DiagrammeR (>= 1.0.0), data.tree (>= 0.7.5), graphics, stats,
survival (>= 2.38), ggplot2, MASS, flexsurv, Rcpp

Statistical Inference for Box-Cox Based Receiver Operating
Characteristic Curves
c(person("Leonidas", "Bantis", role = c("aut"),
email = "lbantis@kumc.edu"),
person("Benjamin", "Brewer", role = c("cre", "ctb"),
email = "tennisbenj@gmail.com"))
Generation of Box-Cox based ROC curves and several aspects of inferences and hypothesis testing. Can be used when inferences for one biomarker (Bantis LE, Nakas CT, Reiser B. (2018) <doi:10.1002/bimj.201700107>) are of interest or when comparisons of two correlated biomarkers (Bantis LE, Nakas CT, Reiser B. (2021) <doi:10.1002/bimj.202000128>) are of interest. Provides inferences and comparisons around the AUC, the Youden index, the sensitivity at a given specificity level (and vice versa), the optimal operating point of the ROC curve (in the Youden sense), and the Youden based cutoff.
Leonidas Bantis [aut],
Benjamin Brewer [cre, ctb]

c(person("Leonidas", "Bantis", role = c("aut"),
email = "lbantis@kumc.edu"),
person("Benjamin", "Brewer", role = c("cre", "ctb"),
email = "tennisbenj@gmail.com"),
person("Christos", "Nakas", role = c("ctb"),
email = "cnakas@uth.gr"),
person("Benjamin", "Reiser", role = c("ctb"),
email = "reiser@stat.haifa.ac.il"))
Leonidas Bantis [aut],
Benjamin Brewer [cre, ctb],
Christos Nakas [ctb],
Benjamin Reiser [ctb]

Functions for a classification method based on receiver
operating characteristics (ROC). Briefly, features are selected
according to their ranked AUC value in the training set. The
selected features are merged by the mean value to form a
metagene. The samples are ranked by their metagene value and
the metagene threshold that has the highest accuracy in
splitting the training samples is determined. A new sample is
classified by its metagene value relative to the threshold. In
the first place, the package is aimed at two class problems in
gene expression data, but might also apply to other problems.