Title: | 'Rcpp' Integration for 'GNU GSL' Vectors and Matrices |
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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. |
Authors: | Dirk Eddelbuettel [aut, cre] , Romain Francois [aut] |
Maintainer: | Dirk Eddelbuettel <[email protected]> |
License: | GPL (>= 2) |
Version: | 0.3.13.1 |
Built: | 2024-10-18 14:16:53 UTC |
Source: | https://github.com/eddelbuettel/rcppgsl |
'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.
Dirk Eddelbuettel <[email protected]>
Dirk Eddelbuettel [aut, cre] (<https://orcid.org/0000-0001-6419-907X>), Romain Francois [aut] (<https://orcid.org/0000-0002-2444-4226>)
GSL: GNU Scientific Library: http://www.gnu.org/software/gsl/
fastLm
estimates the linear model using the gsl_multifit_linear
function of the GNU GSL
library.
fastLmPure(X, y) fastLm(X, ...) ## Default S3 method: fastLm(X, y, ...) ## S3 method for class 'formula' fastLm(formula, data = list(), ...)
fastLmPure(X, y) fastLm(X, ...) ## Default S3 method: fastLm(X, y, ...) ## S3 method for class 'formula' fastLm(formula, data = list(), ...)
y |
a vector containing the explained variable. |
X |
a model matrix. |
formula |
a symbolic description of the model to be fit. |
data |
an optional data frame containing the variables in the model. |
... |
not used |
Linear models should be estimated using the lm
function. In
some cases, lm.fit
may be appropriate.
The fastLmPure
function provides a reference use case of the GSL
library via the wrapper functions in the RcppGSL package.
The fastLm
function provides a more standard implementation of
a linear model fit, offering both a default and a formula interface as
well as print
, summary
and predict
methods.
Lastly, one must be be careful in timing comparisons of
lm
and friends versus this approach based on GSL
or Armadillo
. The reason that GSL
or Armadillo
can
do something like 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 modified
Linpack version. Hence GSL
and Armadillo
uses level-3 BLAS code
whereas the stats package uses level-1 BLAS. However,
GSL
or 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
“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.
fastLmPure
returns a list with three components:
coefficients |
a vector of coefficients |
stderr |
a vector of the (estimated) standard errors of the coefficient estimates |
df |
a scalar denoting the degrees of freedom in the model |
fastLm
returns a richer object which also includes the
residuals and call similar to the lm
or
rlm
functions..
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.
GNU GSL project: https://www.gnu.org/software/gsl/
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)
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)
LdFlags
and 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 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.
LdFlags
and CFlags
are commonly called from the files
Makevars
(or Makevars.win
) rather than in an interactive
session.
LdFlags(print=TRUE) CFlags(print=TRUE)
LdFlags(print=TRUE) CFlags(print=TRUE)
print |
A boolean determining whether the requested value is returned on the standard output, or silenly as a value. |
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 pkg-config
program is queried. On
Windows, environment variables used for GNU GSL builds with R are used.
A character vector suitable by use by the system compiler linker in order to compile and/or link against the GNU GSK.
Dirk Eddelbuettel and Romain Francois
Dirk Eddelbuettel and Romain Francois (2011). Rcpp: Seamless R
and C++ Integration. Journal of Statistical Software,
40(8), 1-18. URL http://www.jstatsoft.org/v40/i08/ and
available as vignette("Rcpp-introduction")
.
The document of the pkg-config
system tool.