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.
Latest binaries and sources can be found at the CRAN package repository:
- MVT_0.3-81.tar.gz - Package sources
- MVT_0.3-81.zip - Windows binaries (R-release)
- MVT_0.3-81.tgz - MacOS binaries (R-release, arm64)
- MVT_0.3-81.tgz - MacOS binaries (R-release, x86_64)
Install MVT from CRAN using.
install.packages("MVT")You can install the latest development version from github with:
# 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 CMD INSTALL MVTNext, you can load the package by using the command library(MVT)
- 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.
To cite package MVT in publications use:
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},
}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 107, 785-813.
- 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 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 46, 201-211
- Hintz, E., Hofert, M., Lemieux, C. (2022). Multivariate Normal Variance Mixtures in R: The R Package nvmix. Journal of Statistical Software 102, 1-31.
- Punzo, A., Bagnato, L. (2020). Allometric analysis using the multivariate shifted exponential normal distribution. Biometrical Journal 62, 1525-1543.
Please report any bugs/suggestions/improvements to Felipe Osorio. If you find these routines useful or not then please let me know. Also, acknowledgement of the use of the routines is appreciated.
Felipe Osorio is an applied statistician and creator of several R packages. Webpage: faosorios.github.io