The max-min hill-climbing Bayesian network structure learning algorithm, Ioannis Tsamardinos · Laura E. Brown · Constantin F. Aliferis, Mach Learn DOI 10.1007/s10994-006-6889-7
*This algorithm reconstructs Bayesian Networks from observational data. Therefore it first builds the skeleton of the DAG (directed acyclic graph) with the max-min parents and children (MMPC) algorithm. Afterwards it directs the edges between the vertices with the Bayesian Dirichlet likelihood-equivalence uniform score.
For more information on that read the report appended or The max-min hill-climbing Bayesian network structure learning algorithm , by Ioannis Tsamardinos, Laura E. Brown & Constantin F. Aliferis.*
Before you can use this package, be sure you have the latest R version (>=3.1), RCPP version (>=0.11.1) and the igraph package installed.
Download the R source file (mmhc_1.0.tar.gz), open R (in the console, RStudio, etc.) and install the package into your R environment with:
install.packages("mmhc_1.0.tar.gz")
Include the library with:
library(mmhc)
Here is the example from the man pages of the package:
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