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pchc (version 0.4)

Bayesian Network Learning with the PCHC and Related Algorithms

Description

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). . b) Tsagris M. (2020). The FEDHC Bayesian network learning algorithm. .

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Version

Install

install.packages('pchc')

Monthly Downloads

306

Version

0.4

License

GPL (>= 2)

Maintainer

Michail Tsagris

Last Published

February 22nd, 2021

Functions in pchc (0.4)

The MMHC Bayesian network learning algorithm

The MMHC Bayesian network learning algorithm
Markov blanket of a node in a Bayesian network

Markov blanket of a node in a Bayesian network
Variable selection using the PC-simple algorithm

Variable selection using the PC-simple algorithm
The PCHC Bayesian network learning algorithm

The PCHC Bayesian network learning algorithm
Skeleton of the FEDHC algorithm

The skeleton of a Bayesian network produced by the FEDHC algorithm
pchc-package

Bayesian Network Learning with the PCHC and Related Algorithms
G-square and Chi-square test of conditional indepdence

G-square test of conditional indepdence
Skeleton of the PC algorithm

The skeleton of a Bayesian network learned with the PC algorithm
Max-Min Parents and Children variable selection algorithm for continuous responses

Max-Min Parents and Children variable selection algorithm for continuous responses
Read big data or a big.matrix object

Read big data or a big.matrix object
Utilities for the skeleton of a (Bayesian) network

Utilities for the skeleton of a (Bayesian) Network
Adjacency matrix of a Bayesian network

Adjacency matrix of a Bayesian network
Correlation significance testing using Fisher's z-transformation

Correlation significance testing using Fisher's z-transformation
Continuous data simulation from a DAG

Continuous data simulation from a DAG.
Plot of a Bayesian network

Plot of a Bayesian network
The FEDHC Bayesian network learning algorithm

The FEDHC Bayesian network learning algorithm
Outliers free data via the reweighted MCD

Outliers free data via the reweighted MCD
Bootstrap versions of the skeleton of a Bayesian network

Bootstrap versions of the skeleton of a Bayesian network
Skeleton of the MMHC algorithm

The skeleton of a Bayesian network learned with the MMHC algorithm
Correlation matrix for FBM class matrices (big matrices)

Correlation matrix for FBM class matrices (big matrices)
All pairwise G-square and chi-square tests of indepedence

All pairwise G-square and chi-square tests of indepedence
Chi-square and G-square tests of (unconditional) indepdence

Chi-square and G-square tests of (unconditional) indepdence
Lower limit of the confidence of an edge

Lower limit of the confidence of an edge
Check whether a directed graph is acyclic

Check whether a directed graph is acyclic
Partial correlation between two continuous variables

Partial correlation
Random values simulation from a Bayesian network

Random values simulation from a Bayesian network
Estimation of the percentage of null p-values

Estimation of the percentage of null p-values
ROC and AUC

ROC and AUC
FBED variable selection method using the correlation

FBED variable selection method using the correlation
Correlations

Correlation between a vector and a set of variables