Package: fHMM 1.4.1
fHMM: Fitting Hidden Markov Models to Financial Data
Fitting (hierarchical) hidden Markov models to financial data via maximum likelihood estimation. See Oelschläger, L. and Adam, T. "Detecting Bearish and Bullish Markets in Financial Time Series Using Hierarchical Hidden Markov Models" (2021, Statistical Modelling) <doi:10.1177/1471082X211034048> for a reference on the method. A user guide is provided by the accompanying software paper "fHMM: Hidden Markov Models for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R. (2024, Journal of Statistical Software) <doi:10.18637/jss.v109.i09>.
Authors:
fHMM_1.4.1.tar.gz
fHMM_1.4.1.tar.gz(r-4.5-noble)fHMM_1.4.1.tar.gz(r-4.4-noble)
fHMM_1.4.1.tgz(r-4.4-emscripten)fHMM_1.4.1.tgz(r-4.3-emscripten)
fHMM.pdf |fHMM.html✨
fHMM/json (API)
NEWS
# Install 'fHMM' in R: |
install.packages('fHMM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/loelschlaeger/fhmm/issues
Pkgdown:https://loelschlaeger.de
- dax - Deutscher Aktienindex (DAX) index data
- dax_model_2n - DAX 2-state HMM with normal distributions
- dax_model_3t - DAX 3-state HMM with t-distributions
- dax_vw_model - DAX/VW hierarchical HMM with t-distributions
- sim_model_2gamma - Simulated 2-state HMM with gamma distributions
- spx - Standard & Poor’s 500 (S&P 500) index data
- unemp - Unemployment rate data USA
- unemp_spx_model_3_2 - Unemployment rate and S&P 500 hierarchical HMM
- vw - Volkswagen AG (VW) stock data
Last updated 3 months agofrom:6f628087a1. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 16 2024 |
R-4.5-linux-x86_64 | OK | Nov 16 2024 |
Exports:compare_modelscompute_residualsdecode_statesdownload_datafHMM_eventsfHMM_parametersfit_modelll_hmmnparpar2parConpar2parUnconparCon2parparCon2parUnconparUncon2parparUncon2parConprepare_datareorder_statesset_controlssimulate_hmmviterbi
Dependencies:askpassassertthatbackportsBBbenchmarkmebenchmarkmeDatabriocallrcheckmateclicliprcodetoolscolorspacecpp11crayoncredentialscurldescdiffobjdigestdoParalleldplyrevaluatefansifarverforeachfsgenericsGenOrdgertggfunggimageggplot2ggplotifyghgitcredsglueGPArotationgridGraphicsgtablehexbinhexStickerhmshttrhttr2iniisobanditeratorsjsonlitelabelinglatex2explatticelifecyclelubridatemagickmagrittrMASSMatrixmgcvmimemnormtmunsellmvtnormnleqslvnlmeoeliopensslpadrpillarpkgbuildpkgconfigpkgloadpracmapraiseprettyunitsprocessxprogresspspsychpurrrquadprogR6rappdirsRColorBrewerRcppRcppArmadillorlangrprojrootrstudioapiscalesshowtextshowtextdbSimMultiCorrDatastringistringrsyssysfontstestthattibbletidyselecttimechangetriangleusethisutf8vctrsVGAMviridisLitewaldowhiskerwithryamlyulab.utilszip
Controls
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Data management
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Introduction
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Model checking
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Model definition
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Model estimation
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Model selection
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State decoding and prediction
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on Nov 16 2024.Last update: 2024-09-17
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