pchc: Bayesian Network Learning with the PCHC and Related Algorithms
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:
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