Title: Various Methods to Estimate the AUC
Description: Estimate the AUC using a variety of methods as follows:
(1) frequentist nonparametric methods based on the Mann-Whitney statistic or kernel methods.
(2) frequentist parametric methods using the likelihood ratio test based on higher-order
asymptotic results, the signed log-likelihood ratio test, the Wald test,
or the approximate ''t'' solution to the Behrens-Fisher problem.

Author: Dai Feng [aut, cre], Damjan Manevski [auc], Maja Pohar Perme [auc]

Author: Youyi Fong <youyifong@gmail.com>, Krisztian Sebestyen <ksebestyen@gmail.com>, Shuxin Yin <yinshuxin05@gmail.com>, Ying Huang <yhuang124@gmail.com>
Description: Implements methods for identifying linear and nonlinear marker combinations that maximizes the Area Under the AUC Curve (AUC).

Authors@R: c(person("Youyi", "Fong", role = "cre", email = "youyifong@gmail.com"), person("Krisztian", "Sebestyen", role = "aut", email = "ksebestyen@gmail.com"), person("Shuxin", "Yin", role = "aut", email = "yinshuxin05@gmail.com"), person("Ying", "Huang", role = "aut", email = "yhuang124@gmail.com"), person("Alexandros", "Karatzoglou", role = "ctb"), person("Alex", "Smola", role = "ctb"), person("Kurt", "Hornik", role = "ctb"))

Authors@R: person("Josh", "Gardner", email = "jpgard@umich.edu", role = c("aut", "cre"))
Description: Performs statistical testing to compare predictive
models based on multiple observations of the A' statistic (also known as
Area Under the Receiver Operating Characteristic Curve, or AUC).
Specifically, it implements a testing method based on the equivalence
between the A' statistic and the Wilcoxon statistic.
For more information, see Hanley and McNeil (1982) <doi:10.1148/radiology.143.1.7063747>.
Packaged: 2017-11-12 18:53:55 UTC; joshgardner

Title: Estimate First-Price Auction Model
Authors@R: c( person("Alex",     "MacKay",       email = "amackay@hbs.edu",            role = c("aut", "cre")),
person("Bob",      "Freeman",      email = "robert_freeman@harvard.edu", role = c("aut")),
person("Paul",     "Jonak",        email = "pjonak@hbs.edu",             role = c("aut")),
person("Victoria", "Prince",       email = "vliublinska@hbs.edu",        role = c("aut")),
person("Ista",     "Zahn",         email = "izahn@hbs.edu",              role = c("aut"))
Description: Estimates a first-price auction model with conditionally independent
private values as described in MacKay (2020) <doi:10.2139/ssrn.3096534>. The
model allows for unobserved heterogeneity that is common to all bidders in
Imports: stats, parallel, numDeriv (>= 2016.8-1)