Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM.
    For a description of the algorithm see Brian S. Caffo, Wolfgang Jank and Galin L. Jones (2005)
	<DOI:10.1111/j.1467-9868.2005.00499.x>.

Fits the Multivariate Cluster Elastic Net (MCEN) presented in Price & Sherwood (2018) <arXiv:1707.03530>. The MCEN model simultaneously estimates regression coefficients and a clustering of the responses for a multivariate response model. Currently accommodates the Gaussian and binomial likelihood.

Machine coded genetic algorithm (MCGA) is a fast tool for
    real-valued optimization problems. It uses the byte
    representation of variables rather than real-values. It
    performs the classical crossover operations (uniform) on these
    byte representations. Mutation operator is also similar to
    classical mutation operator, which is to say, it changes a
    randomly selected byte value of a chromosome by +1 or -1 with
    probability 1/2. In MCGAs there is no need for
    encoding-decoding process and the classical operators are
    directly applicable on real-values. It is fast and can handle a
    wide range of a search space with high precision. Using a
    256-unary alphabet is the main disadvantage of this algorithm
    but a moderate size population is convenient for many problems.
    Package also includes multi_mcga function for multi objective
    optimization problems. This function sorts the chromosomes
    using their ranks calculated from the non-dominated sorting
    algorithm.

Simulating and estimating (regime-switching) Markov chain Gaussian 
    fields with covariance functions of the Gneiting class (Gneiting 2002) 
    <doi:10.1198/016214502760047113>. It supports parameter estimation by 
    weighted least squares and maximum likelihood methods, and produces Kriging 
    forecasts and intervals for existing and new locations.

Implementation of Warnes & Raftery's MCGibbsit run-length and 
  convergence diagnostic for a set of (not-necessarily independent) 
  Markov Chain Monte Carlo (MCMC) samplers.  It combines the quantile 
  estimate error-bounding approach of the Raftery and Lewis MCMC run
  length diagnostic `gibbsit` with the between verses within chain 
  approach of the Gelman and Rubin MCMC convergence diagnostic.