The primary aim of 'MasterBayes' is to use Markov chain Monte Carlo (MCMC) techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data (Hadfield et al. (2006) <doi:10.1111/j.1365-294X.2006.03050.x>).  Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled 'C++' for efficiency.

Analyzes production and dispersal of seeds dispersed from trees and recovered in seed traps.  Motivated by long-term inventory plots where seed collections are used to infer seed production by each individual plant.

Simulates Multidimensional Adaptive Testing using the multidimensional three-parameter logistic model as described in Segall (1996) <doi:10.1007/BF02294343>, van der Linden (1999) <doi:10.3102/10769986024004398>, Reckase (2009) <doi:10.1007/978-0-387-89976-3>, and Mulder & van der Linden (2009) <doi:10.1007/s11336-008-9097-5>.

Calculates Model-Averaged Tail Area Wald
    (MATA-Wald) confidence intervals, and MATA-Wald confidence densities
    and distributions, which are constructed using single-model
    frequentist estimators and model weights. See Turek and Fletcher
    (2012) <doi:10.1016/j.csda.2012.03.002> and Fletcher et al (2019)
    <doi:10.1007/s10651-019-00432-5> for details.

Calculate multiple statistics with confidence intervals for matched
    case-control data including risk difference, risk ratio, relative
    difference, and the odds ratio. Results are equivalent to those from
    'Stata', and you can choose how to format your input data. Methods used are
    those described on page 56 the 'Stata' documentation for "Epitab - Tables
    for Epidemologists" <https://www.stata.com/manuals/repitab.pdf>.

Builds Gaussian-copula matched nulls: synthetic twins of a dataset
    that preserve every marginal distribution and the full correlation matrix
    while containing no cluster structure by construction. A reported number of
    clusters or "types" can then be tested against what the data's own margins
    and covariance already produce, using any clustering pipeline. Implements
    the matched-null procedure of Meng (2026) "Types Without Taxa"
    <https://osf.io/2ekcg>.