lots of clean up for CRAN

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: FFTrees
 Type: Package
 Title: Generate, Visualise, and Evaluate Fast-and-Frugal Decision Trees
-Version: 1.6.1.9000
+Version: 1.6.2
 Date: 2022-07-11
 Authors@R: c(person("Nathaniel", "Phillips", role = c("aut", "cre"),
                      email = "Nathaniel.D.Phillips.is@gmail.com"),

NEWS.md

@@ -1,4 +1,6 @@
-# develop
+# 1.6.2
+
+* Internal cleanup to get package back on CRAN.
 
 ## 1.6.1
 

R/FFTreesguide_function.R

@@ -1,8 +1,8 @@
 #' Opens the FFTrees package guide
 #' @importFrom utils vignette
 #' @export
+#' @return No return value, called for side effects
 #'
-
 FFTrees.guide <- function() {
   vignette("guide", package = "FFTrees")
 }

R/fftrees_create.R

@@ -300,7 +300,8 @@ fftrees_create <- function(data = NULL,
 
   ## Make criterion logical
 
-  if (class(data[[criterion_name]]) %in% c("character", "factor")) {
+  if (inherits(data[[criterion_name]], "character") |
+    inherits(data[[criterion_name]], "factor")) {
 
     # Save original values as decision.labels
     decision.labels <- unique(data[[criterion_name]])
@@ -313,8 +314,6 @@ fftrees_create <- function(data = NULL,
     }
   }
 
-
-
   ## Criterion is in data.test
 
   if (!is.null(data.test)) {

R/fftrees_define.R

@@ -6,6 +6,7 @@
 #'
 #' @export
 #' @keywords internal
+#' @return An FFTrees object with tree definitions
 #'
 #'
 fftrees_define <- function(x, object = NULL) {

R/fftrees_threshold_factor_grid.R

@@ -12,7 +12,7 @@
 #' @import testthat
 #' @importFrom  magrittr "%>%"
 #' @export
-#'
+#' @return A data frame containing accuracy statistics for several factor thresholds
 #'
 fftrees_threshold_factor_grid <- function(thresholds = NULL,
                                           cue_v = NULL,
@@ -161,7 +161,5 @@ fftrees_threshold_factor_grid <- function(thresholds = NULL,
     )
   }
 
-
-
   return(results)
 }

R/fftrees_threshold_numeric_grid.R

@@ -9,9 +9,10 @@
 #' @param cost.outcomes list. A list of length 4 with names 'hi', 'fa', 'mi', and 'cr' specifying the costs of a hit, false alarm, miss, and correct rejection rspectively. E.g.; \code{cost.outcomes = listc("hi" = 0, "fa" = 10, "mi" = 20, "cr" = 0)} means that a false alarm and miss cost 10 and 20 respectively while correct decisions have no cost.
 #' @param goal.threshold character. A string indicating the statistic to maximize when calculting cue thresholds: "acc" = overall accuracy, "wacc" = weighted accuracy, "bacc" = balanced accuracy
 #'
-#'
 #' @export
 #'
+#' @return A data frame containing accuracy statistics for several numeric thresholds
+#'
 fftrees_threshold_numeric_grid <- function(thresholds,
                                            cue_v,
                                            criterion_v,

R/fftrees_wordstofftrees.R

@@ -1,11 +1,12 @@
-
 #' Converts text describing an FFT into an FFT definition.
 #'
 #' @param x FFTrees.
 #' @param my.tree string. A string defining an FFT
 #'
 #' @export
 #'
+#' @return An FFTrees object with a new definition defined by my.tree
+#'
 #' @importFrom stringr str_extract str_detect
 
 fftrees_wordstofftrees <- function(x,

R/helper.R

@@ -406,7 +406,7 @@ comp.pred <- function(formula,
 
         try.pred <- try(predict(train.mod, data.test), silent = TRUE)
 
-        if (class(try.pred) == "try-error") {
+        if (inherits(try.pred, "try-error")) {
           warning("svm crashed predicting new data. That's all I can say")
 
           pred.test <- NULL
@@ -446,7 +446,7 @@ comp.pred <- function(formula,
 
         try.pred <- try(predict(train.mod, data.test.2), silent = TRUE)
 
