CaDrA: Candidate Driver Analysis

#' Pre-computed permutation results for simulated data (\code{sim_FS}) 
#'
#' The permutation result returned from \code{CaDrA} 
#' using pre-simulated dataset
#' (\code{FS = sim_FS}), pre-simulated input scores 
#' (\code{input_score = sim_Scores}), 
#' \code{top_N = 7}, \code{method = "ks_pval"}, \code{alternative = "less"}, 
#' \code{search_method = "both"}, \code{max_size = 10},
#' \code{obs_best_score = NULL} and \code{n_perm = 1000} 
#' as inputs to the function.
#'
#' @docType data
#'
#' @usage data(perm_res)
#'
#' @format A list of objects returned from \code{CaDrA} function. The resulting 
#' object contains a list of key parameters that was used to run the
#' permutation-based testing, a vector of permuted best scores for 
#' a given n_perm, an observed best score, and a permuted p-value.
#'
#' To visualize the Empirical Null Distribution of the permuted best scores over 
#' n_perm iterations, just pass the resulting list to \code{permutation_plot}.
#' 
#' See \code{permutation_plot} for more details.
#'
#' @return a list of objects returned from \code{CaDrA} function
#'
#' @references Kartha VK, Kern JG, Sebastiani P, Zhang L,
#' Varelas X, Monti S (2017) CaDrA: A computational framework for performing
#' candidate driver analyses using binary genomic features.
#' (\href{https://www.frontiersin.org/articles/10.3389/fgene.2019.00121/full}{Frontiers in Genetics})
#'
#' @examples
#'
#' # Load the pre-computed permutation results for sim_FS
#' data(perm_res)
#'
#' # Plot the Empirical Null Distribution of the permuted best scores
#' # against its observed best score
#' permutation_plot(perm_res = perm_res)
#'
"perm_res"