https://github.com/faosorios/mvt
Estimation and testing for the multivariate t-distribution
https://github.com/faosorios/mvt
envelope equicorrelation-test homogeneity-test kurtosis multivariate-analysis multivariate-t student-t-distribution wilson-hilferty-transformation
Last synced: 6 days ago
JSON representation
Estimation and testing for the multivariate t-distribution
- Host: GitHub
- URL: https://github.com/faosorios/mvt
- Owner: faosorios
- Created: 2025-03-17T23:20:53.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2026-02-27T18:54:52.000Z (4 months ago)
- Last Synced: 2026-02-27T22:59:53.381Z (4 months ago)
- Topics: envelope, equicorrelation-test, homogeneity-test, kurtosis, multivariate-analysis, multivariate-t, student-t-distribution, wilson-hilferty-transformation
- Language: C
- Homepage: https://cran.r-project.org/package=MVT
- Size: 58.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: ChangeLog
Awesome Lists containing this project
README
# MVT: Estimation and testing for the multivariate t-distribution
[](https://cran.r-project.org/package=MVT)
[](https://cran.r-project.org/package=MVT)
`MVT` package contains a set of routines to perform estimation and inference under the multivariate t-distribution. These methods are a direct generalization of the multivariate inference under the gaussian assumption. In addition, these procedures provide robust methods useful against outliers.
## Resources
Latest binaries and sources can be found at the [CRAN package repository](https://cran.r-project.org/package=MVT):
* [MVT_0.3-81.tar.gz](https://cran.r-project.org/src/contrib/MVT_0.3-81.tar.gz) - Package sources
* [MVT_0.3-81.zip](https://cran.r-project.org/bin/windows/contrib/4.4/MVT_0.3-81.zip) - Windows binaries (R-release)
* [MVT_0.3-81.tgz](https://cran.r-project.org/bin/macosx/big-sur-arm64/contrib/4.4/MVT_0.3-81.tgz) - MacOS binaries (R-release, arm64)
* [MVT_0.3-81.tgz](https://cran.r-project.org/bin/macosx/big-sur-x86_64/contrib/4.4/MVT_0.3-81.tgz) - MacOS binaries (R-release, x86_64)
## Reference Manual
* [PDF manual](https://cran.r-project.org/web/packages/MVT/MVT.pdf) | [HTML manual](https://cran.r-project.org/web/packages/MVT/refman/MVT.html)
## Features
- Basic functionality for modeling using the multivariate t-distribution.
- Estimation of mean, covariance matrix and the shape (kurtosis) parameter using the EM algorithm.
- The core routines have been implemented in C and linked to R to ensure a reasonable computational speed.
- Performs hypothesis testing about the equicorrelation or homogeneity of variances structures for the covariance matrix, considering the test statistics of likelihood ratio, score, Wald or gradient.
- Multivariate random number generation for the multivariate t- (and gaussian) distribution.
- Graphical methods for assessing the assumption of multivariate t- (and gaussian) distribution.
## Installation instructions
Install `MVT` from CRAN using.
``` r
install.packages("MVT")
```
You can install the latest development version from github with:
``` r
# install.packages("devtools")
devtools::install_github("faosorios/MVT")
```
Alternatively, you can download the source as a tarball or as a zip file. Unpack the tarball or zipfile (thereby creating a directory named, MVT) and install the package source by executing (at the console prompt)
``` r
R CMD INSTALL MVT
```
Next, you can load the package by using the command `library(MVT)`
## Providing Feedback
Please report any bugs/suggestions/improvements to [Felipe Osorio](https://faosorios.github.io/). If you find these routines useful or not then please let me know. Also, acknowledgement of the use of the routines is appreciated.
### To cite package `MVT` in publications use:
``` r
citation("MVT")
To cite MVT package in publications use:
Osorio, F. (2024). Estimation and testing for the multivariate
t-distribution. R package version 0.3-81. URL:
http://mvt.mat.utfsm.cl
A BibTeX entry for LaTeX users is
@Manual{,
title = {Estimation and testing for the multivariate t-distribution},
author = {F. Osorio},
year = {2024},
note = {R package version 0.3-81},
url = {https://github.com/faosorios/MVT},
}
```
## Reference
Osorio, F., Galea, M., Henriquez, C., Arellano-Valle, R. (2023). Addressing non-normality in multivariate analysis using the t-distribution. [AStA Advances in Statistical Analysis](https://doi.org/10.1007/s10182-022-00468-2) 107, 785-813.
## Papers using MVT
- de Freitas, J.V.B, Bondon, P., Azevedo, C.L.N., Reisen, V.A., Nobre, J.S. (2024). Scale mixtures of multivariate centered skew-normal distributions. [Statistics and Computing](https://doi.org/10.1007/s11222-024-10512-7) 34, 212.
- Mignemi, G., Panzeri, A., Granziol, U., Bruno, G., Bertamini, M., Vidotto, G., Spoto, A. (2023). The mediating role of scientifical-medical satisfaction between COVID-19 conspiracy beliefs and vaccine confidence: A two-waves structural equation model. [Journal of Behavioral Medicine](https://doi.org/10.1007/s10865-022-00322-5) 46, 201-211
- Hintz, E., Hofert, M., Lemieux, C. (2022). Multivariate Normal Variance Mixtures in R: The R Package nvmix. [Journal of Statistical Software](https://doi.org/10.18637/jss.v102.i02) 102, 1-31.
- Punzo, A., Bagnato, L. (2020). Allometric analysis using the multivariate shifted exponential normal distribution. [Biometrical Journal](https://doi.org/10.1002/bimj.201900248) 62, 1525-1543.
## About the Author
Felipe Osorio is an applied statistician and creator of several R packages. Webpage: [faosorios.github.io](https://faosorios.github.io/)