A set of functions to build a scoring model from beginning to end, leading the user
	to follow an efficient and organized development process, reducing significantly the time
	spent on data exploration, variable selection, feature engineering, binning and model selection 
	among other recurrent tasks. 
	The package also incorporates monotonic and customized binning, scaling capabilities that 
	transforms logistic coefficients into points for a better business understanding and 
	calculates and visualizes classic performance metrics of a classification model.

Implements multiple imputation of missing covariates by
    Substantive Model Compatible Fully Conditional Specification.
    This is a modification of the popular FCS/chained equations
    multiple imputation approach, and allows imputation of missing
    covariate values from models which are compatible with the user
    specified substantive model.

Data Sets for Kumar and Petersen (2012).
    Statistical Methods in Customer Relationship Management,
    Wiley: New York.

An R-package for Estimating Semiparametric PH and AFT Mixture Cure Models.

Computes standardized mean differences and confidence intervals for 
    multiple data types based on Yang, D., & Dalton, J. E. (2012) 
    <https://support.sas.com/resources/papers/proceedings12/335-2012.pdf>.

Contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.

An easy to use implementation of routine structural missing data diagnostics with functions to visualize the proportions of missing observations, investigate missing data patterns and conduct various empirical missing data diagnostic tests. Reference: Weberpals J, Raman SR, Shaw PA, Lee H, Hammill BG, Toh S, Connolly JG, Dandreo KJ, Tian F, Liu W, Li J, Hernández-Muñoz JJ, Glynn RJ, Desai RJ. smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies. JAMIA Open. 2024 Jan 31;7(1):ooae008. <doi:10.1093/jamiaopen/ooae008>.