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.

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.

Market area models are used to analyze and predict store choices and market areas concerning retail and service locations. This package implements two market area models (Huff Model, Multiplicative Competitive Interaction Model) into R, while the emphases lie on 1.) fitting these models based on empirical data via OLS regression and nonlinear techniques and 2.) data preparation and processing (esp. interaction matrices and data preparation for the MCI Model).

Apply the marginal classification method to achieve the purpose of providing 
    the point and interval estimates for the minimal clinically important difference 
    based on the classical anchor-based method. For more details of the methodology, please 
    see Zehua Zhou, Leslie J. Bisson and Jiwei Zhao (2021) <arXiv:2108.11589>.

Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.