Tools to generate random landscape graphs, evaluate species
    occurrence in dynamic landscapes, simulate future landscape occupation and
    evaluate range expansion when new empty patches are available (e.g. as a
    result of climate change). References: Mestre, F., Canovas, F., Pita, R., 
    Mira, A., Beja, P. (2016) <doi:10.1016/j.envsoft.2016.03.007>; Mestre, F., 
    Risk, B., Mira, A., Beja, P., Pita, R. (2017) 
    <doi:10.1016/j.ecolmodel.2017.06.013>; Mestre, F., Pita, R., Mira, A., Beja,
    P. (2020) <doi:10.1186/s12898-019-0273-5>.

The main function performs meta-analysis of genetic association study summary statistics that may be correlated due to cryptic relatedness or other confounders, generalizing inverse variance weighted methods.  The function that estimates the correlation structure is also provided standalone.  Another key innovation, the estimation of the correlation parameter from the median product of correlated standard normal variables, is provided, as well as a complete set of functions for their underlying distribution: density, cumulative, quantile, and random deviates.  Described in Tu and Ochoa (2025) <doi:10.1101/2025.05.10.653279>.

First- and higher-order likelihood inference in
        meta-analysis and meta-regression models.

A metadata structure for clinical data analysis
    and reporting based on Analysis Data Model (ADaM) datasets.
    The package simplifies clinical analysis and reporting tool development
    by defining standardized inputs, outputs, and workflow.
    The package can be used to create analysis and reporting planning grid,
    mock table, and validated analysis and reporting results based on
    consistent inputs.

Analyzes adverse events in clinical trials using the 'metalite'
    data structure. The package simplifies the workflow to create
    production-ready tables, listings, and figures discussed in the
    adverse events analysis chapters of
    "R for Clinical Study Reports and Submission"
    by Zhang et al. (2022) <https://r4csr.org/>.