Estimation procedures and goodness-of-fit test for several Markov regime switching models and mixtures of  bivariate copula models. The goodness-of-fit test is based on a Cramer-von Mises statistic and uses Rosenblatt's transform and parametric bootstrap to estimate the p-value. The proposed methodologies are described in Nasri, Remillard and Thioub (2020) <doi:10.1002/cjs.11534>.

Contains functions for hidden Markov models with observations having extra zeros as defined in the following two publications, Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) <doi:10.1111/rssc.12194>; Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) <doi:10.1029/2017JB015360>. The observed response variable is either univariate or bivariate Gaussian conditioning on presence of events, and extra zeros mean that the response variable takes on the value zero if nothing is happening. Hence the response is modelled as a mixture distribution of a Bernoulli variable and a continuous variable. That is, if the Bernoulli variable takes on the value 1, then the response variable is Gaussian, and if the Bernoulli variable takes on the value 0, then the response is zero too. This package includes functions for simulation, parameter estimation, goodness-of-fit, the Viterbi algorithm, and plotting the classified 2-D data. Some of the functions in the package are based on those of the R package 'HiddenMarkov' by David Harte. This updated version has included an example dataset and R code examples to show how to transform the data into the objects needed in the main functions. We have also made changes to increase the speed of some of the functions.

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>.