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