Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods.

Analyse single case analyses against a control group.
    Its purpose is to provide a flexible, with good power and
    low first type error
    approach that can manage at the same time controls' and patient's data.
    The use of Bayesian statistics allows to test both the alternative and
    null hypothesis.
    Scandola, M., & Romano, D. (2020, August 3). <doi:10.31234/osf.io/sajdq>
    Scandola, M., & Romano, D. (2021). <doi:10.1016/j.neuropsychologia.2021.107834>.

Fits, validates and compares a number of Bayesian models for
    spatial and space time point referenced and areal unit data. Model fitting
    is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer',
    'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using
    the DIC and WAIC,  and K-fold cross-validation where the user is free
    to select their own subset of data rows for validation. Sahu (2022)
    <doi:10.1201/9780429318443> describes the methods in detail.

Density, distribution, quantile function, random number generation for the BMT (Bezier-Montenegro-Torres) distribution. Torres-Jimenez C.J. and Montenegro-Diaz A.M. (2017) <doi:10.48550/arXiv.1709.05534>. Moments, descriptive measures and parameter conversion for different parameterizations of the BMT distribution. Fit of the BMT distribution to non-censored data by maximum likelihood, moment matching, quantile matching, maximum goodness-of-fit, also known as minimum distance, maximum product of spacing, also called maximum spacing, and minimum quantile distance, which can also be called maximum quantile goodness-of-fit. Fit of univariate distributions for non-censored data using maximum product of spacing estimation and minimum quantile distance estimation is also included.