@@ -3,8 +3,8 @@ Type: Package

 Title: A framework for cross-validated classification problems, with

        applications to differential variability and differential

        distribution testing

-Version: 3.3.15

-Date: 2023-02-21

+Version: 3.3.16

+Date: 2023-02-26

 Authors@R:

     c(

     person(given = "Dario", family = "Strbenac", email = "dario.strbenac@sydney.edu.au", role = c("aut", "cre")),

@@ -477,7 +477,7 @@ setClassUnion("SelectParamsOrNULL", c("SelectParams", "NULL"))

 #' @docType class

 #' @section Constructor:

 #' \describe{

-#' \item{\preformatted{SelectParams(featureRanking, characteristics = DataFrame(), minPresence = 1, intermediate = character(0),subsetToSelections = TRUE, tuneParams = list(nFeatures = seq(10, 100, 10), performanceType = "Balanced Accuracy"), ...)}}{Creates a \code{SelectParams} object which stores the function(s) which will do the selection and parameters that the function will use.\cr

+#' \item{\code{SelectParams(featureRanking, characteristics = DataFrame(), minPresence = 1, intermediate = character(0),subsetToSelections = TRUE, tuneParams = list(nFeatures = seq(10, 100, 10), performanceType = "Balanced Accuracy"), ...)}}{Creates a \code{SelectParams} object which stores the function(s) which will do the selection and parameters that the function will use.\cr

 #'     \describe{

 #'         \item{\code{featureRanking}}{A character keyword referring to a registered feature ranking function. See \code{\link{available}} for valid keywords.}

 #'         \item{\code{characteristics}}{A \code{\link{DataFrame}} describing the characteristics of feature selection to be done. First column must be named \code{"charateristic"} and second column must be named \code{"value"}. If using wrapper functions for feature selection in this package, the feature selection name will automatically be generated and therefore it is not necessary to specify it.}

@@ -582,7 +582,7 @@ setClass("TrainParams", representation(

 #' @docType class

 #' @section Constructor:

 #' \describe{

-#' \item{\preformatted{TrainParams(classifier, balancing = c("downsample", "upsample", "none"), characteristics = DataFrame(),

+#' \item{\code{TrainParams(classifier, balancing = c("downsample", "upsample", "none"), characteristics = DataFrame(),

 #' intermediate = character(0), tuneParams = NULL, getFeatures = NULL, ...)}}{

 #' Creates a \code{TrainParams} object which stores the function which will do the

 #' classifier building and parameters that the function will use.

@@ -854,7 +854,7 @@ setClassUnion("ModellingParamsOrNULL", c("ModellingParams", "NULL"))

 #' @docType class

 #' 

 #' @section Constructor:

-#' \preformatted{ClassifyResult(characteristics, originalNames, originalFeatures,

+#' \code{ClassifyResult(characteristics, originalNames, originalFeatures,

 #'               rankedFeatures, chosenFeatures, models, tunedParameters, predictions, actualOutcome, importance = NULL, modellingParams = NULL, finalModel = NULL)}

 #' \describe{

 #' \item{\code{characteristics}}{A \code{\link{DataFrame}} describing the

@@ -292,9 +292,10 @@ calcCostsAndPerformance <- function(precisionPathways, costs = NULL)

 #' @param object A set of pathways of class \code{PrecisionPathways}.

 #' @param weights A numeric vector of length two specifying how to weight the predictive accuracy

 #' and the cost during ranking. Must sum to 1.

+#' @param ... Not used but just following the S3 requirement of the generic template.

 #' @rdname precisionPathwaysEvaluations

 #' @export

-summary.PrecisionPathways <- function(object, weights = c(accuracy = 0.5, cost = 0.5))

+summary.PrecisionPathways <- function(object, weights = c(accuracy = 0.5, cost = 0.5), ...)

 {

   summaryTable <- data.frame(Pathway = rownames(object[["performance"]]),

                              `Balanced Accuracy` = object[["performance"]][, "accuracy"],

@@ -39,7 +39,7 @@ created by \code{\link{crossValidate}}, \code{\link{runTests}} or \code{\link{ru

 }

 \section{Constructor}{

-\preformatted{ClassifyResult(characteristics, originalNames, originalFeatures,

+\code{ClassifyResult(characteristics, originalNames, originalFeatures,

               rankedFeatures, chosenFeatures, models, tunedParameters, predictions, actualOutcome, importance = NULL, modellingParams = NULL, finalModel = NULL)}

 \describe{

 \item{\code{characteristics}}{A \code{\link{DataFrame}} describing the

@@ -16,7 +16,7 @@ feature selection. The empty constructor is provided for convenience.

 \section{Constructor}{

 \describe{

-\item{\preformatted{SelectParams(featureRanking, characteristics = DataFrame(), minPresence = 1, intermediate = character(0),subsetToSelections = TRUE, tuneParams = list(nFeatures = seq(10, 100, 10), performanceType = "Balanced Accuracy"), ...)}}{Creates a \code{SelectParams} object which stores the function(s) which will do the selection and parameters that the function will use.\cr

+\item{\code{SelectParams(featureRanking, characteristics = DataFrame(), minPresence = 1, intermediate = character(0),subsetToSelections = TRUE, tuneParams = list(nFeatures = seq(10, 100, 10), performanceType = "Balanced Accuracy"), ...)}}{Creates a \code{SelectParams} object which stores the function(s) which will do the selection and parameters that the function will use.\cr

     \describe{

         \item{\code{featureRanking}}{A character keyword referring to a registered feature ranking function. See \code{\link{available}} for valid keywords.}

         \item{\code{characteristics}}{A \code{\link{DataFrame}} describing the characteristics of feature selection to be done. First column must be named \code{"charateristic"} and second column must be named \code{"value"}. If using wrapper functions for feature selection in this package, the feature selection name will automatically be generated and therefore it is not necessary to specify it.}

@@ -15,7 +15,7 @@ The empty constructor is provided for convenience.

 \section{Constructor}{

 \describe{

-\item{\preformatted{TrainParams(classifier, balancing = c("downsample", "upsample", "none"), characteristics = DataFrame(),

+\item{\code{TrainParams(classifier, balancing = c("downsample", "upsample", "none"), characteristics = DataFrame(),

 intermediate = character(0), tuneParams = NULL, getFeatures = NULL, ...)}}{

 Creates a \code{TrainParams} object which stores the function which will do the

 classifier building and parameters that the function will use.

@@ -10,7 +10,7 @@

 \usage{

 calcCostsAndPerformance(precisionPathways, costs = NULL)

-\method{summary}{PrecisionPathways}(object, weights = c(accuracy = 0.5, cost = 0.5))

+\method{summary}{PrecisionPathways}(object, weights = c(accuracy = 0.5, cost = 0.5), ...)

 \method{bubblePlot}{PrecisionPathways}(precisionPathways, pathwayColours = NULL)

@@ -36,6 +36,8 @@ calcCostsAndPerformance(precisionPathways, costs = NULL)

 \item{weights}{A numeric vector of length two specifying how to weight the predictive accuracy

 and the cost during ranking. Must sum to 1.}

+\item{...}{Not used but just following the S3 requirement of the generic template.}

+

 \item{pathwayColours}{A named vector of colours with names being the names of pathways. If none is specified,

 a default colour scheme will automatically be chosen.}