Package: MatrixHMM 1.0.0

Salvatore D. Tomarchio

MatrixHMM: Parsimonious Families of Hidden Markov Models for Matrix-Variate Longitudinal Data

Implements three families of parsimonious hidden Markov models (HMMs) for matrix-variate longitudinal data using the Expectation-Conditional Maximization (ECM) algorithm. The package supports matrix-variate normal, t, and contaminated normal distributions as emission distributions. For each hidden state, parsimony is achieved through the eigen-decomposition of the covariance matrices associated with the emission distribution. This approach results in a comprehensive set of 98 parsimonious HMMs for each type of emission distribution. Atypical matrix detection is also supported, utilizing the fitted (heavy-tailed) models.

Authors:Salvatore D. Tomarchio [aut, cre]

MatrixHMM_1.0.0.tar.gz
MatrixHMM_1.0.0.tar.gz(r-4.5-noble)MatrixHMM_1.0.0.tar.gz(r-4.4-noble)
MatrixHMM_1.0.0.tgz(r-4.4-emscripten)MatrixHMM_1.0.0.tgz(r-4.3-emscripten)
MatrixHMM.pdf |MatrixHMM.html✨
MatrixHMM/json (API)

# Install 'MatrixHMM' in R:
install.packages('MatrixHMM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • simData - A Simulated Dataset from a Matrix-Variate t Hidden Markov Model
  • simData2 - A Simulated Dataset with Atypical Matrices

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 222 downloads 6 exports 34 dependencies

Last updated 4 months agofrom:2625f5a46e. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 27 2024
R-4.5-linuxOKNov 27 2024

Exports:atp.MVCNatp.MVTEigen.HMM_fitEigen.HMM_initextract.bestMr.HMM

Dependencies:clicodetoolscpp11crayondata.tabledoSNOWdplyrfansiforeachgenericsgluehmsiteratorsLaplacesDemonlifecyclemagrittrmclustpillarpkgconfigprettyunitsprogresspurrrR6rlangsnowstringistringrtensortibbletidyrtidyselectutf8vctrswithr