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), omnibus tests for univariate normality, functions to compute the multivariate skewness, kurtosis, the Mahalanobis distance (checking the positive defineteness), and the Wilson-Hilferty transformation of gamma variables. Furthermore, the package provides interfaces to C code callable by another C code from other R packages.
- Version0.5-7721
- R versionunknown
- LicenseGPL-3
- Needs compilation?Yes
- fastmatrix citation info
- Last release09/06/2024
Documentation
Team
Felipe Osorio
Alonso Ogueda
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Last 30 days
This package has been downloaded 1,961 times in the last 30 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 129 times.
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Last 365 days
This package has been downloaded 23,388 times in the last 365 days. The downloads are officially high enough to crash an underfunded departmental server. Quite an accomplishment! The day with the most downloads was Nov 08, 2024 with 210 downloads.
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Dependencies
- Reverse Depends4 packages
- Reverse Imports3 packages