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.

Version: 1.0.3
Depends: R (≥ 4.0.0)
Imports: MASS, Rcpp, progress, foreach
LinkingTo: Rcpp, RcppArmadillo
Suggests: parallel, doSNOW, rmarkdown, knitr, testthat (≥ 3.0.0), covr, printr, tseries, spelling
Published: 2022-07-07
Author: Lennart Oelschläger ORCID iD [aut, cre], Timo Adam ORCID iD [aut], Rouven Michels ORCID iD [aut]
Maintainer: Lennart Oelschläger <oelschlaeger.lennart at gmail.com>
BugReports: https://github.com/loelschlaeger/fHMM/issues
License: GPL-3
URL: https://loelschlaeger.de/fHMM/
NeedsCompilation: yes
Language: en-US
Materials: README NEWS
CRAN checks: fHMM results

Documentation:

Reference manual: fHMM.pdf
Vignettes: Introduction
Model definition
Controls
Data management
Model estimation
State decoding and prediction
Model checking
Model selection

Downloads:

Package source: fHMM_1.0.3.tar.gz
Windows binaries: r-devel: fHMM_1.0.3.zip, r-release: fHMM_1.0.3.zip, r-oldrel: fHMM_1.0.3.zip
macOS binaries: r-release (arm64): fHMM_1.0.3.tgz, r-oldrel (arm64): fHMM_1.0.3.tgz, r-release (x86_64): fHMM_1.0.3.tgz, r-oldrel (x86_64): fHMM_1.0.3.tgz
Old sources: fHMM archive

Linking:

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