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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-staging / r-scorecard
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conda 362.6 kB | noarch/r-scorecard-0.4.2-r43h785f33e_1.conda  1 year and 8 months ago 1093 main
conda 363.6 kB | noarch/r-scorecard-0.4.2-r42h785f33e_1.conda  1 year and 8 months ago 1098 main
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conda 362.6 kB | noarch/r-scorecard-0.4.2-r41h785f33e_0.conda  1 year and 10 months ago 1242 main

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