Implements the Quantile Autoregressive Distributed Lag (QARDL)
    model of Cho, Kim and Shin (2015) <doi:10.1016/j.jeconom.2015.01.003>.
    Estimates quantile-specific long-run (beta), short-run autoregressive (phi),
    and impact (gamma) parameters. Features include BIC-based automatic lag
    selection, Error Correction Model (ECM) parameterization, Wald tests for
    parameter constancy across quantiles, rolling/recursive QARDL estimation,
    Monte Carlo simulation, and publication-ready output tables.

Provides prediction intervals for classical homoscedastic
    autoregressive models (AR(p)) and quantile autoregressive models
    (QAR(p)). The package implements percentile-based and predictive-root-based
    bootstrap procedures for constructing multi-step-ahead prediction
    intervals. For more details, see Novo and Sanchez-Sellero (2025)
    <doi:10.48550/arXiv.2512.22018>.

Fuel economy, size, performance, and price data for cars in Qatar in 2025. Mirrors many of the columns in mtcars, but uses (1) non-US-centric makes and models, (2) 2025 prices, and (3) metric measurements, making it more appropriate for use as an example dataset outside the United States. For more details see Musgrave (2025) <doi:10.1080/15512169.2025.2572320>.

Create quantile binned and conditional plots for Exploratory Data
  Analysis. The package provides several plotting functions that are all based
  on quantile binning. The plots are created with 'ggplot2' and 'patchwork' 
  and can be further adjusted.

Implements the Bayesian quantile regression model for binary longitudinal data 
             (QBLD) developed in Rahman and Vossmeyer (2019) <DOI:10.1108/S0731-90532019000040B009>.
             The model handles both fixed and random effects and implements both a blocked
             and an unblocked Gibbs sampler for posterior inference.

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.