_LIBS=`${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe \ -e "RcppGSL:::LdFlags()"` sed -e "s|@GSL_LIBS@|${GSL_LIBS}|" \ -e "s|@GSL_CFLAGS@|${GSL_CFLAGS}|" \ src/Makevars.in > src/Makevars.win ``` This allows for a simpler and more direct way to just set the compile and link options, taking advantage of the installed \pkg{RcppGSL} package. See the \pkg{RcppZiggurat} package for an example. ## The \texttt{src} directory The \proglang{C++} source file takes the matrix supplied from \proglang{R} and applies the \pkg{GSL} function to each column. ```cpp #include #include #include // [[Rcpp::export]] Rcpp::NumericVector colNorm(const RcppGSL::Matrix & G) { int k = G.ncol(); Rcpp::NumericVector n(k); // results for (int j = 0; j < k; j++) { RcppGSL::vector_view colview = gsl_matrix_const_column(G, j); n[j] = gsl_blas_dnrm2(colview); } return n; // return } ``` The \proglang{Makevars.in} file governs the compilation and uses the values supplied by \texttt{configure} during build-time: ```sh # set by configure GSL_CFLAGS = @GSL_CFLAGS@ GSL_LIBS = @GSL_LIBS@ # combine with standard arguments for R PKG_CPPFLAGS = $(GSL_CFLAGS) PKG_LIBS = $(GSL_LIBS) ``` The variables surrounded by \@ will be filled by \texttt{configure} during package build-time. As discussed above, this can either rely on \texttt{autoconf} or a possibly-simpler \texttt{Rscript}. ## The \texttt{R} directory The \proglang{R} source is very simply: it contains a single file created by the \texttt{Rcpp::compileAttributes()} function implementing the wrapper to the \texttt{colNorm()} function. # Using \pkg{RcppGSL} with \pkg{inline} The \pkg{inline} package \citep{CRAN:inline} is very helpful for prototyping code in \proglang{C}, \proglang{C++} or \proglang{Fortran} as it takes care of code compilation, linking and dynamic loading directly from \proglang{R}. It has been used extensively by \pkg{Rcpp}, for example in the numerous unit tests. The example below shows how \pkg{inline} can be deployed with \pkg{RcppGSL}. We implement the same column norm example, but this time as an \proglang{R} script which is compiled, linked and loaded on-the-fly. Compared to standard use of \pkg{inline}, we have to make sure to add a short section declaring which header files from \pkg{GSL} we need to use; the \pkg{RcppGSL} then communicates with \pkg{inline} to tell it about the location and names of libraries used to build code against \pkg{GSL}. ```cpp require(inline) inctxt=' #include #include ' bodytxt=' // create data structures from SEXP RcppGSL::matrix M = sM; int k = M.ncol(); // to store results Rcpp::NumericVector n(k); for (int j = 0; j < k; j++) { RcppGSL::vector_view colview = gsl_matrix_column (M, j); n[j] = gsl_blas_dnrm2(colview); } return n; ' foo <- cxxfunction( signature(sM="numeric"), body=bodytxt, inc=inctxt, plugin="RcppGSL") # see Section 8.4.13 of the GSL manual: # create M as a sum of two outer products M <- outer(sin(0:9), rep(1,10), "*") + outer(rep(1, 10), cos(0:9), "*") foo(M) ``` The \texttt{RcppGSL} inline plugin supports creation of a package skeleton based on the inline function. ```{r exskel, eval=FALSE} package.skeleton("mypackage", foo) ``` # Using \pkg{RcppGSL} with Rcpp Attributes \label{sec:attributes} \textsl{Rcpp Attributes} \citep{CRAN:Rcpp:Attributes} builds on the features of the \pkg{inline} package described in previous section, and streamlines the compilation, loading and linking process even further. It leverages the existing plugins for \pkg{inline}. We already showed the corresponding function in the previous section. Here, we show it again as a self-contained example used via \texttt{sourceCpp()}. We stress that usage is \texttt{sourceCpp()} is meant for interactive work at the R command-prompt, but is not the recommended practice in a package. ```cpp #include #include #include // declare a dependency on the RcppGSL package; // also activates plugin // // [[Rcpp::depends(RcppGSL)]] // declare the function to be 'exported' to R // // [[Rcpp::export]] Rcpp::NumericVector colNorm(const RcppGSL::Matrix & M) { int k = M.ncol(); Rcpp::NumericVector n(k); // results for (int j = 0; j < k; j++) { RcppGSL::VectorView colview = gsl_matrix_const_column (M, j); n[j] = gsl_blas_dnrm2(colview); } return n; // return } /*** R ## see Section 8.4.13 of the GSL manual: ## create M as a sum of two outer products M <- outer(sin(0:9), rep(1,10), "*") + outer(rep(1, 