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The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.

copied from cf-post-staging / r-scorecard
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conda 365.8 kB | noarch/r-scorecard-0.4.4-r43h785f33e_1.conda  1 year and 1 month ago 958 main
conda 367.3 kB | noarch/r-scorecard-0.4.4-r44h785f33e_1.conda  1 year and 1 month ago 972 main
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