Calculate empirical subcopula and dependence measures from a given bivariate sample, and Bernstein copula approximations.

A test for the existence of a subgroup with enhanced treatment 
    effect. And, a sample size calculation procedure for the subgroup 
    detection test.

A formula 'sub' is a subformula of 'formula' if all the terms
    on the right hand side of 'sub' are terms of 'formula' and their left hand 
    sides are identical. This package aids in the creation of subformulas.

Produces various measures of expected treatment effect heterogeneity under an assumption of homogeneity across subgroups. Graphical presentations are created to compare these expected differences with the observed differences.

Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>.

Classical methods for combining summary data from genome-wide
    association studies (GWAS) only use marginal genetic effects and power can
    be compromised in the presence of heterogeneity. 'subgxe' is a R package
    that implements p-value assisted subset testing for association (pASTA),
    a method developed by Yu et al. (2019) <doi:10.1159/000496867>. pASTA
    generalizes association analysis based on subsets by incorporating
    gene-environment interactions into the testing procedure.

Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates.  The submax or subgroup-maximum method of Lee et al. (2017) <arXiv:1702.00525> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.