10), cos(0:9), "*") colNorm(M) */ ``` With the code above stored in a file, say, ``gslNorm.cpp'' one can simply call \texttt{sourceCpp()} to have the wrapper code added, and all of the compilation, linking and loading done --- including the execution of the short \proglang{R} segment at the end: ```{r exnorm, eval=FALSE} sourceCpp("gslNorm.cpp") ``` The function \texttt{cppFunction()} is also available to convert a simple character string argument containing a valid C++ function into a eponymous R function. And like \texttt{sourceCpp()}, it can also use plugins. See the vignette ``Rcpp-attributes'' \citep{CRAN:Rcpp:Attributes} of the \pkg{Rcpp} package \citep{CRAN:Rcpp} for full details. # Summary The GNU Scientific Library (GSL) by \citet{GSL} offers a very comprehensive collection of rigorously developed and tested functions for applied scientific computing under a widely-used and well-understood Open Source license. This has lead to widespread deployment of \pkg{GSL} among a number of disciplines. Using the automatic wrapping and converters offered by the \pkg{RcppGSL} package presented here, \proglang{R} users and programmers can now deploy algorithmns provided by the \pkg{GSL} with greater ease. \newpage RcppGSL/vignettes/RcppGSL-unitTests.Rnw0000644000176200001440000000362712774327533017523 0ustar liggesusers\documentclass[10pt]{article} %\VignetteIndexEntry{RcppGSL-unitTests} %\VignetteKeywords{R,GSL,Rcpp,unit tests} %\VignettePackage{RcppGSL} \usepackage{vmargin} \setmargrb{0.75in}{0.75in}{0.75in}{0.75in} \RequirePackage{ae,mathpple} % ae as a default font pkg works with Sweave \RequirePackage[T1]{fontenc} <>= require(RcppGSL) prettyVersion <- packageDescription("RcppGSL")$Version prettyDate <- format(Sys.Date(), "%B %e, %Y") library(RUnit) @ \usepackage[colorlinks]{hyperref} \author{Dirk Eddelbuettel \and Romain Fran\c{c}ois} \title{\texttt{RcppGSL}: Unit testing results} \date{\texttt{RcppGSL} version \Sexpr{prettyVersion} as of \Sexpr{prettyDate}} \begin{document} \maketitle \section*{Test Execution} <>= pkg <- "RcppGSL" if (file.exists("unitTests-results")) unlink("unitTests-results", recursive = TRUE) dir.create("unitTests-results") path <- system.file("unitTests", package=pkg) testSuite <- defineTestSuite(name=paste(pkg, "unit testing"), dirs=path) tests <- runTestSuite(testSuite) err <- getErrors(tests) if (err$nFail > 0) stop(sprintf("unit test problems: %d failures", err$nFail)) if (err$nErr > 0) stop( sprintf("unit test problems: %d errors", err$nErr)) printHTMLProtocol(tests, fileName= sprintf("unitTests-results/%s-unitTests.html", pkg)) printTextProtocol(tests, fileName= sprintf("unitTests-results/%s-unitTests.txt" , pkg)) #if (file.exists("/tmp")) { # invisible(sapply(c("txt", "html"), function(ext) { # fname <- sprintf("unitTests-results/%s-unitTests.%s", pkg, ext) # file.copy(fname, "/tmp", overwrite=TRUE) # })) #} @ \section*{Test Results} \begin{verbatim} <>= results <- "unitTests-results/RcppGSL-unitTests.txt" if (file.exists(results)) { writeLines(readLines(results)) } else{ writeLines( "unit test results not available" ) } @ \end{verbatim} \end{document} RcppGSL/README.md0000644000176200001440000000636113070437421013012 0ustar liggesusers## RcppGSL [![Build Status](https://travis-ci.org/eddelbuettel/rcppgsl.svg)](https://travis-ci.org/eddelbuettel/rcppgsl) [![License](http://img.shields.io/badge/license-GPL%20%28%3E=%202%29-brightgreen.svg?style=flat)](http://www.gnu.org/licenses/gpl-2.0.html) [![CRAN](http://www.r-pkg.org/badges/version/RcppGSL)](https://cran.r-project.org/package=RcppGSL) [![Downloads](http://cranlogs.r-pkg.org/badges/RcppGSL?color=brightgreen)](http://www.r-pkg.org/pkg/RcppGSL) This package uses [Rcpp](https://github.com/RcppCore/Rcpp) to connect the [R](https://www.r-project.org) system to the [GNU GSL](http://www.gnu.org/software/gsl/), a collection of numerical routines for scientific computing, particularly its vector and matrix classes. ### Examples #### Faster `lm()` for OLS regression The `fastLm()` function [included as file `src/fastLm.cpp`](https://github.com/eddelbuettel/rcppgsl/blob/master/src/fastLm.cpp) in the package: ```{.cpp} #include #include #include // [[Rcpp::export]] Rcpp::List fastLm(const RcppGSL::Matrix &X, const RcppGSL::Vector &y) { int n = X.nrow(), k = X.ncol(); double chisq; RcppGSL::Vector coef(k); // to hold the coefficient vector RcppGSL::Matrix cov(k,k); // and the covariance matrix // the actual fit requires working memory we allocate and free gsl_multifit_linear_workspace *work = gsl_multifit_linear_alloc (n, k); gsl_multifit_linear (X, y, coef, cov, &chisq, work); gsl_multifit_linear_free (work); // assign diagonal to a vector, then take square roots to get std.error Rcpp::NumericVector std_err; std_err = gsl_matrix_diagonal(cov); // need two step decl. and assignment std_err = Rcpp::sqrt(std_err); // sqrt() is an Rcpp sugar function return Rcpp::List::create(Rcpp::Named("coefficients") = coef, Rcpp::Named("stderr") = std_err, Rcpp::Named("df.residual") = n - k); } ``` #### A simple column norm This example comes from the [complete example package included in RcppGSL](https://github.com/eddelbuettel/rcppgsl/tree/master/inst/examples/RcppGSLExample) and is from [the file `inst/examples/RcppGSLExample/src/colNorm.cpp`](https://github.com/eddelbuettel/rcppgsl/blob/master/inst/examples/RcppGSLExample/src/colNorm.cpp) ```{.cpp} #include #include #include // [[Rcpp::export]] Rcpp::NumericVector colNorm(const RcppGSL::Matrix & G) { int k = G.ncol(); Rcpp::NumericVector n(k); // to store results for (int j = 0; j < k; j++) { RcppGSL::VectorView colview = gsl_matrix_const_column (G, j); n[j] = gsl_blas_dnrm2(colview); } return n; // return vector } ``` ### Dependencies - [GNU GSL](http://www.gnu.org/software/gsl/) library (eg [libgsl0-dev](https://packages.debian.org/sid/libgsl0-dev) on Debian or Ubuntu) - [Rcpp](https://github.com/RcppCore/Rcpp) for seamless R and C++ integration ### Availabililty On [CRAN](https://cran.r-project.org), here and on [its package page](http://dirk.eddelbuettel.com/code/rcpp.gsl.html). ### Authors Dirk Eddelbuettel and Romain Francois ### License GPL (>= 2) RcppGSL/MD50000644000176200001440000000707413161742552012052 0ustar liggesusers065acb33af2acc8ef00dfdfee1779ee7 *ChangeLog ecfaba3ca8365aa499d5f8d058be3786 *DESCRIPTION ab224aaaef366cc78e9ed7430feeea9f *NAMESPACE 4ae937de8397e60712c56229703c16f5 *R/RcppExports.R f7d8b7cbd39e593ba8ddf03a43279dc9 *R/fastLm.R 54fb893fc5a89aa3f3b01e949863f87e *R/init.R e3e6b6943ffd2cc94aca47745e502d33 *R/inline.R c5b9b7ab289082d2db470406ae4ec06c *README.md 3883e612bd76f4fe2df73a4bcaf28164 *TODO 151ddbd1deed1dce79d09e6f8d9579e9 *build/vignette.rds f18d78c02f7b4646acd3897c83bda9b9 *cleanup 6d8f0f03f3eafe94a80f750e5c9b3622 *configure 7697313f7223ee0547730856221945b2 *configure.ac 50e9d4c55b411d6e819a7cc84e8bab1e *inst/NEWS.Rd 95fc42dfaa4077d66daaf8c817e56903 *inst/doc/RcppGSL-intro.R c91507481f9689fd08baa436de5a327e *inst/doc/RcppGSL-intro.Rmd a203c5bc282dcdb27682079a20c4d462 *inst/doc/RcppGSL-intro.pdf 0f98c4d25edede758e9506ca23f53b82 *inst/doc/RcppGSL-unitTests.R 6dda354a3fdcc41d415aab82a49daec6 *inst/doc/RcppGSL-unitTests.Rnw f80e228f9cc57d11a3f969afc827c331 *inst/doc/RcppGSL-unitTests.pdf acd73645dbdb9731139253720713e1f7 *inst/examples/RcppGSLExample/DESCRIPTION ca4d3febd8107be64527dbb80674d7f0 *inst/examples/RcppGSLExample/NAMESPACE 2ad3558b32896f6b90dfca1b628b04ea *inst/examples/RcppGSLExample/R/RcppExports.R 7028487129d9f40d52a3c48c20ca3844 *inst/examples/RcppGSLExample/R/colNorm.R 1ac375832dc27870605fc0ce9da94ad5 *inst/examples/RcppGSLExample/configure 2661ff215b5d361a6793c6b7ebe27068 *inst/examples/RcppGSLExample/configure.ac 3fb2609d195a6961ed89b207212c6413 *inst/examples/RcppGSLExample/man/colNorm.Rd 2d84d061699ad784812b605eeb65f22b *inst/examples/RcppGSLExample/src/Makevars.in d69db63928d9ef7801148b48c32c2ec9 *inst/examples/RcppGSLExample/src/Makevars.win d331e79613b49b2ef102625d49d79bc9 *inst/examples/RcppGSLExample/src/RcppExports.cpp 5948ec85a185c10736ef394861b6b1da *inst/examples/RcppGSLExample/src/colNorm.cpp 70f028ea1e8306b481e28da92536d8e1 *inst/examples/bSpline/bSpline.R a051860124731442375a4f826d1a4244 *inst/examples/bSpline/bSpline.cpp e2747f0b0e0c7528fd9dbefedaf0d5ef *inst/include/RcppGSL.h 008f4d38afad7975fad895ec2c5aac78 *inst/include/RcppGSLForward.h 5469c3710b3cb3727c24d8990188f066 *inst/include/RcppGSL_matrix.h 717041692ed278218de705e9dcd823bd *inst/include/RcppGSL_matrix_view.h fb053660cb0ba2e120de8f380ae53f01 *inst/include/RcppGSL_typedef.h 5db5f8f20bb4ac02cdd22244eeb1bb9f *inst/include/RcppGSL_types.h 2d281fc07605d3561097cf6ea0b48dda *inst/include/RcppGSL_vector.h 419dde941afd8b3f20e25a63c42a507a *inst/include/RcppGSL_vector_view.h eac5eab6352e9a97236f9e11d6069144 *inst/skeleton/Makevars.in 528fb571247b8c0c4db3fda62c047d3d *inst/skeleton/Makevars.win 