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.

A canonical correlation based framework (SmCCNet) designed for the construction of phenotype-specific multi-omics networks. This framework adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. It offers a streamlined setup process that can be tailored manually or configured automatically, ensuring a flexible and user-friendly experience.

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.

Bindings to 'smcrypto' <https://github.com/zhuobie/smcrypto>: a 'Rust'
    implementation of China's Standards of Encryption Algorithms, which is usually 
    called 'ShangMi(SM)' algorithms. It contains 'SM3' message digest algorithm, 
    'SM2' asymmetric encryption algorithm and 'SM4' symmetric encryption algorithm. 
    Users can do message hash, encrypt/decrypt, sign/verify, key exchange and more.

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) 
    <http://www.lerner.ccf.org/qhs/software/lib/stddiff.pdf>.