funHMM: Hidden Markov Models for Functional Data

Fits hidden Markov models to time-ordered sequences of curves, such as sample paths of stochastic processes or smoothed functional observations, without projecting the curves onto a finite basis. The emission functions are Onsager-Machlup functionals of Gaussian measures on function spaces, which allows for Brownian motion with drift, fractional Brownian motion, Ornstein-Uhlenbeck processes and non-parametric state means under a choice of Cameron-Martin norm. The Baum-Welch and Viterbi algorithms are implemented in C. Methods are described in Kashlak, Loliencar and Heo (2023) <https://jmlr.org/papers/v24/22-0685.html>.

Version:	0.1.0
Depends:	R (≥ 3.5.0)
Imports:	stats, graphics, grDevices
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Published:	2026-09-24
DOI:	10.32614/CRAN.package.funHMM
Author:	Adam B Kashlak [aut, cre]
Maintainer:	Adam B Kashlak <kashlak at ualberta.ca>
License:	GPL (≥ 3)
NeedsCompilation:	yes
Citation:	funHMM citation info
Materials:	README, NEWS
CRAN checks:	funHMM results

Documentation:

Reference manual:	funHMM.html , funHMM.pdf
Vignettes:	Hidden Markov models for functional data with funHMM (source, R code)

Downloads:

Package source:	funHMM_0.1.0.tar.gz
Windows binaries:	r-devel: funHMM_0.1.0.zip, r-release: funHMM_0.1.0.zip, r-oldrel: funHMM_0.1.0.zip
macOS binaries:	r-release (arm64): funHMM_0.1.0.tgz, r-oldrel (arm64): funHMM_0.1.0.tgz, r-release (x86_64): funHMM_0.1.0.tgz, r-oldrel (x86_64): funHMM_0.1.0.tgz

Linking:

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