@article{JSSv036i07, title={depmixS4: An R Package for Hidden Markov Models}, volume={36}, url={https://www.jstatsoft.org/index.php/jss/article/view/v036i07}, doi={10.18637/jss.v036.i07}, abstract={<b>depmixS4</b> implements a general framework for defining and estimating dependent mixture models in the <b>R</b> programming language. This includes standard Markov models, latent/hidden Markov models, and latent class and finite mixture distribution models. The models can be fitted on mixed multivariate data with distributions from the <b>glm</b> family, the (logistic) multinomial, or the multivariate normal distribution. Other distributions can be added easily, and an example is provided with the <i>exgaus</i> distribution. Parameters are estimated by the expectation-maximization (EM) algorithm or, when (linear) constraints are imposed on the parameters, by direct numerical optimization with the <b>Rsolnp</b> or <b>Rdonlp2</b> routines.}, number={7}, journal={Journal of Statistical Software}, author={Visser, Ingmar and Speekenbrink, Maarten}, year={2010}, pages={1–21} }