Package: oHMMed 1.0.2

Michal Majka

oHMMed: HMMs with Ordered Hidden States and Emission Densities

Inference using a class of Hidden Markov models (HMMs) called 'oHMMed'(ordered HMM with emission densities <doi:10.1186/s12859-024-05751-4>): The 'oHMMed' algorithms identify the number of comparably homogeneous regions within observed sequences with autocorrelation patterns. These are modelled as discrete hidden states; the observed data points are then realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are then inferred. Relevant for application to genomic sequences, time series, or any other sequence data with serial autocorrelation.

Authors:Michal Majka [aut, cre], Lynette Caitlin Mikula [aut], Claus Vogl [aut]

oHMMed_1.0.2.tar.gz
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oHMMed_1.0.2.tgz(r-4.4-any)oHMMed_1.0.2.tgz(r-4.3-any)
oHMMed_1.0.2.tar.gz(r-4.5-noble)oHMMed_1.0.2.tar.gz(r-4.4-noble)
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oHMMed.pdf |oHMMed.html✨
oHMMed/json (API)

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

Peer review:

Bug tracker:https://github.com/lynettecaitlin/ohmmed/issues

Datasets:

On CRAN:

17 exports 2 stars 1.23 score 111 dependencies 4 scripts 830 downloads

Last updated 5 months agofrom:b9d80cef3f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 17 2024
R-4.5-winOKSep 17 2024
R-4.5-linuxOKSep 17 2024
R-4.4-winOKSep 17 2024
R-4.4-macOKSep 17 2024
R-4.3-winOKSep 17 2024
R-4.3-macOKSep 17 2024

Exports:coef.hmm_mcmc_gamma_poissoncoef.hmm_mcmc_normalconf_matconvert_to_ggmcmceigen_systemgenerate_random_Tget_pihmm_mcmc_gamma_poissonhmm_mcmc_normalhmm_simulate_gamma_poisson_datahmm_simulate_normal_datakullback_leibler_cont_apprkullback_leibler_discplot.hmm_mcmc_gamma_poissonplot.hmm_mcmc_normalposterior_prob_gamma_poissonposterior_prob_normal

Dependencies:backportsbayestestRbbmlebdsmatrixbitbit64bootbroombroom.helperscardscheckmateclassclicliprclockcodetoolscolorspacecpp11crayoncvmsdata.tabledatawizarddiagramdigestdplyrfansifarverforcatsfuturefuture.applygenericsGGallyggmcmcggplot2ggstatsglobalsgluegowergridExtragroupdata2gtablehardhathavenhmsinsightipredisobandKernSmoothlabelinglabelledlatticelavalifecyclelistenvlme4lmtestlubridatemagrittrMASSMatrixmgcvminqamistrMuMInmunsellmvtnormnlmenloptrnnetnumbersnumDerivparallellyparameterspatchworkpillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrpurrrR6RColorBrewerRcppRcppEigenreadrrearrrrecipesrlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vcdvctrsviridisLitevroomwithrzoo

Readme and manuals

Help Manual

Help pageTopics
oHMMed: HMMs with Ordered Hidden States and Emission DensitiesoHMMed-package
Extract Model Estimatescoef.hmm_mcmc_gamma_poisson coef.hmm_mcmc_normal
Calculate and Visualise a Confusion Matrixconf_mat
Converts MCMC Samples into 'ggmcmc' Formatconvert_to_ggmcmc
Calculate Eigenvalues and Eigenvectorseigen_system
Example of a Simulated Gamma-Poisson Modelexample_hmm_mcmc_gamma_poisson
Example of a Simulated Normal Modelexample_hmm_mcmc_normal
Generate a Random Transition Matrixgenerate_random_T
Get the Prior Probability of Statesget_pi
MCMC Sampler sampler for the Hidden Markov with Gamma-Poisson emission densitieshmm_mcmc_gamma_poisson
MCMC Sampler for the Hidden Markov Model with Normal emission densitieshmm_mcmc_normal
Simulate data distributed according to oHMMed with gamma-poisson emission densitieshmm_simulate_gamma_poisson_data
Simulate data distributed according to oHMMed with normal emission densitieshmm_simulate_normal_data
Calculate a Continuous Approximation of the Kullback-Leibler Divergencekullback_leibler_cont_appr
Calculate a Kullback-Leibler Divergence for a Discrete Distributionkullback_leibler_disc
Plot Diagnostics for 'hmm_mcmc_gamma_poisson' Objectsplot.hmm_mcmc_gamma_poisson
Plot Diagnostics for 'hmm_mcmc_normal' Objectsplot.hmm_mcmc_normal
Forward-Backward Algorithm to Calculate the Posterior Probabilities of Hidden States in Poisson-Gamma Modelposterior_prob_gamma_poisson
Forward-Backward Algorithm to Calculate the Posterior Probabilities of Hidden States in Normal Modelposterior_prob_normal