Parents (Total: 8)

The searched file hash is included in 8 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	DEDD7A3838CB9ADFCD7C692A3646E9B2
PackageArch	x86_64
PackageDescription	Implements an automated binning of numeric variables and factors with respect to a dichotomous target variable. Two approaches are provided: An implementation of fine and coarse classing that merges granular classes and levels step by step. And a tree-like approach that iteratively segments the initial bins via binary splits. Both procedures merge, respectively split, bins based on similar weight of evidence (WOE) values and stop via an information value (IV) based criteria. The package can be used with single variables or an entire data frame. It provides flexible tools for exploring different binning solutions and for deploying them to (new) data.
PackageName	R-woeBinning
PackageRelease	lp153.2.3
PackageVersion	0.1.6
SHA-1	9115279CC30FADCD84855A908603CE95447EBD1E
SHA-256	92DC3C42D4D5995022D44CCEC92358AE2998F53C2818058875F09E0E7DD2022F

Key	Value
MD5	C6B4C0CB6BD6A459E1450E00E6858922
PackageArch	x86_64
PackageDescription	Implements an automated binning of numeric variables and factors with respect to a dichotomous target variable. Two approaches are provided: An implementation of fine and coarse classing that merges granular classes and levels step by step. And a tree-like approach that iteratively segments the initial bins via binary splits. Both procedures merge, respectively split, bins based on similar weight of evidence (WOE) values and stop via an information value (IV) based criteria. The package can be used with single variables or an entire data frame. It provides flexible tools for exploring different binning solutions and for deploying them to (new) data.
PackageName	R-woeBinning
PackageRelease	2.19
PackageVersion	0.1.6
SHA-1	234CE22222244B1BB5C17533E33C0CD5D637286D
SHA-256	A645BA65B107EF8209A95D21D0E1B460605A6A5449427D4947B220E852317CDD

Key	Value
MD5	4BFB7DD87AE1455F0899EA67DC18ACF8
PackageArch	x86_64
PackageDescription	Implements an automated binning of numeric variables and factors with respect to a dichotomous target variable. Two approaches are provided: An implementation of fine and coarse classing that merges granular classes and levels step by step. And a tree-like approach that iteratively segments the initial bins via binary splits. Both procedures merge, respectively split, bins based on similar weight of evidence (WOE) values and stop via an information value (IV) based criteria. The package can be used with single variables or an entire data frame. It provides flexible tools for exploring different binning solutions and for deploying them to (new) data.
PackageMaintainer	https://www.suse.com/
PackageName	R-woeBinning
PackageRelease	lp154.2.1
PackageVersion	0.1.6
SHA-1	A89C79501DFA4571CFDD98EA35E716413B43B154
SHA-256	E534133E1D5C5EA3928BEA89658D51BF7D073A539DEFD4FED98AA1FE3C82BCE7

Key	Value
MD5	645E846F8725C29DE315721F512C67DF
PackageArch	x86_64
PackageDescription	Implements an automated binning of numeric variables and factors with respect to a dichotomous target variable. Two approaches are provided: An implementation of fine and coarse classing that merges granular classes and levels step by step. And a tree-like approach that iteratively segments the initial bins via binary splits. Both procedures merge, respectively split, bins based on similar weight of evidence (WOE) values and stop via an information value (IV) based criteria. The package can be used with single variables or an entire data frame. It provides flexible tools for exploring different binning solutions and for deploying them to (new) data.
PackageName	R-woeBinning
PackageRelease	lp152.2.6
PackageVersion	0.1.6
SHA-1	17D31B51872D856B79A993A61AD979EC0E549E2D
SHA-256	33CA21AE4B5F2E2692B1014C2009DC2CCB3E29111BEDB02FF40E829895E84291

Key	Value
MD5	29513BE8B35D8E8530A58EFA95DB8AAC
PackageArch	x86_64
PackageDescription	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.
PackageName	R-scorecard
PackageRelease	lp154.1.1
PackageVersion	0.3.6
SHA-1	1426870C9E91CECF5DBFB8088C8458A11A7F0347
SHA-256	553F1F0AE120A7FF54BCD7CA22FB24B0D33813A1A42B798BEE796BFB6E7E77E3

Key	Value
MD5	31770DF642C95A3A71C63EA7849DD1A5
PackageArch	x86_64
PackageDescription	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.
PackageName	R-scorecard
PackageRelease	lp152.1.1
PackageVersion	0.3.6
SHA-1	D8ADA629FFA2CA04A86EA1856E7EA0A50F30859B
SHA-256	7876B33F040BCBB46CBF03D03B2DB8FFCEC83A73D57AF4FADDE2291FAC0C3219

Key	Value
MD5	A2D9B572295312F07954E441C0BFF4E7
PackageArch	x86_64
PackageDescription	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.
PackageName	R-scorecard
PackageRelease	lp153.1.1
PackageVersion	0.3.6
SHA-1	D2B986C470A915FD233913A02E8F3EA779456233
SHA-256	F8E99842EB7DA595648F2276C433318E7DC646A520711726520C7B7C8F452A2A

Key	Value
MD5	D28E6E2C1BCED8AD616B57E7D8C2D63E
PackageArch	x86_64
PackageDescription	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.
PackageName	R-scorecard
PackageRelease	1.3
PackageVersion	0.3.6
SHA-1	01CF1923928A5F7951C769BA024048AF0AE68420
SHA-256	3B897F6BB052D841EF491C0C8348476F0FBD577A8763EB07CBE19DB06B1C8499