Package: MMLR 0.2.0

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) <arxiv:1901.09600v1> 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.

Authors:Nadezda Spiridovska [aut, cre], Diana Santalova [ctb]

MMLR_0.2.0.tar.gz
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MMLR.pdf |MMLR.html
MMLR/json (API)

# Install 'MMLR' in R:
install.packages('MMLR', repos = c('https://nadezdaspiridovska.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 3 scripts 116 downloads 1 mentions 8 exports 1 dependencies

Last updated 5 years agofrom:01956115b9. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-winNOTENov 02 2024
R-4.5-linuxNOTENov 02 2024
R-4.4-winNOTENov 02 2024
R-4.4-macNOTENov 02 2024
R-4.3-winOKNov 02 2024
R-4.3-macOKNov 02 2024

Exports:Aver_soj_timeB_estrandomizeInitStaterandomizeTaurandomizeXVarYXregYsimulation

Dependencies:pracma