Linear and Non-Linear AUC for Discounting Data
Area under the curve (AUC; Myerson et al., 2001) <doi:10.1901/jeab.2001.76-235>
is a popular measure used in discounting research. Although the calculation of
AUC is standardized, there are differences in AUC based on some assumptions.
For example, Myerson et al. (2001) <doi:10.1901/jeab.2001.76-235>
assumed that (with delay discounting data) a researcher would impute an
indifference point at zero delay equal to the value of the larger, later outcome.
However, this practice is not clearly followed. This imputed zero-delay indifference
point plays an important role in log and ordinal versions of AUC.
Ordinal and log versions of AUC are described by Borges et al. (2016)<doi:10.1002/jeab.219>.
The package can calculate all three versions of AUC [and includes a new version: IHS(AUC)],
impute indifference points when x = 0, calculate ordinal AUC in the case of Halton
sampling of x-values, and account for probability discounting AUC.
person(given = "Jonathan E.",
family = "Friedel",
email = "jfriedel@georgiasouthern.edu",
comment = c(ORCID = "https://orcid.org/0000-0002-1516-330X"),
role = c("aut","cre"))
Jonathan E. Friedel [aut, cre]
(<https://orcid.org/0000-0002-1516-330X>)

Provides data transformations, estimation utilities,
predictive evaluation measures and simulation functions for discrete time
survival analysis.
functional, Matrix, matrixcalc, mvtnorm, numDeriv, caret, mgcv

Ecdat, pec, survival, randomForest, nnet, VGAM

Thomas Welchowski <welchow@imbie.meb.uni-bonn.de> and Matthias Schmid <matthias.schmid@imbie.uni-bonn.de>
Matrix, matrixcalc, numDeriv, caret, Ecdat, pec, survival,
randomForest, nnet, VGAM

Thomas Welchowski <welchow@imbie.meb.uni-bonn.de> and Moritz Berger <moritz.berger@imbie.uni-bonn.de> and David Koehler <koehler@imbie.uni-bonn.de> and Matthias Schmid <matthias.schmid@imbie.uni-bonn.de>
functional, mvtnorm, mgcv, data.table, Rdpack, VGAM, geepack,
rpart, ranger, mvnfast
Matrix, matrixcalc, numDeriv, caret, Ecdat, pec, survival,
nnet