Description: Package to calculate the Generalized Discrimination Score
(also known as Two Alternatives Forced Choice Score (2AFC)

Authors@R: c(person("Andreas", "Weigel", email="", role = "aut"),
person("Jonas", "Bhend", email="jonas.bhend@meteoswiss.ch", role = c('cre', 'ctb')))
Description: 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)

Title: Diagnosis Performance Using Attributable Fraction
Description: 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
Suggests: knitr, rmarkdown, ggplot2, DescTools, kableExtra, coda,
rjags, ggmcmc, spelling, testthat (>= 3.0.0)
Author: John J. Aponte [aut, cre] (<https://orcid.org/0000-0002-3014-3673>),
Maintainer: John J. Aponte <john.j.aponte@gmail.com>

Title: Analysis of Factorial Experiments
Description: Provides convenience functions for analyzing factorial experiments
using ANOVA or mixed-models. ez.glm() and aov.car() allow convenient
calculation of between, within (i.e., repeated-measures), or mixed
between-within (i.e., split-plot) ANOVAs for data in the long format (i.e.,
one observation per row) wrapping car::Anova() (aggregating more then one
observation per individual and cell of the design), per default returning a
print ready ANOVA table. Function mixed() fits a mixed model using
lme4::lmer() and computes p-values for all effects in the model using
either the Kenward-Roger approximation of degrees of freedom (LMM only),
parametric bootstrap (LMMs and GLMMs) or likelihood ratio tests (LMMs and
GLMMs). afex uses type 3 sums of squares as default (imitating commercial
statistical software). Furthermore, compare.2.vectors() conveniently
compares two vectors using a variety of tests.
role=c("ctb")), person(given="Ulf", family="Mertens", role=c("ctb")) )