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>.

A computing tool is developed to automated identify somatic mutation-driven immune cells. The operation modes including: i) inferring the relative abundance matrix of tumor-infiltrating immune cells and integrating it with a particular gene mutation status, ii) detecting differential immune cells with respect to the gene mutation status and converting the abundance matrix of significant differential immune cell into two binary matrices (one for up-regulated and one for down-regulated), iii) identifying somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and iv) visualization of immune cell abundance of samples in different mutation status..