-        if (class(try.pred) == "try-error") {
+        if (inherits(try.pred, "try-error")) {
           warning("randomForest crashed predicting new data. That's all I can say")
 
           pred.test <- NULL

R/inwords.R

@@ -2,7 +2,7 @@
 #'
 #' @param x FFTrees.
 #' @param tree numeric. The tree to display
-#'
+#' @return A string describing an FFT
 #' @export
 #'
 inwords <- function(x, tree = 1) {

R/plotFFTrees_function.R

@@ -25,6 +25,7 @@
 #' @importFrom graphics text points abline legend mtext segments rect arrows axis par layout plot
 #' @importFrom grDevices gray col2rgb rgb
 #' @export
+#' @return A plot vizualizing an FFT
 #' @examples
 #'
 #' # Create FFTrees of the heart disease data
@@ -249,7 +250,7 @@ plot.FFTrees <- function(x = NULL,
             main <- x$params$main
           }
         } else {
-          if (class(data) == "character") {
+          if (inherits(data, "character")) {
             if (data == "train") {
               main <- "Data (Training)"
             }
@@ -258,7 +259,7 @@ plot.FFTrees <- function(x = NULL,
             }
           }
 
-          if (class(data) == "data.frame") {
+          if (inherits(data, "data.frame")) {
             main <- "Test Data"
           }
         }
@@ -275,7 +276,7 @@ plot.FFTrees <- function(x = NULL,
           tree <- which.tree
         }
 
-        if (class(x) != "FFTrees") {
+        if (!inherits(x, "FFTrees")) {
           stop("You did not include a valid FFTrees class object or specify the tree directly with level.names, level.classes (etc.). Either create a valid FFTrees object with FFTrees() or specify the tree directly.")
         }
 
@@ -289,7 +290,7 @@ plot.FFTrees <- function(x = NULL,
           stop(paste("You asked for a tree that does not exist. This object has", x$trees$n, "trees"))
         }
 
-        if (class(data) == "character") {
+        if (inherits(data, "character")) {
           if (data == "test" & is.null(x$trees$stats$test)) {
             stop("You asked to plot the test data but there are no test data in the FFTrees object")
           }

R/printFFTrees_function.R

@@ -4,6 +4,7 @@
 #' @param x FFTrees. A FFTrees x created from FFTrees()
 #' @param tree integer. The tree to explore.
 #' @param ... additional arguments passed to print.
+#' @return Prints summary information about an FFT to the console
 #' @export
 
 print.FFTrees <- function(x = NULL,

R/showcues_function.R

@@ -6,15 +6,14 @@
 #' @param main Main plot description
 #' @param top An integer indicating how many of the top cues to highlight
 #' @importFrom graphics text points abline legend mtext segments rect arrows axis par layout plot
+#' @return A plot showing cue accuracies from an FFTrees object in a ROC space
 #' @export
 #'
-
 showcues <- function(x = NULL,
                      data = "train",
                      cue.accuracies = NULL,
                      main = NULL,
                      top = 5) {
-
   par0 <- par(no.readonly = TRUE)
   on.exit(par(par0), add = TRUE)
 

R/summaryFFTrees_function.R

@@ -2,9 +2,9 @@
 #' @param object FFTrees.
 #' @param tree integer. The tree to summarise
 #' @param ... additional arguments (currently ignored)
+#' @return A data frame comtaining summary information about an FFT
 #' @export
 #'
-
 summary.FFTrees <- function(object,
                             tree = 1,
                             ...) {
@@ -143,7 +143,5 @@ summary.FFTrees <- function(object,
     "spec"
   )
 
