CondMVT: Conditional Multivariate t Distribution, Expectation Maximization Algorithm, and Its Stochastic Variants

Computes conditional multivariate t probabilities, random deviates, and densities. It can also be used to create missing values at random in a dataset, resulting in a missing at random (MAR) mechanism. Inbuilt in the package are the Expectation-Maximization (EM), Monte Carlo EM, and Stochastic EM algorithms for imputation of missing values in datasets assuming the multivariate t distribution. See Kinyanjui, Tamba, Orawo, and Okenye (2020)<doi:10.3233/mas-200493>, and Kinyanjui, Tamba, and Okenye(2021)<http://www.ceser.in/ceserp/index.php/ijamas/article/view/6726/0> for more details.

Version:	0.1.0
Imports:	stats, mvtnorm
Published:	2022-06-28
Author:	Paul Kinyanjui [aut, cre], Cox Tamba [aut], Justin Okenye [aut], Luke Orawo [ctb]
Maintainer:	Paul Kinyanjui <kinyanjui.access at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
Materials:	README
CRAN checks:	CondMVT results

Documentation:

Reference manual:

CondMVT.pdf

Downloads:

Package source:	CondMVT_0.1.0.tar.gz
Windows binaries:	r-prerel: CondMVT_0.1.0.zip, r-release: CondMVT_0.1.0.zip, r-oldrel: CondMVT_0.1.0.zip
macOS binaries:	r-prerel (arm64): CondMVT_0.1.0.tgz, r-release (arm64): CondMVT_0.1.0.tgz, r-oldrel (arm64): CondMVT_0.1.0.tgz, r-prerel (x86_64): CondMVT_0.1.0.tgz, r-release (x86_64): CondMVT_0.1.0.tgz

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