Title: | 'Rcpp' Integration for 'GNU GSL' Vectors and Matrices |
---|---|

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 and Romain Francois |

Maintainer: | Dirk Eddelbuettel <[email protected]> |

License: | GPL (>= 2) |

Version: | 0.3.13.1 |

Built: | 2024-07-06 04:44:11 UTC |

Source: | https://github.com/eddelbuettel/rcppgsl |

- 'Rcpp' Integration for 'GNU GSL' Vectors and Matrices
- Bare-bones linear model fitting function
- Provide RcppGSL Compiler and Linker Flags

'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 and Romain Francois

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.