Package: pchc 1.4

Michail Tsagris

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>. c) Sevinc V. and Tsagris M. (2024). "On the Hyperparameters of PCTABU and PCHC Bayesian Network Learning Algorithms". <doi:10.21203/rs.3.rs-5137132/v1>.

Authors:Michail Tsagris [aut, cre]

pchc_1.4.tar.gz
pchc_1.4.tar.gz(r-4.7-any)pchc_1.4.tar.gz(r-4.6-any)
pchc_1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
pchc/json (API)

# Install 'pchc' in R:
install.packages('pchc', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 7 scripts 342 downloads 51 exports 54 dependencies

Last updated from:cadd7138be. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK147
source / vignettesOK194
linux-release-x86_64OK146
wasm-releaseOK136

Exports:aucbig_corbig_readbn.skel.utilsbn.skel.utils2bnmatbnplotcat.testschi2testchi2test_univariateconf.edge.lowercor.fbedcor2pcorcorpairscorrelscortestdcor.fedhc.skeldcor.mmhc.skelfedhcfedhc.bootfedhc.skelfedhc.skel.bootfedtabufedtabu.bootg2testg2test_permg2test_univariateg2test_univariate_permis.dagmbmmhcmmhc.bootmmhc.skelmmhc.skel.bootmmpcmmtabummtabu.bootpc.selpchcpchc.bootpchc.skelpchc.skel.bootpcorpctabupctabu.bootpi0estrbnrbn2rbn3rmcdtopological_sort

Dependencies:BHbigassertrbigparallelrbigstatsrbitbnlearnclicodetoolscowplotcpp11dcovDEoptimRdoParallelfarverffflockforeachggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMatrixparallellypillarpkgconfigpsR6rangenRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRfastRfast2RhpcBLASctlrlangrmioRnanoflannrobustbaseRSpectraS7scalestibbleutf8vctrsviridisLitewithrzigg

Readme and manuals

Help Manual

Help pageTopics
Bayesian Network Learning with the PCHC and Related Algorithmspchc-package
Adjacency matrix of a Bayesian networkbnmat
All pairwise G-square and chi-square tests of indepedencechi2test_univariate g2test_univariate g2test_univariate_perm
Bootstrap versions of the skeleton of a Bayesian networkfedhc.skel.boot mmhc.skel.boot pchc.skel.boot
Bootstrapping the FEDHC and FEDTABU Bayesian network learning algorithmsfedhc.boot fedtabu.boot
Bootstrapping the MMHC and MMTABU Bayesian network learning algorithmsmmhc.boot mmtabu.boot
Bootstrapping the PCHC and PCTABU Bayesian network learning algorithmspchc.boot pctabu.boot
Check whether a directed graph is acyclicis.dag
Chi-square and G-square tests of (unconditional) indepdencecat.tests
Continuous data simulation from a DAG.rbn2 rbn3
Correlation between pairs of variablescorpairs
Correlation matrix for FBM class matrices (big matrices)big_cor
Correlation significance testing using Fisher's z-transformationcortest
Correlation between a vector and a set of variablescorrels
Estimation of the percentage of null p-valuespi0est
G-square test of conditional indepdencechi2test g2test g2test_perm
Lower limit of the confidence of an edgeconf.edge.lower
Markov blanket of a node in a Bayesian networkmb
Outliers free data via the reweighted MCDrmcd
Partial correlationpcor
Partial correlation matrix from correlation or covariance matrixcor2pcor
Plot of a Bayesian networkbnplot
Random values simulation from a Bayesian networkrbn
Read big data or a big.matrix objectbig_read
ROC and AUCauc
The skeleton of a Bayesian network produced by the MMHC or the FEDHC algorithm using the distance correlationdcor.fedhc.skel dcor.mmhc.skel
The skeleton of a Bayesian network produced by the FEDHC algorithmfedhc.skel
The skeleton of a Bayesian network learned with the MMHC algorithmmmhc.skel
The skeleton of a Bayesian network learned with the PC algorithmpchc.skel
The FEDHC and FEDTABU Bayesian network learning algorithmsfedhc fedtabu
The MMHC and MMTABU Bayesian network learning algorithmsmmhc mmtabu
The PCHC and PCTABU Bayesian network learning algorithmspchc pctabu
Topological sort of a Bayesian networktopological_sort
Utilities for the skeleton of a (Bayesian) Networkbn.skel.utils bn.skel.utils2
Variable selection for continuous data using the FBED algorithmcor.fbed
Variable selection for continuous data using the MMPC algorithmmmpc
Variable selection for continuous data using the PC-simple algorithmpc.sel