[aut, cre],
Larissa A. Matos
[aut],
Victor H. Lachos
[aut]It fits scale mixture of skew-normal linear mixed models using an expectation–maximization (EM) type algorithm, including some possibilities for modeling the within-subject dependence. Details can be found in Schumacher, Lachos and Matos (2021) <doi:10.1002/sim.8870>.
| Version: | 0.2.3 |
| Depends: | R (≥ 3.4.0) |
| Imports: | dplyr, ggplot2, moments, mvtnorm, nlme, numDeriv, purrr |
| Published: | 2021-02-03 |
| Author: | Fernanda L. Schumacher
[aut, cre],
Larissa A. Matos
[aut],
Victor H. Lachos
[aut] |
| Maintainer: | Fernanda L. Schumacher <fernandalschumacher at gmail.com> |
| BugReports: | https://github.com/fernandalschumacher/skewlmm/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/fernandalschumacher/skewlmm |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| In views: | Robust |
| CRAN checks: | skewlmm results |
| Reference manual: | skewlmm.pdf |
| Package source: | skewlmm_0.2.3.tar.gz |
| Windows binaries: | r-devel: skewlmm_0.2.3.zip, r-release: skewlmm_0.2.3.zip, r-oldrel: skewlmm_0.2.3.zip |
| macOS binaries: | r-release: skewlmm_0.2.3.tgz, r-oldrel: skewlmm_0.2.3.tgz |
| Old sources: | skewlmm archive |
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