Inference of transmission tree from a dated phylogeny. 
    Includes methods to simulate and analyse outbreaks.
    The methodology is described in
    Didelot et al. (2014) <doi:10.1093/molbev/msu121>,
    Didelot et al. (2017) <doi:10.1093/molbev/msw275>.

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.

Improves the predictive performance of ridge and lasso regression exploiting one or more sources of prior information on the importance and direction of effects (Rauschenberger and others 2023, <doi:10.1093/bioinformatics/btad680>). For running the vignette, install 'fwelnet' from 'GitHub' <https://github.com/kjytay/fwelnet>.

Tensor Gaussian graphical models (GGMs) have important applications in numerous areas, which can interpret conditional independence structures within tensor data. Yet, the available tensor data in one single study is often limited due to high acquisition costs. 
             Although relevant studies can provide additional data, it remains an open question how to pool such heterogeneous data. This package implements a transfer learning framework for tensor GGMs, which takes full advantage of informative auxiliary domains even when non-informative auxiliary domains are present, benefiting from the carefully designed data-adaptive weights. 
             Reference: 
             Ren, M., Zhen Y., and Wang J. (2022). "Transfer learning for tensor graphical models" <arXiv:2211.09391>.