7ac30c30f4eb7df2278ec84f4e457152 *inst/skeleton/configure af35ab71c2fb50246cdd9ba0512a9e17 *inst/skeleton/configure.win 8cf6b1cf5d563c496afc8b2a9eeb49f6 *inst/unitTests/cpp/gsl.cpp 0041e35ff86e5a7d46ec9d8f3a2851c3 *inst/unitTests/runit.client.package.R b59b394800eb06497aeb04b807d659c7 *inst/unitTests/runit.fastLm.R 8d6aa3dfd23e6dd561067b0a7705c65f *inst/unitTests/runit.gsl.R 7b5c55ec1f47446552acba44e1aae221 *man/LdFlags.Rd 82d3b6c4ed8dd0c31d26b740bbf5238b *man/RcppGSL-package.Rd f3e4ad226e495088c31bfe72a17cbf3f *man/fastLm.Rd d140432b43a8c7ef13efd4f9c3d082d8 *src/Makevars.in ebaff9be3b59a1f76ccd9b400d278a04 *src/Makevars.win fcecc3b7db20ec2f2bdb63cf3feac0e4 *src/RcppExports.cpp ec227745a908b2cee282119745407088 *src/fastLm.cpp 0ed3763b4c2fcc1dc6c12209ae757908 *src/init.c 9cfa37e70e40833dd2607a100c7d2e33 *src/setErrorHandler.cpp 52b9db5ab9b67fe30d328212540182f2 *tests/doRUnit.R c91507481f9689fd08baa436de5a327e *vignettes/RcppGSL-intro.Rmd 6dda354a3fdcc41d415aab82a49daec6 *vignettes/RcppGSL-unitTests.Rnw RcppGSL/build/0000755000176200001440000000000013161737707012637 5ustar liggesusersRcppGSL/build/vignette.rds0000644000176200001440000000037313161737707015201 0ustar liggesusers‹•PË ¤_MŒšÆ{À~…уLíÁ+š4±´LãÍ/·‚…ªDM<»³;;À ò€UCuõÕ™>pFê%ǪZï·‹œI^ÆIALan g–Ë” )â„Õ{`šÜaç·ZE²ïjºøΞ8^ ~pªá³à¹~[8mÈ´yv4|þ iª‰ÇKIŽ™È(ÿ“h›‘l·ƒüw=† jWÖ·ÔU~¢¶!Íe—ÀÝråJ–´¢Œ“7ôR—œ¸B#^Ö±ë\ÕÕ4ÍÍut Description: '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. License: GPL (>= 2) LazyLoad: yes LinkingTo: Rcpp Imports: Rcpp (>= 0.11.0), stats Suggests: RUnit, inline, knitr, rmarkdown, pinp SystemRequirements: GNU GSL VignetteBuilder: knitr NeedsCompilation: yes Packaged: 2017-09-24 14:23:03 UTC; edd Repository: CRAN Date/Publication: 2017-09-24 14:47:06 UTC RcppGSL/configure0000755000176200001440000032513012774327533013454 0ustar liggesusers#! /bin/sh # Guess values for system-dependent variables and create Makefiles. # Generated by GNU Autoconf 2.69 for RcppGSL 0.2.3. # # # Copyright (C) 1992-1996, 1998-2012 Free Software Foundation, Inc. # # # This configure script is free software; the Free Software Foundation # gives unlimited permission to copy, distribute and modify it. ## -------------------- ## ## M4sh Initialization. ## ## -------------------- ## # Be more Bourne compatible DUALCASE=1; export DUALCASE # for MKS sh if test -n "${ZSH_VERSION+set}" && (emulate sh) >/dev/null 2>&1; then : emulate sh NULLCMD=: # Pre-4.2 versions of Zsh do word splitting on ${1+"$@"}, which # is contrary to our usage. Disable this feature. alias -g '${1+"$@"}'='"$@"' setopt NO_GLOB_SUBST else case `(set -o) 2>/dev/null` in #( *posix*) : set -o posix ;; #( *) : ;; esac fi as_nl=' ' export as_nl # Printing a long string crashes Solaris 7 /usr/bin/printf. as_echo='\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' as_echo=$as_echo$as_echo$as_echo$as_echo$as_echo as_echo=$as_echo$as_echo$as_echo$as_echo$as_echo$as_echo # Prefer a ksh shell builtin over an external printf program on Solaris, # but without wasting forks for bash or zsh. if test -z "$BASH_VERSION$ZSH_VERSION" \ && (test "X`print -r -- $as_echo`" = "X$as_echo") 2>/dev/null; then as_echo='print -r --' as_echo_n='print -rn --' elif (test "X`printf %s $as_echo`" = "X$as_echo") 2>/dev/null; then as_echo='printf %s\n' as_echo_n='printf %s' else if test "X`(/usr/ucb/echo -n -n $as_echo) 2>/dev/null`" = "X-n $as_echo"; then as_echo_body='eval /usr/ucb/echo -n "$1$as_nl"' as_echo_n='/usr/ucb/echo -n' else as_echo_body='eval expr "X$1" : "X\\(.*\\)"' as_echo_n_body='eval arg=$1; case $arg in #( *"$as_nl"*) expr "X$arg" : "X\\(.*\\)$as_nl"; arg=`expr "X$arg" : ".*$as_nl\\(.*\\)"`;; esac; expr "X$arg" : "X\\(.*\\)" | tr -d "$as_nl" ' export as_echo_n_body as_echo_n='sh -c $as_echo_n_body as_echo' fi export as_echo_body as_echo='sh -c $as_echo_body as_echo' fi # The user is always right. if test "${PATH_SEPARATOR+set}" != set; then PATH_SEPARATOR=: (PATH='/bin;/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 && { (PATH='/bin:/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 || PATH_SEPARATOR=';' } fi # IFS # We need space, tab and new line, in precisely that order. Quoting is # there to prevent editors from complaining about space-tab. # (If _AS_PATH_WALK were called with IFS unset, it would disable word # splitting by setting IFS to empty value.) IFS=" "" $as_nl" # Find who we are. Look in the path if we contain no directory separator. as_myself= case $0 in #(( *[\\/]* ) as_myself=$0 ;; *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. test -r "$as_dir/$0" && as_myself=$as_dir/$0 && break done IFS=$as_save_IFS ;; esac # We did not find ourselves, most probably we were run as `sh COMMAND' # in which case we are not to be found in the path. if test "x$as_myself" = x; then as_myself=$0 fi if test ! -f "$as_myself"; then $as_echo "$as_myself: error: cannot find myself; rerun with an absolute file name" >&2 exit 1 fi # Unset variables that we do not need and which cause bugs (e.g. in # pre-3.0 UWIN ksh). 