The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.

Derives group sequential clinical trial designs and describes
    their properties. Particular focus on time-to-event, binary, and
    continuous outcomes. Largely based on methods described in
    Jennison, Christopher and Turnbull, Bruce W., 2000,
    "Group Sequential Methods with Applications to Clinical Trials"
    ISBN: 0-8493-0316-8.

The goal of 'gsDesign2' is to enable fixed or group sequential
    design under non-proportional hazards. To enable highly flexible enrollment,
    time-to-event and time-to-dropout assumptions, 'gsDesign2' offers piecewise
    constant enrollment, failure rates, and dropout rates for a stratified
    population. This package includes three methods for designs:
    average hazard ratio, weighted logrank tests in Yung and Liu (2019)
    <doi:10.1111/biom.13196>, and MaxCombo tests.
    Substantial flexibility on top of what is in the 'gsDesign' package
    is intended for selecting boundaries.

Robust Estimation of Multivariate Location and Scatter in the
        Presence of Cellwise and Casewise Contamination and Missing Data.

R-interface to C++ implementation of the rank/score permutation based GSEA test (Subramanian et al 2005 <doi: 10.1073/pnas.0506580102>).

Mark your interesting genes on plot and support more parameters to handle your own gene set enrichment analysis plot.

Provides function to apply "Group sequential enrichment design incorporating subgroup selection" (GSED) method proposed by Magnusson and Turnbull (2013) <doi:10.1002/sim.5738>.

Using an approach based on similarity graph to estimate change-point(s) and the corresponding p-values.  Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.

Genomic selection is a specialized form of marker assisted selection. The package contains functions to select important genetic markers and predict phenotype on the basis of fitted training data using integrated model framework (Guha Majumdar et. al. (2019) <doi:10.1089/cmb.2019.0223>) developed by combining one additive (sparse additive models by Ravikumar et. al. (2009) <doi:10.1111/j.1467-9868.2009.00718.x>) and one non-additive (hsic lasso by Yamada et. al. (2014) <doi:10.1162/NECO_a_00537>) model.