Contains functionality for performing the following methods of p-value aggregation: Fisher's method [Fisher, RA (1932, ISBN: 9780028447308)], the Lancaster method (weighted Fisher's method) [Lancaster, HO (1961, <doi:10.1111/j.1467-842X.1961.tb00058.x>)], and Sidak correction [Sidak, Z (1967, <doi:10.1080/01621459.1967.10482935>)].  Please cite Yi et al., the manuscript corresponding to this package [Yi, L et al., (2017), <doi:10.1101/190199>].

Nonparametric data-driven approach to discovering heterogeneous subgroups in a selection-on-observables framework. 
    'aggTrees' allows researchers to assess whether there exists relevant heterogeneity in treatment effects by generating a sequence of optimal groupings, 
    one for each level of granularity. For each grouping, we obtain point estimation and inference about the group average treatment effects. 
    Please reference the use as Di Francesco (2022) <doi:10.2139/ssrn.4304256>.

Provides several methods for aggregating probabilistic forecasts. You have a group of
    people who have made probabilistic forecasts for the same event. You want to take advantage of
    the "wisdom of the crowd" and combine these forecasts in some sensible way. This package
    provides implementations of several strategies, including geometric mean of odds, an extremized
    aggregate (Neyman, Roughgarden (2021) <doi:10.1145/3490486.3538243>), and "high-density trimmed
    mean" (Powell et al. (2022) <doi:10.1037/dec0000191>).

Fast computation of A (pedigree), G (genomic-base), and H (A corrected
    by G) relationship matrices for diploid and autopolyploid species. Several methods
    are implemented considering additive and non-additive models.

Adaptive Gauss Hermite Quadrature for Bayesian inference.
    The AGHQ method for normalizing posterior distributions
    and making Bayesian inferences based on them. Functions are provided for doing
    quadrature and marginal Laplace approximations, and summary methods are provided
    for making inferences based on the results. 
    See Stringer (2021). "Implementing Adaptive Quadrature for Bayesian Inference: 
    the aghq Package" <arXiv:2101.04468>.