The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

MMLR: Fitting Markov-Modulated Linear Regression Models

A set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space. Model is proposed by Alexander Andronov (2012) <doi:10.48550/arXiv.1901.09600> and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition. Markov-switching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markov-modulated linear regression model the external environment is described as a continuous-time homogeneous irreducible Markov chain with known parameters while switching models consider Markov chain as unobserved and estimation procedure involves estimation of transition matrix. These models have significant differences in terms of the analytical approach. Also, package provides a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes). Research project No. 1.1.1.2/VIAA/1/16/075.

Version: 0.2.0
Imports: stats, pracma
Published: 2020-01-09
DOI: 10.32614/CRAN.package.MMLR
Author: Nadezda Spiridovska [aut, cre], Diana Santalova [ctb]
Maintainer: Nadezda Spiridovska <Spiridovska.N at tsi.lv>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: MMLR results

Documentation:

Reference manual: MMLR.pdf

Downloads:

Package source: MMLR_0.2.0.tar.gz
Windows binaries: r-devel: MMLR_0.2.0.zip, r-release: MMLR_0.2.0.zip, r-oldrel: MMLR_0.2.0.zip
macOS binaries: r-release (arm64): MMLR_0.2.0.tgz, r-oldrel (arm64): MMLR_0.2.0.tgz, r-release (x86_64): MMLR_0.2.0.tgz, r-oldrel (x86_64): MMLR_0.2.0.tgz
Old sources: MMLR archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=MMLR to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.