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.

Provides access to granular sub-national income data from the
    MCC-PIK Database Of Sub-national Economic Output (DOSE). The package
    downloads and processes the data from its open repository on 'Zenodo'
    (<https://zenodo.org/records/13773040>). Functions are provided to
    fetch data at multiple geographic levels, match coordinates to
    administrative regions, and access associated geometries.

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. (2018) <doi:10.1111/biom.12884> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.

Complementary indexes calculation to the Outlying Mean Index analysis to explore niche shift of a community and biological constraint within an Euclidean space, with graphical displays. For details see Karasiewicz 'et al.' (2017) <doi:10.7717/peerj.3364>.

The subplex algorithm for unconstrained optimization, developed by Tom Rowan.

Estimation of copula using ranks and subsampling. The main feature of this method is that simulation studies show a low sensitivity to dimension, on realistic cases.

Balancing computational and statistical efficiency, subsampling techniques offer
    a practical solution for handling large-scale data analysis. Subsampling methods
    enhance statistical modeling for massive datasets by efficiently drawing
    representative subsamples from full dataset based on tailored sampling probabilities. These
    probabilities are optimized for specific goals, such as minimizing the variance
    of coefficient estimates or reducing prediction error.