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>.

A system to implement the Q-Q boxplot.  It is implemented as an
    extension to 'ggplot2'.  The Q-Q boxplot is an amalgam of the boxplot and the
    Q-Q plot and allows the user to rapidly examine summary statistics and tail
    behavior for multiple distributions in the same pane.  As an extension of
    the 'ggplot2' implementation of the boxplot, possible modifications to the 
    boxplot extend to the Q-Q boxplot.

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>.

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>.