Contains many functions useful for data
	analysis, high-level graphics, utility operations, functions for
	computing sample size and power, simulation, importing and annotating datasets,
	imputing missing values, advanced table making, variable clustering,
	character string manipulation, conversion of R objects to LaTeX and html code,
	recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.

Easy to use library to setup, apply and make inference with discrete time and discrete space Hidden Markov Models.

Fits hidden Markov models with discrete non-parametric 
	observation distributions to data sets.  The observations may
	be univariate or bivariate. Simulates data from such models.
	Finds most probable underlying hidden states, the most
	probable sequences of such states, and the log likelihood
	of a collection of observations given the parameters of
	the model.  Auxiliary predictors are accommodated in the
        univariate setting.

Consider the linear mixed model with normal random effects. A typical method to solve Henderson's Mixed Model Equations (HMME) is recursive estimation of the fixed effects and random effects. We provide a fast, stable, and scalable solver to the HMME without computing matrix inverse. See Kim (2017) <arXiv:1710.09663> for more details.

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.