Implements parsimonious hidden Markov models for four-way data via expectation- conditional maximization algorithm, as described in Tomarchio et al. (2020) <doi:10.48550/arXiv.2107.04330>. The matrix-variate normal distribution is used as emission distribution. For each hidden state, parsimony is reached via the eigen-decomposition of the covariance matrices of the emission distribution. This produces a family of 98 parsimonious hidden Markov models.
Version: 1.0.0 Depends: R (≥ 2.10) Imports: withr, snow, doSNOW, foreach, mclust, tensor, tidyr, data.table, LaplacesDemon Published: 2021-11-30 DOI: 10.32614/CRAN.package.FourWayHMM Author: Salvatore D. Tomarchio [aut, cre], Antonio Punzo [aut], Antonello Maruotti [aut] Maintainer: Salvatore D. Tomarchio <daniele.tomarchio at unict.it> License: GPL (≥ 3) NeedsCompilation: no CRAN checks: FourWayHMM results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=FourWayHMM to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4