Refactored Source Code 📚

* All code has been refactored using Clion. The src-folder should now be easy to navigate in.

Author: serkor1

.Rbuildignore

@@ -15,3 +15,6 @@
 ^codemeta
 ^[^/]+\.R$
 Makefile
+src/CMakeLists.txt
+src/cmake-build-debug
+src/.idea

.gitignore

@@ -53,3 +53,9 @@ rsconnect/
 playground/
 docs
 inst/doc
+
+
+# Clion Files
+src/CMakeLists.txt
+src/cmake-build-debug
+src/.idea

R/RcppExports.R

@@ -1,20 +1,6 @@
 # Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
-#' @rdname fmi
-#' @method fmi factor
-#' @export
-fmi.factor <- function(actual, predicted, ...) {
-    .Call(`_SLmetrics_fmi`, actual, predicted)
-}
-
-#' @rdname fmi
-#' @method fmi cmatrix
-#' @export
-fmi.cmatrix <- function(x, ...) {
-    .Call(`_SLmetrics_fmi_cmatrix`, x)
-}
-
 #' @rdname accuracy
 #' @method accuracy factor
 #'
@@ -47,6 +33,22 @@ baccuracy.cmatrix <- function(x, adjust = FALSE, ...) {
     .Call(`_SLmetrics_baccuracy_cmatrix`, x, adjust)
 }
 
+#' @rdname ckappa
+#' @method ckappa factor
+#'
+#' @export
+ckappa.factor <- function(actual, predicted, beta = 1.0, ...) {
+    .Call(`_SLmetrics_ckappa`, actual, predicted, beta)
+}
+
+#' @rdname ckappa
+#' @method ckappa cmatrix
+#'
+#' @export
+ckappa.cmatrix <- function(x, beta = 1.0, ...) {
+    .Call(`_SLmetrics_ckappa_cmatrix`, x, beta)
+}
+
 #' Confusion Matrix
 #'
 #' @description
@@ -185,6 +187,20 @@ fallout.cmatrix <- function(x, micro = NULL, na.rm = TRUE, ...) {
     .Call(`_SLmetrics_fallout_cmatrix`, x, micro, na_rm = na.rm)
 }
 
+#' @rdname fmi
+#' @method fmi factor
+#' @export
+fmi.factor <- function(actual, predicted, ...) {
+    .Call(`_SLmetrics_fmi`, actual, predicted)
+}
+
+#' @rdname fmi
+#' @method fmi cmatrix
+#' @export
+fmi.cmatrix <- function(x, ...) {
+    .Call(`_SLmetrics_fmi_cmatrix`, x)
+}
+
 #' @rdname jaccard
 #' @method jaccard factor
 #' @export
@@ -227,22 +243,6 @@ tscore.cmatrix <- function(x, micro = NULL, na.rm = TRUE, ...) {
     .Call(`_SLmetrics_tscore_cmatrix`, x, micro, na_rm = na.rm)
 }
 
-#' @rdname ckappa
-#' @method ckappa factor
-#'
-#' @export
-ckappa.factor <- function(actual, predicted, beta = 1.0, ...) {
-    .Call(`_SLmetrics_ckappa`, actual, predicted, beta)
-}
-
-#' @rdname ckappa
-#' @method ckappa cmatrix
-#'
-#' @export
-ckappa.cmatrix <- function(x, beta = 1.0, ...) {
-    .Call(`_SLmetrics_ckappa_cmatrix`, x, beta)
-}
-
 #' @rdname mcc
 #' @method mcc factor
 #' @export
@@ -461,6 +461,40 @@ zerooneloss.cmatrix <- function(x, ...) {
     .Call(`_SLmetrics_zerooneloss_cmatrix`, x)
 }
 
+#' Compute the \eqn{R^2}
+#'
+#' @description
+#' The [rsq()]-function calculates the \eqn{R^2}, the [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination), between the ovserved
+#' and predicted <[numeric]> vectors. By default [rsq()] returns the unadjusted \eqn{R^2}. For adjusted \eqn{R^2} set \eqn{k = \kappa - 1}, where \eqn{\kappa} is the number of parameters.
+#'
+#' @usage
+#' # `rsq()`-function
+#' rsq(
+#'   actual,
+#'   predicted,
+#'   k = 0
+#' )
+#'
+#' @inherit huberloss
+#' @param k A <[numeric]>-vector of [length] 1. 0 by default. If \eqn{k>0}
+#' the function returns the adjusted \eqn{R^2}.
+#'
+#' @section Calculation:
+#'
+#' The metric is calculated as follows,
+#'
+#' \deqn{
+#'   R^2 = 1 - \frac{\text{SSE}}{\text{SST}} \frac{n-1}{n - (k + 1)}
+#' }
+#'
+#' Where \eqn{\text{SSE}} is the sum of squared errors, \eqn{\text{SST}} is total sum of squared errors, \eqn{n} is the number of observations, and \eqn{k} is the number of non-constant parameters.
+#'
+#' @family regression
+#'
+rsq <- function(actual, predicted, k = 0) {
+    .Call(`_SLmetrics_rsq`, actual, predicted, k)
+}
+
 #' Compute the \eqn{\text{concordance correlation coefficient}}
 #'
 #' @description
@@ -856,40 +890,6 @@ wrmsle <- function(actual, predicted, w) {
     .Call(`_SLmetrics_wrmsle`, actual, predicted, w)
 }
 
-#' Compute the \eqn{R^2}
-#'
-#' @description
-#' The [rsq()]-function calculates the \eqn{R^2}, the [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination), between the ovserved
-#' and predicted <[numeric]> vectors. By default [rsq()] returns the unadjusted \eqn{R^2}. For adjusted \eqn{R^2} set \eqn{k = \kappa - 1}, where \eqn{\kappa} is the number of parameters.
-#'
-#' @usage
-#' # `rsq()`-function
-#' rsq(
-#'   actual,
-#'   predicted,
-#'   k = 0
-#' )
-#'
-#' @inherit huberloss
-#' @param k A <[numeric]>-vector of [length] 1. 0 by default. If \eqn{k>0}
-#' the function returns the adjusted \eqn{R^2}.
-#'
-#' @section Calculation:
-#'
-#' The metric is calculated as follows,
-#'
-#' \deqn{
-#'   R^2 = 1 - \frac{\text{SSE}}{\text{SST}} \frac{n-1}{n - (k + 1)}
-#' }
-#'
-#' Where \eqn{\text{SSE}} is the sum of squared errors, \eqn{\text{SST}} is total sum of squared errors, \eqn{n} is the number of observations, and \eqn{k} is the number of non-constant parameters.
-#'
-#' @family regression
-#'
-rsq <- function(actual, predicted, k = 0) {
-    .Call(`_SLmetrics_rsq`, actual, predicted, k)
-}
-
 #' Compute the \eqn{\text{symmetric mean absolute percentage error}}
 #'
 #' The [smape()]- and [wsmape()]-function computes the simple and weighted [symmetric mean absolute percentage error](https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error).

src/.gitignore

@@ -1,3 +1,6 @@
 *.o
 *.so
 *.dll
+CMakeLists.txt
+cmake-build-debug
+.idea/