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MMLR  

Fitting Markov-Modulated Linear Regression Models
View on CRAN: Click here


Download and install MMLR package within the R console
Install from CRAN:
install.packages("MMLR")

Install from Github:
library("remotes")
install_github("cran/MMLR")

Install by package version:
library("remotes")
install_version("MMLR", "0.2.0")



Attach the package and use:
library("MMLR")
Maintained by
Nadezda Spiridovska
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-02-03
Latest Update: 2020-01-09
Description:
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) 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.
How to cite:
Nadezda Spiridovska (2019). MMLR: Fitting Markov-Modulated Linear Regression Models. R package version 0.2.0, https://cran.r-project.org/web/packages/MMLR. Accessed 06 Jun. 2026.
Previous versions and publish date:
0.1.0 (2019-02-03 17:33)
Other packages that cited MMLR R package
View MMLR citation profile
Other R packages that MMLR depends, imports, suggests or enhances
Complete documentation for MMLR
Functions, R codes and Examples using the MMLR R package
Some associated functions: Aver_soj_time . B_est . VarY . Xreg . Ysimulation . randomizeInitState . randomizeTau . randomizeX . 
Some associated R codes: MMLR-package.R . MMLR.R .  Full MMLR package functions and examples
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