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 several procedures), 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.

Getting started

Package details

AuthorFelipe Osorio [aut, cre] (<https://orcid.org/0000-0002-4675-5201>), Alonso Ogueda [aut]
MaintainerFelipe Osorio <felipe.osorios@usm.cl>
LicenseGPL-3
Version0.4-1
URL https://faosorios.github.io/fastmatrix/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("fastmatrix")

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fastmatrix documentation built on April 21, 2022, 5:16 p.m.