Carries out model-based clustering or classification using parsimonious Gaussian mixture models. McNicholas and Murphy (2008) <doi:10.1007/s11222-008-9056-0>, McNicholas (2010) <doi:10.1016/j.jspi.2009.11.006>, McNicholas and Murphy (2010) <doi:10.1093/bioinformatics/btq498>, McNicholas et al. (2010) <doi:10.1016/j.csda.2009.02.011>.

Evaluation of the pdf and the cdf of the univariate,
        noncentral, p-generalized normal distribution. Sampling from
        the univariate, noncentral, p-generalized normal distribution
        using either the p-generalized polar method, the p-generalized
        rejecting polar method, the Monty Python method, the Ziggurat
        method or the method of Nardon and Pianca. The package also
        includes routines for the simulation of the bivariate,
        p-generalized uniform distribution and the simulation of the
        corresponding angular distribution.

Computes pseudo-realizations from the posterior distribution of a Gaussian Process (GP) with the method described in Azzimonti et al. (2016) <doi:10.1137/141000749>. The realizations are obtained from simulations of the field at few well chosen points that minimize the expected distance in measure between the true excursion set of the field and the approximate one. Also implements a R interface for (the main function of) Distance Transform of sampled Functions (<https://cs.brown.edu/people/pfelzens/dt/index.html>).

Implements a general framework for creating dependency graphs using projection as introduced in Fan, Feng and Xia (2019)<arXiv:1501.01617>. Both lasso and sparse additive model projections are implemented. Both Pearson correlation and distance covariance options are available to generate the graph.

Provides functions to aid the identification of probable/possible
    duplicates in Plant Genetic Resources (PGR) collections using
    'passport databases' comprising of information records of each constituent
    sample. These include methods for cleaning the data, creation of a
    searchable Key Word in Context (KWIC) index of keywords associated with
    sample records and the identification of nearly identical records with
    similar information by fuzzy, phonetic and semantic matching of keywords.