Package: FourWayHMM 1.0.0
Salvatore D. Tomarchio
FourWayHMM: Parsimonious Hidden Markov Models for Four-Way Data
Implements parsimonious hidden Markov models for four-way data via expectation- conditional maximization algorithm, as described in Tomarchio et al. (2020) <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.
Authors:
FourWayHMM_1.0.0.tar.gz
FourWayHMM_1.0.0.tar.gz(r-4.5-noble)FourWayHMM_1.0.0.tar.gz(r-4.4-noble)
FourWayHMM_1.0.0.tgz(r-4.4-emscripten)FourWayHMM_1.0.0.tgz(r-4.3-emscripten)
FourWayHMM.pdf |FourWayHMM.html✨
FourWayHMM/json (API)
| # Install 'FourWayHMM' in R: |
| install.packages('FourWayHMM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- simX - Simulated Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:f180b5bbd9. Checks:OK: 1 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 12 2024 |
| R-4.5-linux | NOTE | Nov 12 2024 |
Dependencies:clicodetoolscpp11data.tabledoSNOWdplyrfansiforeachgenericsglueiteratorsLaplacesDemonlifecyclemagrittrmclustpillarpkgconfigpurrrR6rlangsnowstringistringrtensortibbletidyrtidyselectutf8vctrswithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fitting for parsimonious hidden Markov models for four-way data | HMM.fit |
| Initialization for the ECM algorithm | HMM.init |
| Simulated Data | simX |