#'

#' @export

format_nfold <- function(nfold_df, score_cols, lab_col, fold_col) {

  # Validate arguments

#

.load_ggplot2 <- function() {

    )

  }

}

  } else {

    )

  }

  )

  # === Create a ggplot object ===

      )

    )

      )

      )

        )

      }

      )

    }

      )

    )

    }

  } else {

    ))

# Summarize basic evaluation measures

#

.summarize_basic <- function(lpoints, mdat) {

  # Summarize AUC of ROC or PRC curves

#

create_curves <- function(pevals, scores = NULL, labels = NULL,

                          x_bins = 1000, keep_pevals = FALSE, ...) {

  # === Validate input arguments ===

  # Create pevals from scores and labels if pevals is missing

#

create_roc <- function(pevals, scores = NULL, labels = NULL, x_bins = 1000,

                       keep_pevals = FALSE, ...) {

  # === Create a ROC curve ===

#

create_prc <- function(pevals, scores = NULL, labels = NULL, x_bins = 1000,

                       keep_pevals = FALSE, ...) {

  # === Create a Precision-Recall curve ===

.create_curve <- function(x_name, y_name, func, func_name, class_name,

                          pevals, scores = NULL, labels = NULL, x_bins = 1000,

                          keep_pevals = FALSE, ...) {

  # === Validate input arguments ===

  # Create pevals from scores and labels if pevals is missing

  # === Create a ggplot object ===

  )

  # === Create a ggplot object ===

  )

  # === Create a ggplot object ===

  )

# Get pAUCs

#

.gather_paucs <- function(curves) {

  # Collect AUCs of ROC or PRC curves

                            calc_avg = FALSE, cb_alpha = 0.05,

                            raw_curves = FALSE, na_worst = TRUE,

                            ties_method = "equiv") {

  # === Calculate AUC ROC ===

    # AUC with the U statistic

#'

#'   \tabular{ll}{

#'     \strong{Function} \tab \strong{Description} \cr

#'   }

#'

#' @section S3 generics:

#'  \code{S3} objects generated by the \code{\link{evalmod}} function.

#'

#'   \tabular{lll}{

#'   }

#'

#' @section Performance measure calculations:

#' @importFrom ggplot2 fortify

#' @importFrom grDevices col2rgb rainbow rgb

#' @importFrom graphics abline layout legend lines

#' @importFrom methods is

#' @importFrom stats qnorm rbeta rnorm sd qt

#' @importFrom data.table frank

#'

calc_auc_with_u <- function(sdat, scores = NULL, labels = NULL, na_worst = TRUE,

                            ties_method = "equiv", keep_sdat = FALSE,

                            ustat_method = "frank", ...) {

  # === Validate input arguments ===

  # Create sdat from scores and labels if sdat is missing

#

.join_datasets <- function(..., efunc_vtype = NULL, efunc_nrow = NULL,

                           byrow = FALSE, chklen = TRUE) {

  # Validate arguments

      } else {

      }

#

.validate_join_datasets_args <- function(..., efunc_vtype, efunc_nrow, byrow,

                                         chklen) {

  # Check ...

# matplot wrapper

#

.matplot_wrapper <- function(obj, type, curvetype, main, xlab, ylab) {

  # === Validate input arguments ===

                          modname = as.character(NA), dsid = 1L,

                          posclass = NULL, na_worst = TRUE,

                          ties_method = "equiv", mode = "rocprc", ...) {

  # === Validate input arguments ===

    # === Create an S3 object ===

    )

  } else {

    # Get score ranks and sorted indices

    # === Create an S3 object ===

    )

  }

#

.rank_scores <- function(scores, na_worst = TRUE, ties_method = "equiv",

                         validate = TRUE) {

  # === Validate input arguments ===

.validate_reformat_data_args <- function(scores, labels, modname, dsid,

                                         posclass, na_worst, ties_method,

                                         mode, ...) {

  # Check '...'

# Calculate basic evaluation measures from confusion matrices

#

calc_measures <- function(cmats, scores = NULL, labels = NULL, ...) {

  # === Validate input arguments ===

  # Create cmats from scores and labels if cmats is missing

#

.validate_scores_and_labels <- function(obj, obj_name, scores, labels, ...) {

    # Check if scores and labels are specified

      )

    }

  }

      )

    }

  }

# Get AUCs

#

.gather_aucs <- function(lcurves, mdat) {

  # Collect AUCs of ROC or PRC curves

# Validate curves object generated by .pl_main_rocprc()

#

.validate_curves_common <- function(curves, class_name) {

  # Need to validate only once

#'

#' @export

create_sim_samples <- function(n_repeat, np, nn, score_names = "random") {

  # === Validate input arguments ===

pl_main <- function(mdat, mode = "rocprc", calc_avg = TRUE, cb_alpha = 0.05,

                    raw_curves = FALSE, x_bins = 1000, interpolate = TRUE,

                    na_worst = TRUE, ties_method = "equiv", validate = TRUE) {

  # === Validation ===

#

.validate_pl_main_args <- function(mdat, mode, calc_avg, cb_alpha, raw_curves,

                                   x_bins, interpolate) {

  # Validate mdat

                   expd_first = NULL, mode = "rocprc",

                   nfold_df = NULL, score_cols = NULL, lab_col = NULL,

                   fold_col = NULL, ...) {

  # === Join datasets ===

#

.validate_mmdata_args <- function(lscores, llabels, modnames, dsids, posclass,

                                  na_worst, ties_method, expd_first, mode) {

  # Check lscores and llabels

                            arg_names) {

  # Check class

    )

  }

  # Check class items

    )

  }

  # Check attributes

    )

  }

  # Check args

    )

  }

}

#

.calc_avg_common <- function(obj, mode, class_name, modnames, uniq_modnames,

                             cb_alpha, x_bins) {

  # === Validate input arguments ===

      )

#'

#' # a function to test mode = "aucroc"

#' func_evalmod_aucroc <- function(samp) {

#'   as.data.frame(uaucs)

#' }

#'

                    posclass = NULL, na_worst = TRUE, ties_method = "equiv",

                    calc_avg = TRUE, cb_alpha = 0.05, raw_curves = FALSE,

                    x_bins = 1000, interpolate = TRUE, ...) {

  # Validation

      )

    }

                                   posclass, na_worst, ties_method,

                                   calc_avg, cb_alpha, raw_curves,

                                   x_bins, interpolate) {

  # Check mode

    ),

    )

  )

}

    ),

    )

  )

}

#

create_confmats <- function(fmdat, scores = NULL, labels = NULL,

                            keep_fmdat = FALSE, ...) {

  # === Validate input arguments ===

  # Create fmdat from scores and labels if fmdat is missing