Utilities to make your clinical collaborations easier if not
          fun. It contains functions for designing studies such as Simon
          2-stage and group sequential designs and for data analysis such
          as Jonckheere-Terpstra test and estimating survival quantiles.

A collection of tools to easily analyze clinical data, including functions for correlation analysis, 
  and statistical testing. The package facilitates the integration of clinical metadata with other omics layers, 
  enabling exploration of quantitative variables. It also includes the utility for frequency matching samples across 
  a dataset based on patient variables.

Post-hoc fairness auditing toolkit for clinical prediction
    models. Unlike in-processing approaches that modify model training,
    this package evaluates existing models by computing group-wise
    fairness metrics (demographic parity, equalized odds, predictive
    parity, calibration disparity), visualizing disparities across
    protected attributes, and performing threshold-based mitigation.
    Supports intersectional analysis across multiple attributes and
    generates audit reports useful for fairness-oriented auditing
    in clinical AI settings.
    Methods described in Obermeyer et al. (2019)
    <doi:10.1126/science.aax2342> and Hardt, Price, and Srebro (2016)
    <doi:10.48550/arXiv.1610.02413>.

Provides an interface to the 'ClinicalOmicsDB' API, allowing for easy data downloading and importing. 'ClinicalOmicsDB' is a database of clinical and 'omics' data from cancer patients. The database is accessible at <http://trials.linkedomics.org>.

A clinical significance analysis can be used to determine if an 
    intervention has a meaningful or practical effect for patients. You provide 
    a tidy data set plus a few more metrics and this package will take care of 
    it to make your results publication ready. Accompanying package to 
    Claus et al. <doi:10.18637/jss.v111.i01>.

Provides estimates of several summary measures for clinical trials including
    the average hazard ratio, the weighted average hazard ratio, the restricted 
    superiority probability ratio, the restricted mean survival difference and 
    the ratio of restricted mean times lost, based on the short-term and 
    long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) 
    which accommodates various non-proportional hazards scenarios. The inference
    procedures and the asymptotic results for the summary measures are discussed 
    in Yang (2018, <doi:10.1002/sim.7676>).