-
-
   return(summary.df)
 }

README.Rmd

@@ -13,13 +13,13 @@ knitr::opts_chunk$set(
 )
 ```
 
-# FFTrees
+# FFTrees `r packageVersion("FFTrees")`
 
 [![Build Status](https://travis-ci.org/ndphillips/FFTrees.svg?branch=master)](https://travis-ci.org/ndphillips/FFTrees)
 [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/FFTrees)](https://CRAN.R-project.org/package=FFTrees)
 [![Downloads](https://cranlogs.r-pkg.org/badges/FFTrees?color=brightgreen)](https://www.r-pkg.org/pkg/FFTrees)
 
-The goal of FFTrees is to create and visualize fast-and-frugal decision trees (FFTs) from data with a binary outcome following the methods described in [Phillips, Neth, Woike & Gaissmaier (2017)](http://journal.sjdm.org/17/17217/jdm17217.pdf).
+The goal of FFTrees is to create and visualize fast-and-frugal decision trees (FFTs) from data with a binary outcome following the methods described in [Phillips, Neth, Woike & Gaissmaier (2017)](https://journal.sjdm.org/17/17217/jdm17217.pdf).
 
 ## Installation
 
@@ -105,7 +105,7 @@ plot(my.fft,
 
 Phillips, Nathaniel D., Neth, Hansjoerg, Woike, Jan K., & Gaissmaier, W. (2017). FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees. *Judgment and Decision Making*, 12(4), 344-368.
 
-We had a lot of fun creating FFTrees and hope you like it too! We have an article introducing the `FFTrees` package in the journal Judgment and Decision Making titled FFTrees: [A toolbox to create, visualize,and evaluate fast-and-frugal decision trees](http://journal.sjdm.org/17/17217/jdm17217.pdf). We encourage you to read the article to learn more about the history of FFTs and how the FFTrees package creates them.
+We had a lot of fun creating FFTrees and hope you like it too! We have an article introducing the `FFTrees` package in the journal Judgment and Decision Making titled FFTrees: [A toolbox to create, visualize,and evaluate fast-and-frugal decision trees](https://journal.sjdm.org/17/17217/jdm17217.pdf). We encourage you to read the article to learn more about the history of FFTs and how the FFTrees package creates them.
 
 If you use FFTrees in your work, please cite us and spread the word so we can continue developing the package
 

README.md

@@ -1,7 +1,7 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# FFTrees
+# FFTrees 1.6.2
 
 [![Build
 Status](https://travis-ci.org/ndphillips/FFTrees.svg?branch=master)](https://travis-ci.org/ndphillips/FFTrees)
@@ -11,7 +11,7 @@ Status](https://travis-ci.org/ndphillips/FFTrees.svg?branch=master)](https://tra
 The goal of FFTrees is to create and visualize fast-and-frugal decision
 trees (FFTs) from data with a binary outcome following the methods
 described in [Phillips, Neth, Woike & Gaissmaier
-(2017)](http://journal.sjdm.org/17/17217/jdm17217.pdf).
+(2017)](https://journal.sjdm.org/17/17217/jdm17217.pdf).
 
 ## Installation
 
@@ -38,10 +38,9 @@ library(FFTrees)
 #>   / \
 #>  F   O
 #>     / \
-#>    F   Trees 1.6.1
+#>    F   Trees 1.6.2
 #> 
 #> Email: Nathaniel.D.Phillips.is@gmail.com
-#> LinkedIn: linkedin.com/in/nathanieldphillips/
 #> FFTrees.guide() opens the main guide.
 ```
 
@@ -155,13 +154,13 @@ heart.fft$competition$test
 #> 1   fftrees 153 64 19  9 61 0.8767123 0.7625 0.2375 0.7710843 0.8714286
 #> 2        lr 153 55 13 18 67 0.7534247 0.8375 0.1625 0.8088235 0.7882353
 #> 3      cart 153 50 19 23 61 0.6849315 0.7625 0.2375 0.7246377 0.7261905
-#> 4        rf 153 58  8 15 72 0.7945205 0.9000 0.1000 0.8787879 0.8275862
+#> 4        rf 153 58  9 15 71 0.7945205 0.8875 0.1125 0.8656716 0.8255814
 #> 5       svm 153 55  7 18 73 0.7534247 0.9125 0.0875 0.8870968 0.8021978
 #>         acc      bacc      cost cost_decisions cost_cues
 #> 1 0.8169935 0.8196062 0.1830065      0.1830065         0
 #> 2 0.7973856 0.7954623 0.2026144      0.2026144        NA
 #> 3 0.7254902 0.7237158 0.2745098      0.2745098        NA
-#> 4 0.8496732 0.8472603 0.1503268      0.1503268        NA
+#> 4 0.8431373 0.8410103 0.1568627      0.1568627        NA
 #> 5 0.8366013 0.8329623 0.1633987      0.1633987        NA
 ```
 
@@ -206,7 +205,7 @@ We had a lot of fun creating FFTrees and hope you like it too! We have
 an article introducing the `FFTrees` package in the journal Judgment and
 Decision Making titled FFTrees: [A toolbox to create, visualize,and
 evaluate fast-and-frugal decision
-trees](http://journal.sjdm.org/17/17217/jdm17217.pdf). We encourage you
+trees](https://journal.sjdm.org/17/17217/jdm17217.pdf). We encourage you
 to read the article to learn more about the history of FFTs and how the
 FFTrees package creates them.