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# CITATION 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 "hdMTD" in publications use:'
type: software
license: GPL-3.0-only
title: 'hdMTD: Inference for High-Dimensional Mixture Transition Distribution Models'
version: 0.1.4
doi: 10.32614/CRAN.package.hdMTD
abstract: Estimates parameters in Mixture Transition Distribution (MTD) models, a
  class of high-order Markov chains. The set of relevant pasts (lags) is selected
  using either the Bayesian Information Criterion or the Forward Stepwise and Cut
  algorithms. Other model parameters (e.g. transition probabilities and oscillations)
  can be estimated via maximum likelihood estimation or the Expectation-Maximization
  algorithm. Additionally, 'hdMTD' includes a perfect sampling algorithm that generates
  samples of an MTD model from its invariant distribution. For theory, see Ost & Takahashi
  (2023) <http://jmlr.org/papers/v24/22-0266.html>.
authors:
- family-names: Gripp
  given-names: Maiara
  email: maiara@dme.ufrj.br
repository: https://maiaragripp.r-universe.dev
repository-code: https://github.com/MaiaraGripp/hdMTD
commit: e238820abb03fc16d8d7d539ecd0ce038d4b617e
url: https://github.com/MaiaraGripp/hdMTD
date-released: '2026-04-25'
contact:
- family-names: Gripp
  given-names: Maiara
  email: maiara@dme.ufrj.br