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. The relevant papers are a) Tsagris M. (2021). "A new scalable Bayesian network learning algorithm with applications to economics". Computational Economics (To appear). <doi:10.1007/s10614-020-10065-7>. b) Tsagris M. (2020). The FEDHC Bayesian network learning algorithm. <arXiv:2012.00113>.
| Version: | 0.4 |
| Depends: | R (≥ 3.6.0) |
| Imports: | bigmemory, bigstatsr, bnlearn, Rfast, Rfast2, robustbase, stats |
| Published: | 2021-02-22 |
| 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_0.4.tar.gz |
| Windows binaries: | r-devel: pchc_0.4.zip, r-release: pchc_0.4.zip, r-oldrel: pchc_0.4.zip |
| macOS binaries: | r-release: pchc_0.4.tgz, r-oldrel: pchc_0.3.tgz |
| Old sources: | pchc archive |
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