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>.
Version: 1.4.2 Depends: R (≥ 4.0.0) Imports: checkmate, cli, curl, foreach, graphics, grDevices, httr, jsonlite, MASS, oeli (≥ 0.3.0), padr, pracma, progress, Rcpp, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: covr, devtools, doSNOW, knitr, parallel, rmarkdown, testthat (≥ 3.0.0), tseries Published: 2025-03-24 DOI: 10.32614/CRAN.package.fHMM Author: Lennart Oelschläger [aut, cre], Timo Adam [aut], Rouven Michels [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 Citation: fHMM citation info Materials: README NEWS In views: Finance CRAN checks: fHMM results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=fHMM to link to this page.
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