Hidden Hybrid Markov/Semi-Markov Model Fitting
the M step function of the EM algorithm
predicting the response values for the regime switching model
weighted covariance for data with missing values
weighted covariance
pdf of the mixture of Gaussian linear (Markov-switching) models for hh...
pdf of the mixture of multivariate normals for hhsmm
pdf of the multinomial emission distribution for hhsmm
pdf of the mixture of B-splines for hhsmm
pdf of the Gaussian additive (Markov-switching) model for hhsmm
pdf of the mixture of the robust emission proposed by Qin et al. (2024...
convert to hhsmm data
hhsmm model fit
hhsmm specification
Computing maximum homogeneity of two state sequences
initial clustering of the data set
initial estimation of the model parameters for a specified emission di...
initialize the hhsmmspec model for a specified emission distribution
Create hhsmm data of lagged time series
left to right clustering
left to right linear regression clustering
make a hhsmmspec model for a specified emission distribution
the M step function of the EM algorithm
the M step function of the EM algorithm
the M step function of the EM algorithm
the M step function of the EM algorithm
the M step function of the EM algorithm
the M step function of the EM algorithm
prediction of state sequence for hhsmm
prediction of state sequence for hhsmm
Random data generation from the Gaussian additive (Markov-switching) m...
Random data generation from the mixture of Gaussian linear (Markov-swi...
Random data generation from the mixture of Gaussian linear (Markov-swi...
Random data generation from the mixture of multivariate normals for hh...
Random data generation from the multinomial emission distribution for ...
the M step function of the EM algorithm
the score of new observations
Simulation of data from hhsmm model
Splitting the data sets to train and test
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>).