Package: fHMM 1.4.1
Lennart Oelschläger
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
- 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 1 months agofrom:6f628087a1. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 17 2024 |
| R-4.5-linux-x86_64 | OK | Oct 17 2024 |
Exports:compare_modelscompute_residualsdecode_statesdownload_datafHMM_eventsfHMM_parametersfit_modelll_hmmnparpar2parConpar2parUnconparCon2parparCon2parUnconparUncon2parparUncon2parConprepare_datareorder_statesset_controlssimulate_hmmviterbi
Dependencies:askpassassertthatbackportsBBbenchmarkmebenchmarkmeDatabriocallrcheckmateclicliprcodetoolscolorspacecpp11crayoncredentialscurldescdiffobjdigestdoParalleldplyrevaluatefansifarverforeachfsgenericsGenOrdgertggfunggimageggplot2ggplotifyghgitcredsglueGPArotationgridGraphicsgtablehexbinhexStickerhmshttrhttr2iniisobanditeratorsjsonlitelabelinglatex2explatticelifecyclelubridatemagickmagrittrMASSMatrixmgcvmimemnormtmunsellmvtnormnleqslvnlmeoeliopensslpadrpillarpkgbuildpkgconfigpkgloadpracmapraiseprettyunitsprocessxprogresspspsychpurrrquadprogR6rappdirsRColorBrewerRcppRcppArmadillorematch2rlangrprojrootrstudioapiscalesshowtextshowtextdbSimMultiCorrDatastringistringrsyssysfontstestthattibbletidyselecttimechangetriangleusethisutf8vctrsVGAMviridisLitewaldowhiskerwithryamlyulab.utilszip
Controls
Rendered fromv02_controls.Rmdusingknitr::rmarkdownon Oct 17 2024.Last update: 2024-09-17
Started: 2022-03-14
Data management
Rendered fromv03_data_management.Rmdusingknitr::rmarkdownon Oct 17 2024.Last update: 2024-09-17
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Introduction
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Model checking
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Model definition
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Model estimation
Rendered fromv04_model_estimation.Rmdusingknitr::rmarkdownon Oct 17 2024.Last update: 2024-09-17
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Model selection
Rendered fromv07_model_selection.Rmdusingknitr::rmarkdownon Oct 17 2024.Last update: 2024-09-17
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State decoding and prediction
Rendered fromv05_state_decoding_and_prediction.Rmdusingknitr::rmarkdownon Oct 17 2024.Last update: 2024-09-17
Started: 2022-03-14
