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## Package level imports/exports
#' @import igraph
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## Add package documentation
#' The CausalR package
#'
#' Causal network analysis methods for regulator prediction and network reconstruction from genome scale data.
#'
#' @rdname CausalR-package
#' @name CausalR-package
#' @aliases CausalR-package CausalR
#' @concept CausalR
#' @docType package
#'
#' @details The most important functions are:
#' \itemize{
#' \item{\code{\link{CreateCCG}}: read a computational causal graph from a .sif file}
#' \item{\code{\link{ReadExperimentalData}}: read a experimental data from a .txt file}
#' \item{\code{\link{MakePredictions}}: make causal reasoning predictions from a CCG}
#' \item{\code{\link{ScoreHypothesis}}: score causal reasoning predictions}
#' \item{\code{\link{CalculateSignificance}}: calculate statisitical significance of a result}
#' \item{\code{\link{RankTheHypotheses}}: compare different possible regulatory hypotheses on a single CCG}
#' \item{\code{\link{runSCANR}}: reduce false positives by selecting common hypotheses across pathlengths}
#' \item{\code{\link{WriteExplainedNodesToSifFile}}: reconstruct hypothesis specific regulatory network}
#' }
#'
#' @references
#' \itemize{
#' \item{"CausalR - extracting mechanistic sense from genome scale data", Bradley, G. and Barrett, S.J., Application note, Bioinformatics (\emph{submitted})}
#' \item{"Causal reasoning on biological networks: interpreting transcriptional changes", Chindelevitch \emph{et al.}, Bioinformatics \bold{28} 1114 (2012). doi:\href{http://dx.doi.org/10.1093/bioinformatics/bts090}{10.1093/bioinformatics/bts090}}
#' \item{"Assessing statistical significance in causal graphs", Chindelevitch \emph{et al.}, BMC Bioinformatics \bold{13} 35 (2012). doi:\href{http://dx.doi.org/10.1186/1471-2105-13-35}{10.1186/1471-2105-13-35}}
#' }
#'
#' @author{
#' Glyn Bradley, Steven J. Barrett, Chirag Mistry, Mark Pipe, David Riley, David Wille, Bhushan Bonde, Peter Woollard
#' }
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