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pchc (version 0.5)

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

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Version

Install

install.packages('pchc')

Monthly Downloads

304

Version

0.5

License

GPL (>= 2)

Maintainer

Michail Tsagris

Last Published

March 21st, 2021

Functions in pchc (0.5)

ROC and AUC

ROC and AUC
Variable selection for continuous data using the FBED algorithm

FBED variable selection method using the correlation
Read big data or a big.matrix object

Read big data or a big.matrix object
Correlation matrix for FBM class matrices (big matrices)

Correlation matrix for FBM class matrices (big matrices)
Adjacency matrix of a Bayesian network

Adjacency matrix of a Bayesian network
Correlations

Correlation between a vector and a set of variables
Chi-square and G-square tests of (unconditional) indepdence

Chi-square and G-square tests of (unconditional) indepdence
Plot of a Bayesian network

Plot of a Bayesian network
Utilities for the skeleton of a (Bayesian) network

Utilities for the skeleton of a (Bayesian) Network
Lower limit of the confidence of an edge

Lower limit of the confidence of an edge
All pairwise G-square and chi-square tests of indepedence

All pairwise G-square and chi-square tests of indepedence
The MMHC and MMTABU Bayesian network learning algorithms

The MMHC and MMTABU Bayesian network learning algorithms
Markov blanket of a node in a Bayesian network

Markov blanket of a node in a Bayesian network
Continuous data simulation from a DAG

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

Correlation significance testing using Fisher's z-transformation
Outliers free data via the reweighted MCD

Outliers free data via the reweighted MCD
The FEDHC and FEDTABU Bayesian network learning algorithms

The FEDHC and FEDTABU Bayesian network learning algorithms
Skeleton of the MMHC algorithm

The skeleton of a Bayesian network learned with the MMHC algorithm
The PCHC and PCTABU Bayesian network learning algorithms

The PCHC and PCTABU Bayesian network learning algorithms
Variable selection for continuous data using the MMPC algorithm

Max-Min Parents and Children variable selection algorithm for continuous responses
Skeleton of the PC algorithm

The skeleton of a Bayesian network learned with the PC algorithm
Skeleton of the FEDHC algorithm

The skeleton of a Bayesian network produced by the FEDHC algorithm
G-square and Chi-square test of conditional indepdence

G-square test of conditional indepdence
Bootstrap versions of the skeleton of a Bayesian network

Bootstrap versions of the skeleton of a Bayesian network
Partial correlation between two continuous variables

Partial correlation
Check whether a directed graph is acyclic

Check whether a directed graph is acyclic
pchc-package

Bayesian Network Learning with the PCHC and Related Algorithms
Variable selection for continuous data using the PC-simple algorithm

Variable selection using the PC-simple algorithm
Estimation of the percentage of null p-values

Estimation of the percentage of null p-values
Topological sort of a Bayesian network

Topological sort of a Bayesian network
Random values simulation from a Bayesian network

Random values simulation from a Bayesian network