pchc: Bayesian Network Learning with the PCHC and Related Algorithms

Bayesian network learning using the PCHC algorithm. 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: 1.2
Depends: R (≥ 4.0)
Imports: bigstatsr, bnlearn, dcov, foreach, doParallel, parallel, Rfast, Rfast2, robustbase, stats
Suggests: bigreadr, Rgraphviz
Published: 2023-09-06
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


Reference manual: pchc.pdf


Package source: pchc_1.2.tar.gz
Windows binaries: r-devel: pchc_1.2.zip, r-release: pchc_1.2.zip, r-oldrel: pchc_1.2.zip
macOS binaries: r-release (arm64): pchc_1.2.tgz, r-oldrel (arm64): pchc_1.2.tgz, r-release (x86_64): pchc_1.2.tgz, r-oldrel (x86_64): pchc_1.2.tgz
Old sources: pchc archive

Reverse dependencies:

Reverse imports: Compositional


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