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.