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.

State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.

Functions to implement a Hwang(2021) <doi:10.2139/ssrn.3866876> estimator, which bounds an omitted variable bias using auxiliary data.

Bayesian network structure learning, parameter learning and inference.
  This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, MMPC,
  Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing,
  Tabu Search, DirectLiNGAM) and hybrid (MMHC, RSMAX2, H2PC) structure learning
  algorithms for discrete, Gaussian, conditional Gaussian and zero-inflated
  networks, along with many score functions and conditional independence tests.
  The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also
  implemented. Some utility functions (model comparison and manipulation,
  random data generation, arc orientation testing, simple and advanced plots)
  are included, as well as support for parameter estimation (maximum likelihood
  and Bayesian) and inference, conditional probability queries, interventions,
  counterfactuals, cross-validation, bootstrap and model averaging. Development
  snapshots with the latest bugfixes are available from
  <https://www.bnlearn.com/>.