Package: RcppHMM 1.2.2.1

Roberto A. Cardenas-Ovando

RcppHMM: Rcpp Hidden Markov Model

Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.

Authors:Roberto A. Cardenas-Ovando [aut, cre], Julieta Noguez [aut], Claudia Rangel-Escareno [aut]

RcppHMM_1.2.2.1.tar.gz
RcppHMM_1.2.2.1.tar.gz(r-4.7-arm64)RcppHMM_1.2.2.1.tar.gz(r-4.7-x86_64)RcppHMM_1.2.2.1.tar.gz(r-4.6-arm64)RcppHMM_1.2.2.1.tar.gz(r-4.6-x86_64)
RcppHMM_1.2.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
RcppHMM/json (API)
NEWS

# Install 'RcppHMM' in R:
install.packages('RcppHMM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

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

openblascpp

1.26 score 1 stars 18 scripts 244 downloads 12 exports 2 dependencies

Last updated from:92bd8e39de. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK146
linux-devel-x86_64OK129
source / vignettesOK191
linux-release-arm64OK150
linux-release-x86_64OK119
wasm-releaseOK126

Exports:evaluationforwardBackwardgenerateObservationsinitGHMMinitHMMinitPHMMlearnEMloglikelihoodsetNamessetParametersverifyModelviterbi

Dependencies:RcppRcppArmadillo