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rawKS

The goal of rawKS is to easily extract the raw KS statistics to plot and report. The Python version pyks is hosted from GitHub.

Installation

The development version from GitHub with:

# install.packages("devtools")
devtools::install_github("JiaxiangBU/rawKS")

Example

library(tidyverse)
#> Registered S3 methods overwritten by 'ggplot2':
#>   method         from 
#>   [.quosures     rlang
#>   c.quosures     rlang
#>   print.quosures rlang
#> ── Attaching packages ───────────────────────────────────────────────────────── tidyverse 1.2.1 ──
#> <U+2713> ggplot2 3.1.1     <U+2713> purrr   0.3.3
#> <U+2713> tibble  2.1.3     <U+2713> dplyr   0.8.3
#> <U+2713> tidyr   0.8.3     <U+2713> stringr 1.4.0
#> <U+2713> readr   1.3.1     <U+2713> forcats 0.4.0
#> Warning: package 'purrr' was built under R version 3.6.1
#> Warning: package 'dplyr' was built under R version 3.6.1
#> ── Conflicts ──────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag()    masks stats::lag()
library(rawKS)
data("two_class_example_edited")
max(ks_table(two_class_example_edited$yhat, two_class_example_edited$y)$ks)
#> [1] 0.7276892
ks_table(two_class_example_edited$yhat, two_class_example_edited$y) %>% 
  ks_plot()

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.