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:
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