From a given data frame, this package learns its Bayesian network structure based on a selected score.

MCMC algorithms & processing functions for: 1. single response multiple regression, see Papageorgiou, G. (2018) <doi: 10.32614/RJ-2018-069>, 2. multivariate response multiple regression, with nonparametric models for the means, the variances and the correlation matrix, with variable selection, see Papageorgiou, G. and Marshall, B. C. (2020) <doi: 10.1080/10618600.2020.1739534>, 3. joint mean-covariance models for multivariate responses, see Papageorgiou, G. (2022) <doi: 10.1002/sim.9376>, and 4.Dirichlet process mixtures, see Papageorgiou, G. (2019) <doi: 10.1111/anzs.12273>.

Bayesian Network Structure Learning from Data with Missing Values.
    The package implements the Silander-Myllymaki complete search,
    the Max-Min Parents-and-Children, the Hill-Climbing, the
    Max-Min Hill-climbing heuristic searches, and the Structural
    Expectation-Maximization algorithm. Available scoring functions are
    BDeu, AIC, BIC. The package also implements methods for generating and using
    bootstrap samples, imputed data, inference.

Bayesian networks provide an intuitive framework for probabilistic reasoning 
             and its graphical nature can be interpreted quite clearly. Graph based methods 
             of machine learning are becoming more popular because they offer a richer model 
             of knowledge that can be understood by a human in a graphical format. The 'bnviewer' 
             is an R Package that allows the interactive visualization of Bayesian Networks. 
             The aim of this package is to improve the Bayesian Networks visualization over 
             the basic and static views offered by existing packages.

A menu-driven program and library of functions for carrying out
    convergence diagnostics and statistical and graphical analysis of Markov
    chain Monte Carlo sampling output.

Tools for constructing board/grid based games, as well as readily available game(s) for your entertainment.