Generalized Turnbull's estimator proposed by Dehghan and Duchesne
    (2011).

Implementation of the GTE (Group Technical Effects) model for single-cell data. GTE is a quantitative metric to assess batch effects for individual genes in single-cell data. For a single-cell dataset, the user can calculate the GTE value for individual features (such as genes), and then identify the highly batch-sensitive features. Removing these highly batch-sensitive features results in datasets with low batch effects.

Four graph-based tests are provided for testing whether two samples are from the same distribution.  It works for both continuous data and discrete data.

New multi-sample tests for testing whether multiple samples are from the same distribution. They work well particularly for high-dimensional data.
    Song, H. and Chen, H. (2022) 
    <arXiv:2205.13787>.

Implements three nonparametric two-sample tests for multivariate paired data
    and pair matching. Methods are described in the associated preprint:
    <doi:10.48550/arXiv.2007.01497>.

A convenient R interface to the Genotype-Tissue Expression (GTEx) 
    Portal API. The GTEx project is a comprehensive public resource for studying 
    tissue-specific gene expression and regulation in human tissues. Through 
    systematic analysis of RNA sequencing data from 54 non-diseased tissue sites 
    across nearly 1000 individuals, GTEx provides crucial insights into the 
    relationship between genetic variation and gene expression. This data is 
    accessible through the GTEx Portal API enabling programmatic access to human 
    gene expression data. For more information on the API, see 
    <https://gtexportal.org/api/v2/redoc>.

Provides additional functions for creating beautiful tables
    with 'gt'. The functions are generally wrappers around boilerplate or
    adding opinionated niche capabilities and helpers functions.

Generalizes application of gray-level co-occurrence matrix
    (GLCM) metrics to objects outside of images. The current focus is to
    apply GLCM metrics to the study of biological networks and fitness
    landscapes that are used in studying evolutionary medicine and
    biology, particularly the evolution of cancer resistance. The package was
    developed as part of the author's publication in Physics in Medicine and Biology
    Barker-Clarke et al. (2023) <doi:10.1088/1361-6560/ace305>. 
    A general reference to learn more about mathematical oncology can be found at
    Rockne et al. (2019) <doi:10.1088/1478-3975/ab1a09>.