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>.
Version: 1.3 Depends: R (≥ 4.0) Imports: bigstatsr, bnlearn, dcov, foreach, doParallel, parallel, Rfast, Rfast2, robustbase, stats Suggests: bigreadr, Rgraphviz Published: 2024-12-06 DOI: 10.32614/CRAN.package.pchc Author: Michail Tsagris [aut, cre] Maintainer: Michail Tsagris <mtsagris at uoc.gr> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] NeedsCompilation: no In views: GraphicalModels CRAN checks: pchc results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=pchc to link to this page.
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