Package: scorecardModelUtils 0.0.1.0

scorecardModelUtils: Credit Scorecard Modelling Utils

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.

Authors:Arya Poddar [aut, cre], Aiana Goyal [ctb], Kanishk Dogar [ctb]

scorecardModelUtils_0.0.1.0.tar.gz
scorecardModelUtils_0.0.1.0.zip(r-4.7)scorecardModelUtils_0.0.1.0.zip(r-4.6)scorecardModelUtils_0.0.1.0.zip(r-4.5)
scorecardModelUtils_0.0.1.0.tgz(r-4.6-any)scorecardModelUtils_0.0.1.0.tgz(r-4.5-any)
scorecardModelUtils_0.0.1.0.tar.gz(r-4.7-any)scorecardModelUtils_0.0.1.0.tar.gz(r-4.6-any)
scorecardModelUtils_0.0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
scorecardModelUtils/json (API)

# Install 'scorecardModelUtils' in R:
install.packages('scorecardModelUtils', repos = c('https://aryapoddar.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.46 score 29 scripts 208 downloads 27 exports 93 dependencies

Last updated from:1430b62610. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK167
source / vignettesOK191
linux-release-x86_64OK149
macos-release-arm64OK178
macos-oldrel-arm64OK190
windows-develOK109
windows-releaseOK115
windows-oldrelOK101
wasm-releaseOK127

Exports:cat_new_classcategorical_ivclub_cat_classcv_filtercv_tablecv_testdtree_split_valdtree_trend_ivfn_conf_matfn_cross_indexfn_errorfn_modefn_targetgini_tablegradient_boosting_parametersiv_filteriv_tablemissing_valnum_to_catothers_classrandom_forest_parameterssamplingscallingscoringsupport_vector_parametersunivariatevif_filter

Dependencies:abindbackportsbitbit64blobbootbroomcachemcarcarDatachronclassclicolorspacecowplotcpp11DBIDerivdoBydplyre1071farverfastmapforecastFormulafracdiffgbmgenericsggplot2gluegsubfngtableinumisobandlabelinglatticelibcoinlifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkminqamodelrmvtnormnlmenloptrnnetnumDerivpartykitpbkrtestpillarpkgconfigplyrprotoproxypurrrquantregR6randomForestrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rlangrpartRSQLiteS7scalesSparseMsqldfstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo

Readme and manuals

Help Manual

Help pageTopics
Clubbing class of categorical variables with low population percentage with another class of similar event ratecat_new_class
IV table for individual categorical variablecategorical_iv
Clubbing class of a categorical variable with low population percentage with another class of similar event rateclub_cat_class
Variable reduction based on Cramer's V filtercv_filter
Pairwise Cramer's V among a list of categorical variablescv_table
Cramer's V value between two categorical variablescv_test
Getting the split value for terminal nodes from decision treedtree_split_val
Recursive Decision Tree partitioning with monotonic event rate along with IV table for individual numerical variabledtree_trend_iv
Creates confusion matrix and its related measuresfn_conf_mat
Creates random index for k-fold cross validationfn_cross_index
Computes error measures between observed and predicted valuesfn_error
Calculating mode value of a vectorfn_mode
Redefines target valuefn_target
Performance measure table with Gini coefficient, KS-statistics and Gini lift curvegini_table
Hyperparameter optimisation or parameter tuning for Gradient Boosting Regression Modelling by grid searchgradient_boosting_parameters
Variable reduction based on Information Value filteriv_filter
WOE and IV table for list of numerical and categorical variablesiv_table
Missing value imputationmissing_val
Binning numerical variables based on cuts from IV tablenum_to_cat
Clubbing of classes of categorical variable with low population percentage into one classothers_class
Hyperparameter optimisation or parameter tuning for Random Forest by grid searchrandom_forest_parameters
Random sampling of data into train and testsampling
Converting coefficients of logistic regression into scores for scorecard buildingscalling
Scoring a dataset with class based on a scalling logic to arrive at final scorescoring
Hyperparameter optimisation or parameter tuning for Suppert Vector Machine by grid searchsupport_vector_parameters
Univariate analysis of variablesunivariate
Removing multicollinearity from a model using vif testvif_filter