A set of objects and functions for Bayes Linear emulation and history matching.
  Core functionality includes automated training of emulators to data, diagnostic functions
  to ensure suitability, and a variety of proposal methods for generating 'waves' of points.
  For details on the mathematical background, there are many papers available on the topic
  (see references attached to function help files or the below references); for details of 
  the functions in this package, consult the manual or help files.
  Iskauskas, A, et al. (2024) <doi:10.18637/jss.v109.i10>.
  Bower, R.G., Goldstein, M., and Vernon, I. (2010) <doi:10.1214/10-BA524>.
  Craig, P.S., Goldstein, M., Seheult, A.H., and Smith, J.A. (1997) <doi:10.1007/978-1-4612-2290-3_2>.

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.

An algorithm for time series analysis that leverages hidden Markov models, cluster analysis, and mixture distributions to segment data, detect patterns and predict future sequences.

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.