A constrained EM algorithm for univariate normal mixtures
Journal of Statistical Computation and Simulation
Nonstationary hidden Markov model
Signal Processing
Stylized facts of financial time series and hidden semi-Markov models
Computational Statistics & Data Analysis
Computational issues in parameter estimation for stationary hidden Markov models
Computational Statistics
Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
IEEE Transactions on Information Theory
Editorial: Second special issue on statistical algorithms and software
Computational Statistics & Data Analysis
Approximate forward-backward algorithm for a switching linear Gaussian model
Computational Statistics & Data Analysis
Hidden Markov models with arbitrary state dwell-time distributions
Computational Statistics & Data Analysis
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Hidden semi-Markov models are a generalization of the well-known hidden Markov model. They allow for a greater flexibility of sojourn time distributions, which implicitly follow a geometric distribution in the case of a hidden Markov chain. The aim of this paper is to describe hsmm, a new software package for the statistical computing environment R. This package allows for the simulation and maximum likelihood estimation of hidden semi-Markov models. The implemented Expectation Maximization algorithm assumes that the time spent in the last visited state is subject to right-censoring. It is therefore not subject to the common limitation that the last visited state terminates at the last observation. Additionally, hsmm permits the user to make inferences about the underlying state sequence via the Viterbi algorithm and smoothing probabilities.