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Dillon * inst/include/RcppGSLForward: Rework vector_view and matrix_view to better support const variants * inst/include/RcppGSL_types.h: Idem * inst/examples/RcppGSLExample/src/colNorm.cpp: Update usage example * inst/unitTests/cpp/gsl.cpp: Update unite tests 2015-08-18 Dirk Eddelbuettel * inst/examples/RcppGSLExample/src/colNorm.cpp: Use 'const &' 2015-08-16 Daniel C. Dillon * inst/include/RcppGSLForward: Add support for const references * inst/include/RcppGSL_types.h: Idem * src/fastLm: Use 'const &' in function interface 2015-07-27 Dirk Eddelbuettel * vignettes/RcppGSL-intro.Rnw (Rcpp): Added GSL usage table 2015-07-23 Dirk Eddelbuettel * vignettes/RcppGSL-intro.Rnw: Several small updates and edits 2015-07-20 Dirk Eddelbuettel * inst/include/RcppGSL_typedef.h: Also define MatrixView and VectorView 2015-07-19 Dirk Eddelbuettel * src/fastLm.cpp: Simplified thanks to reference counting and improved object management: GSL objects now in signature, and no longer require an explicit free() call (but still allow it). * inst/examples/RcppGSLExample: idem * inst/examples/bSpline: idem * vignettes/RcppGSL-intro.Rnw: idem * inst/include/*: Consistent indentation and whitespace * inst/include/RcppGSL_typedef.h: Define Matrix and Vector shorthands 2015-07-17 Dirk Eddelbuettel * inst/include/macros/RCPPGSL_SPEC.h: Add boolean to check whether 'data' remains allocated, and use it to free resources in destructor 2015-07-06 Dirk Eddelbuettel * DESCRIPTION: Release 0.2.5 * NAMESPACE: Added now-required importFrom() statements * DESCRIPTION: Added Imports: stats 2015-07-04 Dirk Eddelbuettel * vignettes/RcppGSL-intro.Rnw (Rcpp): Updated and edited throughout. 2015-06-27 Dirk Eddelbuettel * DESCRIPTION (Description, Date): More Title Case; rolled Date 2015-06-26 Dirk Eddelbuettel * inst/examples/RcppGSLExample/DESCRIPTION: Updated Date and Version * inst/examples/RcppGSLExample/src/colNorm.cpp: Added new version based on Attributes * inst/examples/RcppGSLExample/src/RcppExports.cpp: Added * inst/examples/RcppGSLExample/R/RcppExports.R: Added * inst/examples/RcppGSLExample/R/colNorm.R: Retired * inst/unitTests/runit.client.package.R (test.client.package): Updated to use version 0.0.3 of RcppGSLExample * vignettes/RcppGSL-intro.Rnw (Rcpp): Updated to reflect updated example client package RcppGSLExample * src/RcppExports.cpp: Updated via newer compileAttributes() * .travis.yml (install): Use Launchpad PPA for r-cran-* packages 2015-06-24 Dirk Eddelbuettel * inst/examples/bSpline/bSpline.cpp (fitData): Removed unneccessary instantiation of a data.frame object 2015-01-24 Dirk Eddelbuettel * DESCRIPTION: Version 0.2.4 2015-01-22 Dirk Eddelbuettel * vignettes/RcppGSL-intro.Rnw: Updated to note that package turns off GSL error when loaded via .onAttach(). 2015-01-14 Dirk Eddelbuettel * src/setErrorHandler.cpp: Add two simple functions to turn off the GSL error handler (which is recommended), and to reset to the default value if so desired. Note that under the default value GSL will abort on error which is not desirable when called from R. * R/init.R (.onAttach): Call the new gslSetErrorHandler() function 2015-01-13 Qiang Kou * vignettes/RcppGSL-intro.Rnw: Add section about setting default error in GSL per http://thread.gmane.org/gmane.comp.lang.r.rcpp/7905 2015-01-10 Dirk Eddelbuettel * DESCRIPTION: Version 0.2.3 2015-01-09 Dirk Eddelbuettel * vignettes/RcppGSL-intro.Rnw: Small update for look&feel, also updated regarding configure variables 2015-01-06 Dirk Eddelbuettel * src/Makevars.in: One char correction requested by CRAN Maintainers * configure.ac: Updated and shortened * configure: Ditto 2014-05-31 Dirk Eddelbuettel * DESCRIPTION: Version 0.2.2 * inst/NEWS.Rd: Ditto 2014-05-30 Kevin Ushey * inst/include/RcppGSLForward.h: Take address of private