modified 0.3.9

.Rbuildignore

@@ -7,3 +7,4 @@
 ^README\.Rmd$
 ^README\.md$
 ^\.travis\.yml$
+^docs$

.gitignore

@@ -8,4 +8,6 @@ alookr.Rproj
 .DS_Store
 doc
 Meta
-_pkgdown.yml
+_pkgdown.yml
+/doc/
+/Meta/

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: alookr
 Type: Package
 Title: Model Classifier for Binary Classification
-Version: 0.3.8.9000
+Version: 0.3.9
 Authors@R: c(
   person("Choonghyun", "Ryu",, "[email protected]", role = c("aut", "cre"))
   )
@@ -46,8 +46,8 @@ Suggests:
 Author: Choonghyun Ryu [aut, cre]
 Maintainer: Choonghyun Ryu <[email protected]>
 BugReports: https://github.com/choonghyunryu/alookr/issues
-License: GPL-2 | file LICENSE
+License: GPL-2
 Encoding: UTF-8
 VignetteBuilder: knitr
-RoxygenNote: 7.1.2
+RoxygenNote: 7.2.3
 Language: en-US

NEWS.md

@@ -0,0 +1,101 @@
+# alookr 0.3.9
+
+## MINOR CHANGES
+
+* Fix error in treatment_corr() that is "All columns in a tibble must be vectors." error. 
+    - (#6, thanks to Cathy Tomson)
+
+
+
+# alookr 0.3.8
+
+## BUG FIXES
+
+* Fix error in treatment_corr() that is "All columns in a tibble must be vectors." error. 
+    - (#6, thanks to Cathy Tomson)
+
+
+
+# alookr 0.3.7      
+      
+## MAJOR CHANGES
+
+* Removed plan(multiprocess) from logic for parallel processing. 
+    - Because, plan(multiprocess) of future is deprecated. (#2, thanks to Henrik Bengtsson)
+
+## MINOR CHANGES
+
+* Remove the waring of "UNRELIABLE VALUE" with seed = TRUE in future function. 
+
+## BUG FIXES
+
+* Fix error in run_performance() that is "replacement has length zero" error.
+    - (#5, thanks to Muhammad Fawad)
+
+
+
+# alookr 0.3.6
+      
+## MINOR CHANGES
+
+* Implemented a function to replace the unbalanced package used in the process of performing split data. 
+    - This is because unbalanced packages have been removed from CRAN. (#3)
+
+
+
+# alookr 0.3.5
+      
+## BUG FIXES
+
+* Fix error in glmnet when run_predict() is performed with test data that has more variables than train data.
+
+
+
+# alookr 0.3.4
+      
+## MAJOR CHANGES
+
+* add xgboosting methodlogy for binary classifier.
+* add lasso regression model for binary classifier.      
+
+
+
+# alookr 0.3.3
+      
+## BUG FIXES
+
+* run_predict() fixed error when try to predict on dataset without the response variable 
+    - (thanks @shivakhanal, #1).
+
+## MINOR CHANGES
+
+* run_models(), run_predict(), run_performance() not support future::multiprocess when running R from RStudio. 
+
+
+
+# alookr 0.3.2
+      
+## BUG FIXES
+
+* Fixed explanation errors in `Classification Modeling` vignettes for debian linux.
+
+## MINOR CHANGES
+
+* Renamed compare_category() to compare_target_category(). 
+    - This is because it overlaps the function name of the dlookr package.
+* Renamed compare_numeric() to compare_target_numeric(). 
+    - This is because it overlaps the function name of the dlookr package.
+* compare_target_category() modified from is.tibble(), as.tibble() to is_tibble(), as_tibble().  
+* compare_diag() modified from is.tibble(), as.tibble() to is_tibble(), as_tibble().  
+* sampling_target() modified from as.tbl() to tibble::as_tibble().
+
+
+
+# alookr 0.3.1
+      
+## BUG FIXES
+
+* Fixed explanation errors in `Cleansing the dataset` vignettes.
+* Fixed explanation errors in `Classification Modeling` vignettes.
+* Modified explanation errors in `Splitting the dataset` vignettes.
+

R/evaluate.R

@@ -110,7 +110,6 @@ get_cross <- function(predicted, y, positive) {
 #' In this case, the speed of visualization can be slow.
 #'
 #' @examples
-#' \donttest{
 #' library(ggplot2)
 #' library(rpart)
 #' data(kyphosis)
@@ -130,7 +129,6 @@ get_cross <- function(predicted, y, positive) {
 #' plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "prob", measure = "mcc")
 #' plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "prob", measure = "cross")
 #' plot_cutoff(pred, kyphosis$Kyphosis, "present", type = "prob", measure = "half")
-#' }
 #' 
 #' @import dplyr
 #' @import ggplot2
@@ -286,7 +284,6 @@ plot_cutoff <- function(predicted, y, positive, type = c("mcc", "density", "prob
 #' }
 #'
 #' @examples
-#' \donttest{
 #' library(dplyr)
 #'
 #' # Divide the train data set and the test data set.
@@ -326,7 +323,6 @@ plot_cutoff <- function(predicted, y, positive, type = c("mcc", "density", "prob
 #' # Calculate Confusion Matrix by cutoff = 0.55.
 #' performance_metric(attr(pred$predicted[[1]], "pred_prob"), test$Kyphosis,
 #'   "present", "ConfusionMatrix", cutoff = 0.55)
-#' }
 #'    
 #' @importFrom stats density
 #' @export

R/modeling.R

@@ -156,10 +156,7 @@ classifier_dispatch <- function(model = c("logistic", "rpart", "ctree",
 #' # Run the several kinds model fitting by dplyr
 #' train %>%
 #'   run_models(target = "Kyphosis", positive = "present")
-#'
-#' # Run the logistic model fitting by dplyr
-#' train %>%
-#'   run_models(target = "Kyphosis", positive = "present", models = "logistic")
+#' 
 #' @importFrom stats density
 #' @importFrom future plan
 #' @importFrom parallelly supportsMulticore
@@ -313,14 +310,10 @@ predictor <- function(model, .data, target, positive, negative, is_factor,
 #' result <- run_models(.data = train, target = "Kyphosis", positive = "present")
 #' result
 #'
-#' # Predict the model.
-#' pred <- run_predict(result, test)
-#' pred
-#'
 #' # Run the several kinds model predict by dplyr
 #' result %>%
 #'   run_predict(test)
-#'
+#' 
 #' @importFrom stats density
 #' @importFrom future plan
 #' @importFrom parallelly supportsMulticore