Functions for pooling/combining the results (i.e., p-values) from (dependent) hypothesis tests. Included are Fisher's method, Stouffer's method, the inverse chi-square method, the Bonferroni method, Tippett's method, and the binomial test. Each method can be adjusted based on an estimate of the effective number of tests or using empirically derived null distribution using pseudo replicates. For Fisher's, Stouffer's, and the inverse chi-square method, direct generalizations based on multivariate theory are also available (leading to Brown's method, Strube's method, and the generalized inverse chi-square method). An introduction can be found in Cinar and Viechtbauer (2022) <doi:10.18637/jss.v101.i01>.

An easy-to-use tool for working with presence/absence tests
    on 'pooled' or 'grouped' samples. The primary application is for
    estimating prevalence of a marker in a population based on the results
    of tests on pooled specimens.  This sampling method is often employed
    in surveillance of rare conditions in humans or animals (e.g.
    molecular xenomonitoring). The package was initially conceived as an
    R-based alternative to the molecular xenomonitoring software,
    'PoolScreen' <https://sites.uab.edu/statgenetics/software/>. However,
    it goes further, allowing for estimates of prevalence to be adjusted
    for hierarchical sampling frames, and perform flexible mixed-effect
    regression analyses (McLure et al. Environmental Modelling and
    Software.  <DOI:10.1016/j.envsoft.2021.105158>). The package is
    currently in early stages, however more features are planned or in the
    works: e.g. adjustments for imperfect test specificity/sensitivity,
    functions for helping with optimal experimental design, and functions
    for spatial modelling.

Gene-based association tests using the actual impurity reduction (AIR) variable importance. The function aggregates AIR importance measures from a group of SNPs or probes and outputs a p-value for each gene. The procedures builds upon the method described in <doi:10.1093/Bioinformatics/Bty373> and will be published soon.