fastmatrix: Fast Computation of some Matrices Useful in Statistics

Small set of functions to fast computation of some matrices and operations useful in statistics and econometrics. Currently, there are functions for efficient computation of duplication, commutation and symmetrizer matrices with minimal storage requirements. Some commonly used matrix decompositions (LU and LDL), basic matrix operations (for instance, Hadamard, Kronecker products and the Sherman-Morrison formula) and iterative solvers for linear systems are also available. In addition, the package includes a number of common statistical procedures such as the sweep operator, weighted mean and covariance matrix using an online algorithm, linear regression (using Cholesky, QR, SVD, sweep operator and conjugate gradients methods), ridge regression (with optimal selection of the ridge parameter considering the GCV procedure), functions to compute the multivariate skewness, kurtosis, Mahalanobis distance (checking the positive defineteness) and the Wilson-Hilferty transformation of chi squared variables. Furthermore, the package provides interfaces to C code callable by another C code from other R packages.

Version: 0.3-819
Depends: R (≥ 3.5.0)
Published: 2021-05-08
Author: Felipe Osorio ORCID iD [aut, cre], Alonso Ogueda [aut]
Maintainer: Felipe Osorio <felipe.osorios at>
License: GPL-3
NeedsCompilation: yes
Citation: fastmatrix citation info
Materials: ChangeLog
CRAN checks: fastmatrix results


Reference manual: fastmatrix.pdf
Package source: fastmatrix_0.3-819.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: fastmatrix_0.3-819.tgz, r-oldrel: fastmatrix_0.3-819.tgz
Old sources: fastmatrix archive

Reverse dependencies:

Reverse depends: L1pack, SpatialPack
Reverse linking to: L1pack, SpatialPack


Please use the canonical form to link to this page.