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

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

        applications to differential variability and differential

        distribution testing

-Version: 3.3.9

-Date: 2022-12-08

+Version: 3.4.0

+Date: 2022-12-12

 Authors@R:

     c(

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

@@ -20,18 +20,17 @@ VignetteBuilder: knitr

 Encoding: UTF-8

 biocViews: Classification, Survival

 Depends: R (>= 4.1.0), generics, methods, S4Vectors, MultiAssayExperiment, BiocParallel, survival

-Imports: grid, genefilter, utils, dplyr, plyr, tidyr, rlang, ranger, ggplot2 (>= 3.0.0), ggpubr, reshape2

+Imports: grid, genefilter, utils, dplyr, tidyr, rlang, ranger, ggplot2 (>= 3.0.0), ggpubr, reshape2

 Suggests: limma, edgeR, car, Rmixmod, gridExtra (>= 2.0.0), cowplot,

         BiocStyle, pamr, PoiClaClu, parathyroidSE, knitr, htmltools, gtable,

         scales, e1071, rmarkdown, IRanges, robustbase, glmnet, class, randomForestSRC,

         MatrixModels, xgboost

-Description: The software formalises a framework for classification in R.

-             There are four stages; Data transformation, feature selection, classifier training,

-             and prediction. The requirements of variable types and names are

+Description: The software formalises a framework for classification and survival model evaluatio

+             in R. There are four stages; Data transformation, feature selection, model training,

+             and prediction. The requirements of variable types and variable order are

              fixed, but specialised variables for functions can also be provided.

-             The classification framework is wrapped in a driver loop, that

-             reproducibly carries out a number of cross-validation schemes.

-             Functions for differential expression, differential variability,

+             The framework is wrapped in a driver loop that reproducibly carries out a

+             number of cross-validation schemes. Functions for differential mean, differential variability,

              and differential distribution are included. Additional functions

              may be developed by the user, by creating an interface to the framework.

 License: GPL-3

@@ -87,13 +87,12 @@ exportMethods(selectionPlot)

 exportMethods(show)

 exportMethods(totalPredictions)

 exportMethods(tunedParameters)

+import(BiocParallel)

 import(MultiAssayExperiment)

-import(dplyr)

 import(ggplot2)

 import(ggpubr)

 import(grid)

 import(methods)

-import(plyr)

 import(reshape2)

 import(utils)

 importFrom(S4Vectors,as.data.frame)

@@ -25,12 +25,8 @@

 #' feature selection, \code{"random"} if \code{doRandomFeatures} is \code{TRUE} for metrics of random selection and

 #' \code{"params"} for a list of parameters used during the execution of this function.

 #' @author Harry Robertson

-

-#' @import plyr

-#' @import dplyr

 #'

 #' @export

-#' 

 crissCrossValidate <- function(measurements, outcomes, 

                                nFeatures = 20, selectionMethod = "auto",

@@ -67,6 +67,7 @@ setMethod("runTests", c("matrix"), function(measurements, outcome, ...) # Matrix

 })

 #' @rdname runTests

+#' @import BiocParallel

 #' @export

 setMethod("runTests", "DataFrame", function(measurements, outcome, crossValParams = CrossValParams(), modellingParams = ModellingParams(),

            characteristics = S4Vectors::DataFrame(), ..., verbose = 1)

@@ -130,7 +130,7 @@

       if(tuneCombo != "none") # Add real parameters before function call.

         paramList <- append(paramList, tuneCombo)

       if(attr(featureRanking, "name") == "randomSelection")

-        paramList <- append(paramList, nFeatures = topNfeatures)

+        paramList <- append(paramList, list(nFeatures = topNfeatures))

       do.call(featureRanking, paramList)

     })

deleted file mode 100644

@@ -1,15 +0,0 @@

-url: https://sydneybiox.github.io/ClassifyR/

-template:

-  bootstrap: 5

-articles:

-- title: Menu

-  contents:

-    - introduction

-    - performanceEvaluation

-    - multiViewMethods

-    - incorporateNew

-    - ClassifyR

-    - DevelopersGuide

-navbar:

-  title: ~

-  bg: dark