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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "funHMM" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'funHMM: Hidden Markov Models for Functional Data'
version: 0.1.0
abstract: 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>.
authors:
- family-names: Kashlak
  given-names: Adam B
  email: kashlak@ualberta.ca
preferred-citation:
  type: article
  title: Topological Hidden Markov Models
  authors:
  - family-names: Kashlak
    given-names: Adam B
    email: kashlak@ualberta.ca
  - family-names: Loliencar
    given-names: Prachi
  - family-names: Heo
    given-names: Giseon
  journal: Journal of Machine Learning Research
  year: '2023'
  volume: '24'
  issue: '340'
  url: https://jmlr.org/papers/v24/22-0685.html
  start: '1'
  end: '49'
repository: https://cran.r-universe.dev
commit: d0388a300ae8a9197bc177d2c03e94e51d2632a6
date-released: '2026-09-24'
contact:
- family-names: Kashlak
  given-names: Adam B
  email: kashlak@ualberta.ca