This one iterates Logistic Regression Model and provide range of model options.
 Final model is based of following options ( user cut-off ):
		- VIF
		- Sign of beta
		- P-Value
		- No. of variables

 Along with model it provides following metrics:
 	- KS
 	- Concordant, Discordant, Pairs, Somers D
 	- AUC/C-Stat/Area under curve
 	- Correlation with bad and model variable

 Create WOE variables for numeric and character variables with following options 
 	- Raw WOE binning 	
 	- Monotonic WOE binning	
 	- Provide SAS code to generate Monotonic WOE binning variables 

 https://github.com/amitmse/in_SAS_/blob/master/Woe_calculation_Macro.sas
 Weight of Evidence Calculation

 Compute Information value and Weight of Evidence variables with following options
 	- Information value
 	- Raw WOE binning 	
 	- Monotonic WOE binning	
 	- Provide SAS code to generate Monotonic WOE binning variables 

 	https://github.com/amitmse/in_SAS_/blob/master/Raw_Information_Value.sas

 Compute Information value

 	https://github.com/amitmse/in_SAS_/blob/master/Raw_information_value_LP.sas

 	https://github.com/amitmse/in_SAS_/blob/master/Information_value_with_Weight.sas