Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

Get Started for Free

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>.

Functions (31)

auc

ROC and AUC

Correlation matrix for FBM class matrices (big matrices)

Read big data or a big.matrix object

bn.skel.utils

Utilities for the skeleton of a (Bayesian) Network

Adjacency matrix of a Bayesian network

Plot of a Bayesian network

Chi-square and G-square tests of (unconditional) indepdence

conf.edge.lower

Lower limit of the confidence of an edge

FBED variable selection method using the correlation

Correlation between a vector and a set of variables

Correlation significance testing using Fisher's z-transformation

The FEDHC and FEDTABU Bayesian network learning algorithms

The skeleton of a Bayesian network produced by the FEDHC algorithm

G-square test of conditional indepdence

g2test_univariate

All pairwise G-square and chi-square tests of indepedence

Check whether a directed graph is acyclic

Markov blanket of a node in a Bayesian network

mmhc

The MMHC and MMTABU Bayesian network learning algorithms

The skeleton of a Bayesian network learned with the MMHC algorithm

mmpc

Max-Min Parents and Children variable selection algorithm for continuous responses

Variable selection using the PC-simple algorithm

pchc

The PCHC and PCTABU Bayesian network learning algorithms

pchc-package

Bayesian Network Learning with the PCHC and Related Algorithms

The skeleton of a Bayesian network learned with the PC algorithm

pchc.skel.boot

Bootstrap versions of the skeleton of a Bayesian network

pcor

Partial correlation

Estimation of the percentage of null p-values

rbn

Random values simulation from a Bayesian network

rbn2

Continuous data simulation from a DAG.

rmcd

Outliers free data via the reweighted MCD

topological_sort

Topological sort of a Bayesian network

pchc

Bayesian Network Learning with the PCHC and Related Algorithms

v0.5

GPL (>= 2)

Authors

Michail Tsagris [aut, cre]

Initial release

2021-03-21

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.