Hidden Markov Models for Functional Data R package funHMM version 0.1.0 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>. Software Depends: R (>= 3.5.0) Imports: stats, graphics, grDevices Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Adam B Kashlak <kashlak@ualberta.ca> Comprehensive R Archive Network (CRAN) Adam B Kashlak [aut, cre] GPL (>= 3) 2026-09-24 application/tgz https://CRAN.R-project.org/package=funHMM doi:10.32614/CRAN.package.funHMM