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  "Type": "Package",
  "Title": "Optimal Binning of Continuous and Categorical Variables",
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  "Date": "2018-09-21",
  "Author": "Daniel Safai",
  "Maintainer": "Daniel Safai <danielsafai@gmail.com>",
  "Description": "Tool for easy and efficient discretization of continuous\nand categorical data. The package calculates the most optimal\nbinning of a given explanatory variable with respect to a\nuser-specified target variable. The purpose is to assign a\nunique Weight-of-Evidence value to each of the calculated\nbinpoints in order to recode the original variable. The package\nallows users to impose certain restrictions on the functional\nform on the resulting binning while maximizing the overall\ninformation value in the original data. The package is well\nsuited for logistic scoring models where input variables may be\nsubject to restrictions such as linearity by e.g. regulatory\nauthorities. An excellent source describing in detail the\ndevelopment of scorecards, and the role of Weight-of-Evidence\ncoding in credit scoring is (Siddiqi 2006, ISBN:\n978–0-471–75451–0). The package utilizes the discrete nature of\ndecision trees and Isotonic Regression to accommodate the\ntrade-off between flexible functional forms and maximum\ninformation value.",
  "License": "GPL (>= 2)",
  "Encoding": "UTF-8",
  "RoxygenNote": "6.1.0",
  "NeedsCompilation": "no",
  "Packaged": {
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  "Repository": "https://safai.r-universe.dev",
  "Date/Publication": "2018-10-12 12:50:03 UTC",
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