Supports risk assessors in performing the entry step of the
    quantitative Pest Risk Assessment. It allows the estimation of the
    amount of a plant pest entering a risk assessment area (in terms of
    founder populations) through the calculation of the imported
    commodities that could be potential pathways of pest entry, and the
    development of a pathway model. Two 'Shiny' apps based on the
    functionalities of the package are included, that simplify the process
    of assessing the risk of entry of plant pests. The approach is based
    on the work of the European Food Safety Authority (EFSA PLH Panel et
    al., 2018) <doi:10.2903/j.efsa.2018.5350>.

Provides functionality for creating Quantile-Quantile (QQ) and Probability-Probability (PP) plots with simultaneous 
    testing bands to asses significance of sample deviation from a reference distribution <doi:10.18637/jss.v106.i10>.

Implements Quantile-on-Quantile Kernel-Based Regularized
    Least Squares (QQKRLS) as in Adebayo, Ozkan and Eweade (2024)
    <doi:10.1016/j.jclepro.2024.140832>. Combines Kernel-Based
    Regularized Least Squares (KRLS) of Hainmueller and Hazlett (2014)
    <doi:10.1093/pan/mpt019> with the Quantile-on-Quantile regression
    of Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>: for
    each quantile theta of the independent variable the response is fit
    by KRLS on the corresponding sub-sample and the tau-quantile of the
    resulting pointwise marginal effects yields beta(theta, tau).
    Standard errors come from a paired bootstrap. Visualisations use
    the 'MATLAB' 'Parula' colour map by default.

Create Q-Q and manhattan plots for GWAS data from PLINK results.

Extensions of 'ggplot2' Q-Q plot functionalities.

A collection of routines for finding reference limits using, where 
    appropriate, QQ methodology.  All use a data vector X of cases from the 
    reference population. The default is to get the central 95% reference range 
    of the population, namely the 2.5 and 97.5 percentile, with optional 
    adjustment of the range.  Along with the reference limits, we want 
    confidence intervals which, for historical reasons, are typically at 90% 
    confidence.  A full analysis provides six numbers:
      – the upper and the lower reference limits, and 
      - each of their confidence intervals.
    For application details, see Hawkins and Esquivel (2024) 
    <doi:10.1093/jalm/jfad109>.