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, Fast-IAMB, MMPC,
  Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu
  Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete,
  Gaussian and conditional Gaussian 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, cross-validation, bootstrap and model averaging.
  Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.

Network meta-analyses using Bayesian framework following Dias et al. (2013) <DOI:10.1177/0272989X12458724>. Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-level data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results.

An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.