scutari.r-universe.dev

[scutari] bnlearn 5.2.1

scutari@bnlearn.com (Marco Scutari) — Fri, 17 Jul 2026 17:34:28 GMT

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 .

[scutari] fairml 0.9.1

scutari@bnlearn.com (Marco Scutari) — Mon, 09 Mar 2026 08:50:02 GMT

Fair machine learning regression models which take sensitive attributes into account in model estimation. Currently implementing Komiyama et al. (2018) , Zafar et al. (2019) and my own approach from Scutari, Panero and Proissl (2022) that uses ridge regression to enforce fairness.

[scutari] rbmn 0.9-6

scutari@bnlearn.com (Marco Scutari) — Fri, 30 Jun 2023 07:50:02 GMT

Creation, manipulation, simulation of linear Gaussian Bayesian networks from text files and more...