member, not variable passed in 2014-05-26 Dirk Eddelbuettel * DESCRIPTION: Version 0.2.1 * inst/unitTests/runit.client.package.R: Add 'require(Rcpp)' which is now needed as we only Import: rather than Depends: in the example 2014-05-22 Dirk Eddelbuettel * inst/examples/RcppGSLExample/DESCRIPTION: Updated * inst/examples/RcppGSLExample/NAMESPACE: Ditto * inst/examples/RcppGSLExample/configure.ac: Ditto * inst/examples/RcppGSLExample/configure: Ditto * inst/examples/RcppGSLExample/src/Makevars.in: Ditto * inst/examples/RcppGSLExample/src/Makevars.win: Ditto 2014-05-21 Dirk Eddelbuettel * DESCRIPTION: Updated for Rcpp (>= 0.11.0) * NAMESPACE: Ditto * configure.ac: Simplified as we longer need -L flags for Rcpp * src/Makevars.in: Ditto * src/Makevars.win (PKG_LIBS): Removed call to Rcpp:::LdFlags() * R/inline.R (inlineCxxPlugin): Now call Rcpp::Rcpp.plugin.maker() * .Rbuildignore: Added 2013-10-22 Dirk Eddelbuettel * DESCRIPTION: Added 'Suggests: Rcpp' to build vignette as we call into Rcpp to get its bibtex file 2013-10-09 Dirk Eddelbuettel * NAMESPACE: Export LdFlags and CFlags * man/LdFlags.Rd: Added documentation for exported functions 2013-09-10 Dirk Eddelbuettel * DESCRIPTION (Imports): Now 'Imports: Rcpp' rather than Depends: to satisfy R CMD check for the R version under development 2013-08-24 Dirk Eddelbuettel * DESCRIPTION (Suggests): Added 'highlight' so that the package is available during 'R CMD check' in order to build vignettes * vignettes/RcppGSL-intro.Rnw: Also cite the Eddelbuettel and Sanderson (2013, CSDA) paper on RcppArmadillo 2013-06-23 Dirk Eddelbuettel * inst/unitTests/runit.gsl.R: Corrections to new unitTest scheme * inst/unitTests/cpp/gsl.cpp: Idem 2013-06-22 Dirk Eddelbuettel * inst/unitTests/runit.gsl.R: Rewritten to use sourceCpp() * inst/unitTests/cpp/gsl.cpp: New C++ file with unit tests * vignettes/RcppGSL-unitTests.Rnw: Minor tweaking * vignettes/RcppGSL-intro.Rnw: Added section on attributes * vignettes/buildVignette.R: Default to all .Rnw files in directory, also set boxes option to TRUE 2013-06-21 Dirk Eddelbuettel * vignettes/buildVignette.R: Added simple helper script to build the vignette(s) on the command-line (which requires highlight) * cleanup: Take some tasks that the vignette/Makefile had * src/fastLm.cpp: Minor improvement in computing std.error of est. 2013-06-19 Dirk Eddelbuettel * vignettes/RcppGSL/RcppGSL-intro.Rnw: Some fixes 2013-06-19 Romain Francois * vignettes/RcppGSL/RcppGSL-intro.Rnw: Converted back to use with package highlight (>= 0.4.2) 2012-11-11 Dirk Eddelbuettel * inst/examples/bSpline/bSpline.cpp: New example for B-spline fit taken from GSL manual * inst/examples/bSpline/bSpline.cpp: R wrapper and illustration 2012-10-14 Dirk Eddelbuettel * vignettes/RcppGSL/RcppGSL-intro.Rnw: Switch to using Rcpp:::bib() and the bibtex file shipped with Rcpp 2012-07-22 Dirk Eddelbuettel * DESCRIPTION: Version 0.2.0 * inst/unitTests/runit.fastLm.R: expanded unit tests * R/inline.R: Use two variables in a new package-global environment to store RcppGSL compiler and linker flags * vignettes/RcppGSL/RcppGSL-intro.Rnw: Skip use of highlight for C++ and shell snippets to sidestep build issues on 32bit OSs * vignettes/RcppGSL.bib: updated references 2012-07-21 Dirk Eddelbuettel * R/fastLm.R: expanded summary() display as in RcppArmadillo * inst/NEWS.Rd: converted from ascii text to Rd format * vignettes/*: moved from inst/doc/* per newer R Policy * vignettes/: renamed main vignette to RcppGSL-intro to not clash with the filename of the package reference manual RcppGSL.pdf * DESCRIPTION: changed Maintainer: to single person per CRAN Policy 2011-12-23 Dirk Eddelbuettel * inst/unitTests/runTests.R: unit tests output 'fallback' directory changed to '..' and files are now in top-level of $pkg.Rcheck/ 2011-12-22 Dirk Eddelbuettel * inst/include/RcppGSLForward.h (RcppGSL): Commented-out long and ulong declarations which currently clash with int64 * inst/include/RcppGSL_matrix.h (RcppGSL): Commented-out long and ulong casts which currently clash with int64 * inst/unitTests/runit.gsl.R: Disable corresponding tests 2011-06-14 Douglas Bates * R/fastLm.R, man/fastLm.Rd, src/fastLm.cpp, inst/unitTests/runit.fastLm.R: Change order of arguments in fastLm.cpp, fastLm.R, unit test and documentation. 