A set of vectorised functions to calculate medical equations used in transplantation, 
    focused mainly on transplantation of abdominal organs. These functions include donor and recipient
    risk indices as used by NHS Blood & Transplant, OPTN/UNOS and Eurotransplant, tools for 
    quantifying HLA mismatches, functions for calculating estimated glomerular filtration rate (eGFR), 
    a function to calculate the APRI (AST to platelet ratio) score used in initial screening of suitability to receive a 
    transplant from a hepatitis C seropositive donor and some biochemical unit converter functions. 
    All functions are designed to work with either US or international units.
    References for the equations are provided in the vignettes and function documentation.

To visualize the gene structure with multiple isoforms better, I developed this package to draw different transcript structures easily.

Solve optimal transport problems. Compute Wasserstein distances (a.k.a. Kantorovitch, Fortet--Mourier, Mallows, Earth Mover's, or minimal L_p distances), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, (weighted) point patterns, and mass vectors.

Doubly-robust, non-parametric estimators for the transported average treatment effect from 
  Rudolph, Williams, Stuart, and Diaz (2023) <doi:10.48550/arXiv.2304.00117> and the 
  intent-to-treatment average treatment effect from Rudolph and van der Laan (2017)
  <doi:10.1111/rssb.12213>. Estimators are fit using cross-fitting and nuisance parameters are 
  estimated using the Super Learner algorithm.

A tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. 'TransProR' is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include 'limma' (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), 'edgeR' (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), 'DESeq2' (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.