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.

Fitting multivariate covariance generalized linear
     models (McGLMs) to data.  McGLM is a general framework for non-normal
     multivariate data analysis, designed to handle multivariate response
     variables, along with a wide range of temporal and spatial correlation
     structures defined in terms of a covariance link function combined
     with a matrix linear predictor involving known matrices.
     The models take non-normality into account in the conventional way
     by means of a variance function, and the mean structure is modelled
     by means of a link function and a linear predictor.
     The models are fitted using an efficient Newton scoring algorithm
     based on quasi-likelihood and Pearson estimating functions, using
     only second-moment assumptions. This provides a unified approach to
     a wide variety of different types of response variables and covariance
     structures, including multivariate extensions of repeated measures,
     time series, longitudinal, spatial and spatio-temporal structures.
     The package offers a user-friendly interface for fitting McGLMs
     similar to the glm() R function. 
     See Bonat (2018) <doi:10.18637/jss.v084.i04>, for more information 
     and examples.

Performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 <DOI:10.1198/106186007X257025>) and a boundary proposed by Besag and Clifford, 1991 <DOI:10.1093/biomet/78.2.301>. Gives valid p-values and confidence intervals on p-values.