The 'agghoo' procedure is an alternative to usual cross-validation.
    Instead of choosing the best model trained on V subsamples, it determines
    a winner model for each subsample, and then aggregates the V outputs.
    For the details, see "Aggregated hold-out" by Guillaume Maillard,
    Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890>
    published in Journal of Machine Learning Research 22(20):1--55.

The use of structured elicitation to inform decision making has grown dramatically in recent decades, however, judgements from multiple experts must be aggregated into a single estimate. Empirical evidence suggests that mathematical aggregation provides more reliable estimates than enforcing behavioural consensus on group estimates. 'aggreCAT' provides state-of-the-art mathematical aggregation methods for elicitation data including those defined in Hanea, A. et al. (2021) <doi:10.1371/journal.pone.0256919>. The package also provides functions to visualise and evaluate the performance of your aggregated estimates on validation data.

Convenience functions for aggregating a data frame or data table.
    Currently mean, sum and variance are supported. For Date variables, the recency
    and duration are supported. There is also support for dummy variables in
    predictive contexts. Code has been completely re-written in data.table for computational speed.

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>.