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>.

Functions for specifying and fitting marginal models for contingency tables proposed by Bergsma and Rudas (2002) <doi:10.1214/aos/1015362188> here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci, Colombi and Forcina (2007) <https://www.jstor.org/stable/24307737>; multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency tables proposed by Lang (2004) <doi:10.1214/aos/1079120140> and Lang (2005) <doi:10.1198/016214504000001042>. Inequality constraints on the parameters are allowed and can be tested.

Analysing time-series accelerometer data to quantify length and 
             intensity of physical activity using hidden Markov models. 
             It also contains the traditional cut-off point method.
             Witowski V, Foraita R, Pitsiladis Y, Pigeot I, Wirsik N (2014).
             <doi:10.1371/journal.pone.0114089>.