This R package assists breeders in linking data systems with their analytic pipelines, 
    a crucial step in digitizing breeding processes. It supports querying and retrieving 
    phenotypic and genotypic data from systems like 'EBS' <https://ebs.excellenceinbreeding.org/>, 
    'BMS' <https://bmspro.io>, 'BreedBase' <https://breedbase.org>,
    'GIGWA' <https://github.com/SouthGreenPlatform/Gigwa2> (using 'BrAPI' <https://brapi.org> calls),
    , and 'Germinate' <https://germinateplatform.github.io/get-germinate/>. 
    Extra helper functions support environmental data sources, including 
    'TerraClimate' <https://www.climatologylab.org/terraclimate.html> and 'FAO' 
    'HWSDv2' <https://gaez.fao.org/pages/hwsd> soil database.

Programmatically access the 'Quickbase' JSON API <https://developer.quickbase.com>. 
    You supply parameters for an API call, 'qbr' delivers an http request to the 
    API endpoint and returns its response. Outputs follow 'tidyverse' philosophy.

Provides a 'brms'-like interface for fitting Bayesian regression models 
    using 'INLA' (Integrated Nested Laplace Approximations) and 'TMB' (Template Model Builder). 
    The package offers faster model fitting while maintaining familiar 'brms' syntax and 
    output formats. Supports fixed and mixed effects models, multiple probability distributions, 
    conditional effects plots, and posterior predictive checks with summary methods compatible 
    with 'brms'. 'TMB' integration provides fast ordinal regression capabilities. Implements 
    methods adapted from 'emmeans' for marginal means estimation and 'bayestestR' for Bayesian 
    inference assessment. Methods are based on Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x>, 
    Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>, Lenth (2016) <doi:10.18637/jss.v069.i01>, 
    Bürkner (2017) <doi:10.18637/jss.v080.i01>, Makowski et al. (2019) <doi:10.21105/joss.01541>, 
    and Kruschke (2014, ISBN:9780124058880).

An extensive set of functions to perform Qualitative Comparative Analysis:
       crisp sets ('csQCA'), temporal ('tQCA'), multi-value ('mvQCA')
       and fuzzy sets ('fsQCA'), using a GUI - graphical user interface.
       'QCA' is a methodology that bridges the qualitative and quantitative
       divide in social science research. It uses a Boolean minimization
       algorithm, resulting in a minimal causal configuration associated
       with a given phenomenon.