Provides probability mass, distribution, quantile, random variate
    generation, and method-of-moments parameter fitting for the MBBEFD family of
    distributions used in insurance modeling as described in Bernegger (1997)
    <doi:10.2143/AST.27.1.563208> without any external dependencies.

Calibrate and apply multivariate bias correction algorithms
    for climate model simulations of multiple climate variables. Three methods
    described by Cannon (2016) <doi:10.1175/JCLI-D-15-0679.1> and 
    Cannon (2018) <doi:10.1007/s00382-017-3580-6> are implemented —
    (i) MBC Pearson correlation (MBCp), (ii) MBC rank correlation (MBCr),
    and (iii) MBC N-dimensional PDF transform (MBCn) — as is the Rank
    Resampling for Distributions and Dependences (R2D2) method.

The companion package provides all original data sets and functions that are used in the book "Model-Based Clustering and Classification for Data Science" by Charles Bouveyron, Gilles Celeux, T. Brendan Murphy and Adrian E. Raftery (2019, ISBN:9781108644181).

Bending non-positive-definite (symmetric) matrices to positive-definite, using weighted and unweighted methods.
   Jorjani, H., et al. (2003) <doi:10.3168/jds.S0022-0302(03)73646-7>.
   Schaeffer, L. R. (2014) <http://animalbiosciences.uoguelph.ca/~lrs/ELARES/PDforce.pdf>.

Implements methods that are useful in designing research studies and analyzing data, with 
	particular emphasis on methods that are developed for or used within the behavioral, 
	educational, and social sciences (broadly defined). That being said, many of the methods 
	implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a 
	suite of functions for a variety of related topics, such as effect sizes, confidence intervals 
	for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size
	planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and 
	minimum-risk point estimation perspectives), mediation analysis, various properties of 
	distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally 
	an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but MBESS became
	more general and now contains methods applicable and used in a wide variety of fields and is an 
	orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has 
	greatly benefited from others, see <https://www3.nd.edu/~kkelley/r-packages.html> for a detailed 
	list of those that have contributed and other details.