2011-06-13 Dirk Eddelbuettel * NAMESPACE: Properly export S3methods as such * man/fastLm.Rd: Similar updates to help page 2011-04-08 Dirk Eddelbuettel * R/fastLm.R: In print.summary.fastLm(), use 'P.values' not 'P.value' 2011-04-05 Dirk Eddelbuettel * DESCRIPTION: Version 0.1.1 2011-04-04 Dirk Eddelbuettel * inst/doc/Makefile: Do not call clean in all target 2011-02-28 Dirk Eddelbuettel * inst/doc/Makefile: Call R and Rscript relative to R_HOME/bin 2011-02-11 Dirk Eddelbuettel * inst/doc/RcppGSL/Makefile: Also create unitTest vignette * inst/doc/RcppGSL/RcppGSL-unitTests.Rnw: idem * inst/doc/RcppGSL/unitTests/RcppGSL-unitTests.R: idem 2010-12-06 Romain Francois * inst/doc/RcppGSL/RcppGSL.Rnw: cosmetics 2010-11-30 Dirk Eddelbuettel * DESCRIPTION: Version 0.1.0 and initial release 2010-11-29 Romain Francois * inst/include/RcppGSLForward.h: vector_view now exposes a conversion operator to implicitely convert it to the associated gsl_vector_foo * type. Similarly matrix_view expose a conversion operator to the associated gsl matrix pointer. 2010-11-28 Dirk Eddelbuettel * inst/examples/RcppGSLExample/: Started as a means to provide a simple yet complete example of using RcppGSL in a user package * NEWS: Added with initial notes towards a release 2010-11-27 Romain Francois * include/include/*h: Updated to satisfy some grumblings from g++ 2010-11-27 Dirk Eddelbuettel * R/fastLm.R: summary() now also computed R2 and adjR2 2010-05-25 Romain Francois * inst/include/RcppGSLForward.h : add indexing operator, stl iterator and begin() and end() methods to RcppGSL::vector using proxy classes * inst/include/RcppGSLForward.h : RcppGSL::matrix gets indexing operator(int,int) * configure.win: added empty configure.win so that R CMD check does not get jealous about the configure script * src/Makevars.win: use Brian Ripley's suggestions to anticipate R 2.12.0 * inst/include/*.h: RcppGSL::vector_view and RcppGSL::matrix_view 2010-05-13 Dirk Eddelbuettel * R/fastLm.R: fastLm is now generic and behaves similar to lm(): formula interface, returns object of class 'fastLm', and had methods for print, summary and predict * man/fasttLm.Rd: documented interface accordingly * src/fastLm.cpp: Added and degrees of freedom to list of result returned from C++ to R 2010-05-13 Romain Francois * inst/include/*.h: wrap specializations are now inline * inst/include/*.h: new classes RcppGSL::matrix and RcppGSL::matrix_view 2010-05-12 Dirk Eddelbuettel * src/fastLm.cpp : added fastLm from Rcpp examples 2010-05-12 Romain Francois * inst/include/*.h: added classes RcppGSL::vector that act as smart pointers to gsl_vector_* objects. This gives nicer syntax and helps Rcpp implicit converters wrap and as. RcppGSL/man/0000755000176200001440000000000012774327533012314 5ustar liggesusersRcppGSL/man/LdFlags.Rd0000644000176200001440000000322712774327533014123 0ustar liggesusers\name{LdFlags} \alias{LdFlags} \alias{CFlags} \title{Provide RcppGSL Compiler and Linker Flags} \description{ \code{LdFlags} and \code{CFlags} return the required flags and options for the compiler and system linker in order to build against GNU GSL. This allows portable use of \pkg{RcppGSL} (which needs the GNU GSL) as package location as well as operating-system specific details are abstracted away behind the interface of this function. \code{LdFlags} and \code{CFlags} are commonly called from the files \code{Makevars} (or \code{Makevars.win}) rather than in an interactive session. } \usage{ LdFlags(print=TRUE) CFlags(print=TRUE) } \arguments{ \item{print}{A boolean determining whether the requested value is returned on the standard output, or silenly as a value.} } \value{ A character vector suitable by use by the system compiler linker in order to compile and/or link against the GNU GSK. } \details{ Thee functions are not meant to used interactively, and are intended solely for use by the build tools. The values that are returned are acquired by the package at load time. On Linux and OS X, the \code{pkg-config} program is queried. On Windows, environment variables used for GNU GSL builds with R are used. } \references{ Dirk Eddelbuettel and Romain Francois (2011). \pkg{Rcpp}: Seamless R and C++ Integration. \emph{Journal of Statistical Software}, \bold{40(8)}, 1-18. URL http://www.jstatsoft.org/v40/i08/ and available as \code{vignette("Rcpp-introduction")}. } \seealso{ The document of the \code{pkg-config} system tool. } \author{Dirk Eddelbuettel and Romain Francois} \keyword{programming} \keyword{interface} RcppGSL/man/RcppGSL-package.Rd0000644000176200001440000000113412774327533015445 