Package: hhsmm 0.2.5

hhsmm: Hidden Hybrid Markov/Semi-Markov Model Fitting

Develops algorithms for fitting, prediction, simulation and initialization of the hidden hybrid Markov/semi-Markov model, introduced by Guedon (2005) <doi:10.1016/j.csda.2004.05.033>, which also includes several tools for handling missing data, nonparametric mixture of B-splines emissions (Langrock et al., 2015 <doi:10.1111/biom.12282>), fitting regime switching regression (Kim et al., 2008 <doi:10.1016/j.jeconom.2007.10.002>) and auto-regressive hidden hybrid Markov/semi-Markov model, and many other useful tools (read for more description: <arxiv:2109.12489>).

Authors:Morteza Amini [aut, cre, cph], Afarin Bayat [aut], Reza Salehian [aut]

hhsmm_0.2.5.tar.gz
hhsmm_0.2.5.zip(r-4.7)hhsmm_0.2.5.zip(r-4.6)hhsmm_0.2.5.zip(r-4.5)
hhsmm_0.2.5.tgz(r-4.6-x86_64)hhsmm_0.2.5.tgz(r-4.6-arm64)hhsmm_0.2.5.tgz(r-4.5-x86_64)hhsmm_0.2.5.tgz(r-4.5-arm64)
hhsmm_0.2.5.tar.gz(r-4.7-arm64)hhsmm_0.2.5.tar.gz(r-4.7-x86_64)hhsmm_0.2.5.tar.gz(r-4.6-arm64)hhsmm_0.2.5.tar.gz(r-4.6-x86_64)
hhsmm_0.2.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
hhsmm/json (API)

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

Bug tracker:https://github.com/mortamini/hhsmm/issues

On CRAN:

Conda:

2.48 score 3 stars 1 scripts 262 downloads 26 exports 103 dependencies

Last updated from:9b1989404c. Checks:11 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR239
linux-devel-x86_64ERROR221
source / vignettesOK214
linux-release-arm64ERROR220
linux-release-x86_64ERROR228
macos-release-arm64ERROR195
macos-release-x86_64ERROR263
macos-oldrel-arm64ERROR193
macos-oldrel-x86_64ERROR289
windows-develERROR148
windows-releaseERROR131
windows-oldrelERROR173
wasm-releaseOK169

Exports:cov.miss.mix.wtcov.mix.wtdmixlmdmixmvnormdnonparhhsmmdatahhsmmfithhsmmspechomogeneityinitial_clusterinitial_estimateinitialize_modellagdataltr_clusltr_reg_clusmake_modelmiss_mixmvnorm_mstepmixdiagmvnorm_mstepmixlm_mstepmixmvnorm_mstepnonpar_msteprmixarrmixlmrmixmvnormscoretrain_test_split

Dependencies:backportsbase64encbitbit64bootbroombslibcachemclicliprCMAPSScodetoolscpp11cprcrayondigestdplyrevaluatefarverfastmapfontawesomeforcatsforeachfsgenericsggplot2glmnetglueGPArotationgtablehavenhighrhmshtmltoolshtmlwidgetsisobanditeratorsjomojquerylibjsonliteknitrlabelinglatticelifecyclelme4magrittrMASSMatrixmemoisemicemimeminqamisc3dmitmlmnormtmvtnormnlmenloptrnnetnumDerivordinalpanpillarpkgconfigplot3DprettyunitsprogresspsychpurrrR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrreformulasrglrlangrmarkdownrpartS7sassscalesshapestringistringrsurvivaltibbletidyrtidyselecttinytextzdbucminfutf8vctrsviridisLitevroomwithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
the M step function of the EM algorithmadditive_reg_mstep
predicting the response values for the regime switching modeladdreg_hhsmm_predict
weighted covariance for data with missing valuescov.miss.mix.wt
weighted covariancecov.mix.wt
pdf of the mixture of Gaussian linear (Markov-switching) models for hhsmmdmixlm
pdf of the mixture of multivariate normals for hhsmmdmixmvnorm
pdf of the mixture of B-splines for hhsmmdnonpar
pdf of the Gaussian additive (Markov-switching) model for hhsmmdnorm_additive_reg
convert to hhsmm datahhsmmdata
hhsmm model fithhsmmfit
hhsmm specificationhhsmmspec
Computing maximum homogeneity of two state sequenceshomogeneity
initial clustering of the data setinitial_cluster
initial estimation of the model parameters for a specified emission distributioninitial_estimate
initialize the hhsmmspec model for a specified emission distributioninitialize_model
Create hhsmm data of lagged time serieslagdata
left to right clusteringltr_clus
left to right linear regression clusteringltr_reg_clus
make a hhsmmspec model for a specified emission distributionmake_model
the M step function of the EM algorithmmiss_mixmvnorm_mstep
the M step function of the EM algorithmmixdiagmvnorm_mstep
the M step function of the EM algorithmmixlm_mstep
the M step function of the EM algorithmmixmvnorm_mstep
the M step function of the EM algorithmnonpar_mstep
prediction of state sequence for hhsmmpredict.hhsmm
prediction of state sequence for hhsmmpredict.hhsmmspec
Random data generation from the Gaussian additive (Markov-switching) model for hhsmm modelraddreg
Random data generation from the mixture of Gaussian linear (Markov-switching) autoregressive models for hhsmm modelrmixar
Random data generation from the mixture of Gaussian linear (Markov-switching) models for hhsmm modelrmixlm
Random data generation from the mixture of multivariate normals for hhsmm modelrmixmvnorm
the score of new observationsscore
Simulation of data from hhsmm modelsimulate.hhsmmspec
Splitting the data sets to train and testtrain_test_split