Fast and easy computation of Euclidean Minimum Spanning Trees (EMST) from data,
    relying on the R API for 'mlpack' - the C++ Machine Learning Library (Curtin et. al., 2013).
    'emstreeR' uses the Dual-Tree Boruvka (March, Ram, Gray, 2010, <doi:10.1145/1835804.1835882>), 
    which is theoretically and empirically the fastest algorithm for computing an EMST. This package also provides 
    functions and an S3 method for readily visualizing Minimum Spanning Trees (MST) using either the 
    style of the 'base', 'scatterplot3d', or 'ggplot2' libraries; and functions to export the MST output to shapefiles.

Goodness-of-fit tests for discrete multivariate data. It is
        tested if a given observation is likely to have occurred under
        the assumption of an ab-initio model. Monte Carlo methods are provided to
        make the package capable of solving high-dimensional problems.

Allows one to estimate the output of a computer program,
 as a function of the input parameters, without actually running it.
 The computer program is assumed to be a Gaussian process, whose
 parameters are estimated using Bayesian techniques that give a PDF of
 expected program output.  This PDF is conditional on a training set
 of runs, each consisting of a point in parameter space and the model
 output at that point.  The emphasis is on complex codes that take
 weeks or months to run, and that have a large number of undetermined
 input parameters; many climate prediction models fall into this
 class.  The emulator essentially determines Bayesian posterior
 estimates of the PDF of the output of a model, conditioned on results
 from previous runs and a user-specified prior linear model.  The
 package includes functionality to evaluate quadratic forms 
 efficiently.

Provide the EMU Speech Database Management System (EMU-SDMS) with
    database management, data extraction, data preparation and data
    visualization facilities. See <https://ips-lmu.github.io/The-EMU-SDMS-Manual/>
    for more details.