Statistical decision in proteomics data using a hierarchical
    Bayesian model. There are two regression models for describing the 
    mean-variance trend, a gamma regression or a latent gamma mixture
    regression. The regression model is then used as an Empirical Bayes
    estimator for the prior on the variance in a peptide. Further, it assumes
    that each measurement has an uncertainty (increased variance) associated
    with it that is also inferred. Finally, it tries to estimate the posterior
    distribution (by Hamiltonian Monte Carlo) for the differences in means for
    each peptide in the data. Once the posterior is inferred, it integrates the
    tails to estimate the probability of error from which a statistical decision
    can be made.
    See Berg and Popescu for details (<doi:10.1016/j.mcpro.2023.100658>).

Hypothesis tests and sure independence screening (SIS) procedure based on ball statistics, including ball divergence <doi:10.1214/17-AOS1579>, ball covariance <doi:10.1080/01621459.2018.1543600>, and ball correlation <doi:10.1080/01621459.2018.1462709>, are developed to analyze complex data in metric spaces, e.g, shape, directional, compositional and symmetric positive definite matrix data. The ball divergence and ball covariance based distribution-free tests are implemented to detecting distribution difference and association in metric spaces <doi:10.18637/jss.v097.i06>. Furthermore, several generic non-parametric feature selection procedures based on ball correlation, BCor-SIS and all of its variants, are implemented to tackle the challenge in the context of ultra high dimensional data. A fast implementation for large-scale multiple K-sample testing with ball divergence <doi: 10.1002/gepi.22423> is supported, which is particularly helpful for genome-wide association study.

Analysis of gene expression RNA-seq data using Bartlett-Adjusted Likelihood-based LInear model (BALLI). Based on likelihood ratio test, it provides comparisons for effect of one or more variables. See Kyungtaek Park (2018) <doi:10.1101/344929> for more information.