hhsmm

Develops algorithms for fitting, prediction, simulation and initialization of the following models (1)- hidden hybrid Markov/semi-Markov model, introduced by Guedon (2005) doi:10.1016/j.csda.2004.05.033, (2)- nonparametric mixture of B-splines emissions (Langrock et al., 2015 doi:10.1111/biom.12282), (3)- regime switching regression model (Kim et al., 2008 doi:10.1016/j.jeconom.2007.10.002) and auto-regressive hidden hybrid Markov/semi-Markov model, (4)- spline-based nonparametric estimation of additive state-switching models (Langrock et al., 2018 doi:10.1111/stan.12133) (5)- robust emission model proposed by Qin et al, 2024 doi:10.1007/s10479-024-05989-4 (6)- several emission distributions, including mixture of multivariate normal (which can also handle missing data using EM algorithm) and multi-nomial emission (for modeling polymer or DNA sequences) (7)- tools for prediction of future state sequence, computing the score of a new sequence, splitting the samples and sequences to train and test sets, computing the information measures of the models, computing the residual useful lifetime (reliability) and many other useful tools ... (read for more description: Amini et al., 2022 doi:10.1007/s00180-022-01248-x and its arxiv version: doi:10.48550/arXiv.2109.12489).

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