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.

Provides systematic, dependency-aware exploration of
    group sequential designs created with 'gsDesign'.
    Supports reproducible grid and random search over user-defined
    candidate sets, parallel evaluation via the 'future' framework,
    standardized metric extraction, and auditable reporting for
    design-space evaluation and trade-off analysis.
    Methods for group sequential design are described in
    Anderson (2025) <doi:10.32614/CRAN.package.gsDesign>.
    The 'future' framework for parallel processing is described in
    Bengtsson (2021) <doi:10.32614/RJ-2021-048>.

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

Methods to construct and power group sequential clinical trial 
  designs for outcomes at multiple times. Outcomes at earlier times provide
  information on the final (primary) outcome. A range of recruitment and 
  correlation models are available as are methods to simulate data in order to 
  explore design operating characteristics. For more details see Parsons (2024)
  <doi:10.1186/s12874-024-02174-w>.