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HMMHSMM  

Inference and Estimation of Hidden Markov Models and Hidden Semi-Markov Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("HMMHSMM", "0.1.0")



Attach the package and use:
library("HMMHSMM")
Maintained by
Ting Wang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-12-18
Latest Update: 2025-12-18
Description:
Provides flexible maximum likelihood estimation and inference for Hidden Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs), as well as the underlying systems in which they operate. The package supports a wide range of observation and dwell-time distributions, offering a flexible modelling framework suitable for diverse practical data. Efficient implementations of the forward-backward and Viterbi algorithms are provided via 'Rcpp' for enhanced computational performance. Additional functionality includes model simulation, residual analysis, non-initialised estimation, local and global decoding, calculation of diverse information criteria, computation of confidence intervals using parametric bootstrap methods, numerical covariance matrix estimation, and comprehensive visualisation functions for interpreting the data-generating processes inferred from the models. Methods follow standard approaches described by Guédon (2003) <doi:10.1198/1061860032030>, Zucchini and MacDonald (2009, ISBN:9781584885733), and O'Connell and Højsgaard (2011) <doi:10.18637/jss.v039.i04>.
How to cite:
Ting Wang (2025). HMMHSMM: Inference and Estimation of Hidden Markov Models and Hidden Semi-Markov Models. R package version 0.1.0, https://cran.r-project.org/web/packages/HMMHSMM. Accessed 20 Sep. 2026.
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Complete documentation for HMMHSMM
Functions, R codes and Examples using the HMMHSMM R package
Full HMMHSMM package functions and examples
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