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Discovering causal relationships from data is the ultimate goal of manyresearch areas. Constraint based causal exploration algorithms, such as PC,FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress andhave many applications. A common problem with these methods is the highcomputational complexity, which hinders their applications in real world highdimensional datasets, e.g gene expression datasets. In this paper, we presentan R package, ParallelPC, that includes the parallelised versions of thesecausal exploration algorithms. The parallelised algorithms help speed up theprocedure of experimenting big datasets and reduce the memory used when runningthe algorithms. The package is not only suitable for super-computers orclusters, but also convenient for researchers using personal computers withmulti core CPUs. Our experiment results on real world datasets show that usingthe parallelised algorithms it is now practical to explore causal relationshipsin high dimensional datasets with thousands of variables in a single multicorecomputer. ParallelPC is available in CRAN repository athttps://cran.rproject.org/web/packages/ParallelPC/index.html.

ParallelPC: an R package for efficient constraint based causal exploration