Bayesian Network Learning with the PCHC and Related Algorithms R package pchc version 1.4 CRAN Task View: GraphicalModels (https://CRAN.R-project.org/view=GraphicalModels) Bayesian network learning using the PCHC, FEDHC, MMHC and variants of these algorithms. PCHC stands for PC Hill-Climbing, a new hybrid algorithm that uses PC to construct the skeleton of the BN and then applies the Hill-Climbing greedy search. More algorithms and variants have been added, such as MMHC, FEDHC, and the Tabu search variants, PCTABU, MMTABU and FEDTABU. The relevant papers are: a) Tsagris M. (2021). "A new scalable Bayesian network learning algorithm with applications to economics". Computational Economics, 57(1): 341-367. <doi:10.1007/s10614-020-10065-7>. b) Tsagris M. (2022). "The FEDHC Bayesian Network Learning Algorithm". Mathematics 2022, 10(15): 2604. <doi:10.3390/math10152604>. c) Sevinc V. and Tsagris M. (2024). "On the Hyperparameters of PCTABU and PCHC Bayesian Network Learning Algorithms". <doi:10.21203/rs.3.rs-5137132/v1>. Software Depends: R (>= 4.0) Imports: bigstatsr, bnlearn, dcov, foreach, doParallel, parallel, rangen, Rfast, Rfast2, robustbase, stats Suggests: bigreadr, Rgraphviz Michail Tsagris <mtsagris@uoc.gr> Comprehensive R Archive Network (CRAN) Michail Tsagris [aut, cre] GPL (>= 2) 2026-03-25 application/tgz https://CRAN.R-project.org/package=pchc doi:10.32614/CRAN.package.pchc