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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 |
| Reference manual: | CondMVT.pdf |
| Package source: | CondMVT_0.1.0.tar.gz |
| Windows binaries: | r-devel: CondMVT_0.1.0.zip, r-release: CondMVT_0.1.0.zip, r-oldrel: CondMVT_0.1.0.zip |
| macOS binaries: | r-release (arm64): CondMVT_0.1.0.tgz, r-oldrel (arm64): CondMVT_0.1.0.tgz, r-release (x86_64): CondMVT_0.1.0.tgz, r-oldrel (x86_64): CondMVT_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.