Package: hdMTD
Type: Package
Title: Inference for High-Dimensional Mixture Transition Distribution
        Models
Version: 0.1.4
Authors@R: c(person(given = "Maiara", family = "Gripp", email = "maiara@dme.ufrj.br", role = c("aut", "cre")), person(given = "Guilherme", family = "Ost", email = "guilhermeost@im.ufrj.br", role = "ths"), person(given = "Giulio", family = "Iacobelli", email = "giulio@im.ufrj.br", role = "ths"))
Description: 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>.
URL: https://github.com/MaiaraGripp/hdMTD
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Roxygen: list(markdown = TRUE)
Depends: R (>= 4.1.0)
Imports: methods, dplyr, purrr, igraph
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
Config/pak/sysreqs: libglpk-dev libxml2-dev
Repository: https://maiaragripp.r-universe.dev
Date/Publication: 2026-04-25 20:02:42 UTC
RemoteUrl: https://github.com/maiaragripp/hdmtd
RemoteRef: HEAD
RemoteSha: e238820abb03fc16d8d7d539ecd0ce038d4b617e
NeedsCompilation: no
Packaged: 2026-10-31 10:01:46 UTC; root
Author: Maiara Gripp [aut, cre],
  Guilherme Ost [ths],
  Giulio Iacobelli [ths]
Maintainer: Maiara Gripp <maiara@dme.ufrj.br>