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scorecardModelUtils
View on CRAN: Click
here
Download and install scorecardModelUtils package within the R console
Install from CRAN:
install.packages("scorecardModelUtils")
Install from Github:
library("remotes")
install_github("cran/scorecardModelUtils") Install by package version:
library("remotes")
install_version("scorecardModelUtils", "0.0.1.0") Attach the package and use:
library("scorecardModelUtils")
Maintained by
Arya Poddar
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-04-27
Latest Update: 2019-04-14
Description:
Provides infrastructure functionalities such as missing value treatment, information value calculation, GINI calculation etc. which are used for developing a traditional credit scorecard as well as a machine learning based model. The functionalities defined are standard steps for any credit underwriting scorecard development, extensively used in financial domain.
How to cite:
Arya Poddar (2018). scorecardModelUtils: Credit Scorecard Modelling Utils. R package version 0.0.1.0, https://cran.r-project.org/web/packages/scorecardModelUtils. Accessed 29 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:01), 0.0.0.9 (2018-04-27 12:42)
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Other R packages that scorecardModelUtils depends,
imports, suggests or enhances
Complete documentation for scorecardModelUtils
Functions, R codes and Examples using
the scorecardModelUtils R package
Some associated functions: cat_new_class . categorical_iv . club_cat_class . cv_filter . cv_table . cv_test . dtree_split_val . dtree_trend_iv . fn_conf_mat . fn_cross_index . fn_error . fn_mode . fn_target . gini_table . gradient_boosting_parameters . iv_filter . iv_table . missing_val . num_to_cat . others_class . random_forest_parameters . sampling . scalling . scoring . support_vector_parameters . univariate . vif_filter .
Some associated R codes: functions.R . Full scorecardModelUtils package functions and examples
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