hdMTD: Inference for High-Dimensional Mixture Transition Distribution Models

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>.

Version:	0.1.0
Depends:	R (≥ 4.1.0)
Imports:	methods, dplyr, purrr
Suggests:	testthat (≥ 3.0.0)
Published:	2025-04-24
DOI:	10.32614/CRAN.package.hdMTD
Author:	Maiara Gripp [aut, cre], Guilherme Ost [ths], Giulio Iacobelli [ths]
Maintainer:	Maiara Gripp <maiara at dme.ufrj.br>
License:	MIT + file LICENSE
URL:	https://github.com/MaiaraGripp/hdMTD
NeedsCompilation:	no
Materials:	README
CRAN checks:	hdMTD results

Documentation:

Reference manual:

hdMTD.pdf

Downloads:

Package source:	hdMTD_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): hdMTD_0.1.0.tgz, r-oldrel (arm64): hdMTD_0.1.0.tgz, r-release (x86_64): hdMTD_0.1.0.tgz, r-oldrel (x86_64): hdMTD_0.1.0.tgz

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