0ustar liggesusers\name{RcppGSL-package} \alias{RcppGSL-package} \alias{RcppGSL} \docType{package} \title{ Glue between Rcpp and the GNU GSL } \description{ Glue between Rcpp and the GNU GSL } \details{ \tabular{ll}{ Package: \tab RcppGSL\cr Type: \tab Package\cr Version: \tab 0.0\cr Date: \tab 2010-04-04\cr License: \tab GPL (>= 2)\cr LazyLoad: \tab yes\cr } } \author{ Romain Francois and Dirk Eddelbuettel Maintainer: Dirk Eddelbuettel and Romain Francois } \references{ GSL: GNU Scientific Library: \url{http://www.gnu.org/software/gsl/} } \keyword{ package } RcppGSL/man/fastLm.Rd0000644000176200001440000000633212774327533014035 0ustar liggesusers\name{fastLm} \alias{fastLm} \alias{fastLmPure} \alias{fastLm.default} \alias{fastLm.formula} \concept{regression} \title{Bare-bones linear model fitting function} \description{ \code{fastLm} estimates the linear model using the \code{gsl_multifit_linear} function of the \code{GNU GSL} library. } \usage{ fastLmPure(X, y) fastLm(X, \dots) \method{fastLm}{default}(X, y, \dots) \method{fastLm}{formula}(formula, data = list(), \dots) } \arguments{ \item{y}{a vector containing the explained variable.} \item{X}{a model matrix.} \item{formula}{a symbolic description of the model to be fit.} \item{data}{an optional data frame containing the variables in the model.} \item{\ldots}{not used} } \details{ Linear models should be estimated using the \code{\link{lm}} function. In some cases, \code{\link{lm.fit}} may be appropriate. The \code{fastLmPure} function provides a reference use case of the \code{GSL} library via the wrapper functions in the \pkg{RcppGSL} package. The \code{fastLm} function provides a more standard implementation of a linear model fit, offering both a default and a formula interface as well as \code{print}, \code{summary} and \code{predict} methods. Lastly, one must be be careful in timing comparisons of \code{\link{lm}} and friends versus this approach based on \code{GSL} or \code{Armadillo}. The reason that \code{GSL} or \code{Armadillo} can do something like \code{\link{lm.fit}} faster than the functions in the stats package is because they use the Lapack version of the QR decomposition while the stats package uses a \emph{modified} Linpack version. Hence \code{GSL} and \code{Armadillo} uses level-3 BLAS code whereas the stats package uses level-1 BLAS. However, \code{GSL} or \code{Armadillo} will choke on rank-deficient model matrices whereas the functions from the stats package will handle them properly due to the modified Linpack code. Statisticians want a pivoting scheme of \dQuote{pivot only on (apparent) rank deficiency} and numerical analysts have no idea why statisticians want this so it is not part of conventional linear algebra software. } \value{ \code{fastLmPure} returns a list with three components: \item{coefficients}{a vector of coefficients} \item{stderr}{a vector of the (estimated) standard errors of the coefficient estimates} \item{df}{a scalar denoting the degrees of freedom in the model} \code{fastLm} returns a richer object which also includes the residuals and call similar to the \code{\link{lm}} or \code{\link[MASS]{rlm}} functions.. } \seealso{\code{\link{lm}}, \code{\link{lm.fit}}} \references{GNU GSL project: \url{http://www.gnu.org/software/gsl}} \author{ The GNU GSL library is being written by team of authors with the overall development, design and implementation lead by Brian Gough and Gerard Jungman. RcppGSL is written by Romain Francois and Dirk Eddelbuettel. } \examples{ data(trees, package="datasets") ## bare-bones direct interface flm <- fastLmPure( cbind(1, log(trees$Girth)), log(trees$Volume) ) print(flm) ## standard R interface for formula or data returning object of class fastLm flmmod <- fastLm( log(Volume) ~ log(Girth), data=trees) summary(flmmod) } \keyword{regression} RcppGSL/cleanup0000755000176200001440000000107313161463457013114 0ustar liggesusers#!/bin/sh rm -f config.log config.status confdefs.h \ src/*.o src/*.so src/Makevars src/symbols.rds \ inst/doc/*.blg inst/doc/*.bbl \ */*~ *~ rm -rf autom4te.cache inst/doc/*/auto (cd inst/examples/RcppGSLExample; \ rm -f config.log config.status \ src/*.o src/*.so src/Makevars \ */*~ *~ ; \ rm -rf autom4te.cache) (cd vignettes && \ rm -rf RcppGSL*.log RcppGSL*.aux RcppGSL*.out RcppGSL*.tex RcppGSL*.pdf *.blg *.bbl \ *.xwm jss.bst pinp.cls \ auto unitTests-results/ jss/auto/ )