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>.
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