Package: HMMpa 1.0.1
HMMpa: Analysing Accelerometer Data Using Hidden Markov Models
Analysing time-series accelerometer data to quantify length and intensity of physical activity using hidden Markov models. It also contains the traditional cut-off point method. Witowski V, Foraita R, Pitsiladis Y, Pigeot I, Wirsik N (2014)<doi:10.1371/journal.pone.0114089>.
Authors:
HMMpa_1.0.1.tar.gz
HMMpa_1.0.1.zip(r-4.5)HMMpa_1.0.1.zip(r-4.4)HMMpa_1.0.1.zip(r-4.3)
HMMpa_1.0.1.tgz(r-4.4-any)HMMpa_1.0.1.tgz(r-4.3-any)
HMMpa_1.0.1.tar.gz(r-4.5-noble)HMMpa_1.0.1.tar.gz(r-4.4-noble)
HMMpa_1.0.1.tgz(r-4.4-emscripten)HMMpa_1.0.1.tgz(r-4.3-emscripten)
HMMpa.pdf |HMMpa.html✨
HMMpa/json (API)
| # Install 'HMMpa' in R: |
| install.packages('HMMpa', repos = c('https://rforaita.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 years agofrom:d8aa5cfb92. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 13 2024 |
| R-4.5-win | OK | Nov 13 2024 |
| R-4.5-linux | OK | Nov 13 2024 |
| R-4.4-win | OK | Nov 13 2024 |
| R-4.4-mac | OK | Nov 13 2024 |
| R-4.3-win | OK | Nov 13 2024 |
| R-4.3-mac | OK | Nov 13 2024 |
Exports:AIC_HMMBaum_Welch_algorithmBIC_HMMcut_off_point_methoddgenpoisdirect_numerical_maximizationforward_backward_algorithmHMM_based_methodHMM_decodingHMM_simulationHMM_traininginitial_parameter_traininglocal_decoding_algorithmpgenpoisrgenpoisViterbi_algorithm
Dependencies:
