Asymmetric hidden Markov model (HMM-A) learning. HMM-As are similar to regular HMMs, but use Bayesian Networks (BNs) in their emission distribution. The HMM-As can therefore offer more problem insight [Bueno et al. 2017, <doi:10.1016/j.ijar.2017.05.011>].
| Version: | 1.1.0 |
| Depends: | R (≥ 3.6.0) |
| Imports: | bnlearn, mhsmm, MCMCpack, Rgraphviz, graph, methods |
| Suggests: | knitr, testthat, rmarkdown |
| Published: | 2020-06-26 |
| Author: | Marcos L.P Bueno [aut], Arjen Hommersom [aut, cre], Joop Thibaudier [aut], Marco Scutari [ctb], Robert Ness [ctb], Robert Gentleman and Ross Ihaka [cph], The R Development Core Team [cph], The R Foundation [cph] |
| Maintainer: | Arjen Hommersom <arjen.hommersom at ou.nl> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | hmma results |
| Reference manual: | hmma.pdf |
| Vignettes: |
Introduction to the hmma package |
| Package source: | hmma_1.1.0.tar.gz |
| Windows binaries: | r-devel: hmma_1.1.0.zip, r-release: hmma_1.1.0.zip, r-oldrel: hmma_1.1.0.zip |
| macOS binaries: | r-release: hmma_1.1.0.tgz, r-oldrel: hmma_1.1.0.tgz |
| Old sources: | hmma archive |
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