pchc: Bayesian Network Learning with the PCHC and Related Algorithms

Bayesian network learning using the PCHC algorithm. PCHC stands for PC Hill-Climbing. It is a new hybrid algorithm that used PC to construct the skeleton of the BN and then utilizes 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. (2020). The FEDHC Bayesian network learning algorithm. <arXiv:2012.00113>.

Version: 0.5
Depends: R (≥ 4.0)
Imports: bigmemory, bigstatsr, bnlearn, Rfast, Rfast2, robustbase, stats
Published: 2021-03-21
Author: Michail Tsagris [aut, cre]
Maintainer: Michail Tsagris <mtsagris at uoc.gr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: pchc results


Reference manual: pchc.pdf
Package source: pchc_0.5.tar.gz
Windows binaries: r-devel: pchc_0.5.zip, r-release: pchc_0.5.zip, r-oldrel: pchc_0.4.zip
macOS binaries: r-release: pchc_0.5.tgz, r-oldrel: pchc_0.4.tgz
Old sources: pchc archive

Reverse dependencies:

Reverse imports: Compositional


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