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Hidden Markov Models are useful for modeling sequential data. This package provides several functions implemented in C++ for explaining the algorithms used for Hidden Markov Models (forward, backward, decoding, learning).
Version: | 2023.8.28 |
Imports: | Rcpp (≥ 1.0.7) |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | testthat, knitr, markdown, R.utils, covr, depmixS4, data.table, ggplot2, neuroblastoma, microbenchmark |
Published: | 2023-09-05 |
DOI: | 10.32614/CRAN.package.plotHMM |
Author: | Toby Hocking [aut, cre] |
Maintainer: | Toby Hocking <toby.hocking at r-project.org> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | yes |
Materials: | NEWS |
CRAN checks: | plotHMM results |
Reference manual: | plotHMM.pdf |
Vignettes: |
Comparison with depmixS4 Multiple sequences |
Package source: | plotHMM_2023.8.28.tar.gz |
Windows binaries: | r-devel: plotHMM_2023.8.28.zip, r-release: plotHMM_2023.8.28.zip, r-oldrel: plotHMM_2023.8.28.zip |
macOS binaries: | r-release (arm64): plotHMM_2023.8.28.tgz, r-oldrel (arm64): plotHMM_2023.8.28.tgz, r-release (x86_64): plotHMM_2023.8.28.tgz, r-oldrel (x86_64): plotHMM_2023.8.28.tgz |
Old sources: | plotHMM archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.