A set of tools for fitting Markovmodulated linear regression, where responses Y(t) are timeadditive, 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. Markovswitching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markovmodulated linear regression model the external environment is described as a continuoustime 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 Markovmodulated linear regression (for academical/research purposes). Research project No. 1.1.1.2/VIAA/1/16/075.
Package details 


Author  Nadezda Spiridovska [aut, cre], Diana Santalova [ctb] 
Maintainer  Nadezda Spiridovska <[email protected]> 
License  GPL (>= 2) 
Version  0.2.0 
Package repository  View on CRAN 
Installation 
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