Estimate ideal efficiencies of aerosol sampling through sample
    lines. Functions were developed consistent with the approach described in 
    Hogue, Mark; Thompson, Martha; Farfan, Eduardo; Hadlock, Dennis, (2014), 
    "Hand Calculations for Transport of Radioactive Aerosols through Sampling 
    Systems" Health Phys 106, 5, S78-S87, <doi:10.1097/HP.0000000000000092>.

Estimates the attributable fraction in different sampling designs
    adjusted for measured confounders using logistic regression (cross-sectional
    and case-control designs), conditional logistic regression (matched case-control
    design), Cox proportional hazard regression (cohort design with time-to-
    event outcome), gamma-frailty model with a Weibull baseline hazard and instrumental variables analysis.
    An exploration of the AF with a genetic exposure can be found in the package 'AFheritability' Dahlqwist E et al. (2019) <doi:10.1007/s00439-019-02006-8>.

This is an implementation of the Generalized Discrimination Score
    (also known as Two Alternatives Forced Choice Score, 2AFC) for various 
    representations of forecasts and verifying observations. The Generalized 
    Discrimination Score is a generic forecast verification framework which 
    can be applied to any of the following verification contexts: dichotomous, 
    polychotomous (ordinal and nominal), continuous, probabilistic, and ensemble.
    A comprehensive description of the Generalized Discrimination Score, including 
    all equations used in this package, is provided by Mason and Weigel (2009) 
    <doi:10.1175/MWR-D-10-05069.1>.

Government Analysis Function recommended colours for use in
    charts on gov.uk to help meet accessibility guidance.

Estimate diagnosis performance (Sensitivity, Specificity, 
    Positive predictive value, Negative predicted value) of a diagnostic test
    where can not measure the golden standard but can estimate it using the 
    attributable fraction.

Convenience functions for analyzing factorial experiments using ANOVA or
         mixed models. aov_ez(), aov_car(), and aov_4() allow specification of
         between, within (i.e., repeated-measures), or mixed (i.e., split-plot) 
         ANOVAs for data in long format (i.e., one observation per row),
         automatically aggregating multiple observations per individual and cell 
         of the design. mixed() fits mixed models using lme4::lmer() and computes 
         p-values for all fixed effects using either Kenward-Roger or Satterthwaite 
         approximation for degrees of freedom (LMM only), parametric bootstrap 
         (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). 
         afex_plot() provides a high-level interface for interaction or one-way 
         plots using ggplot2, combining raw data and model estimates. afex uses 
         type 3 sums of squares as default (imitating commercial statistical software).