Description: Tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators.  The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.  One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods.  The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.

Title: Cross-Validated Covariance Matrix Estimation
person("Philippe", "Boileau", email = "philippe_boileau@berkeley.edu",
person("Nima", "Hejazi", email = "nh@nimahejazi.org",
person("Brian", "Collica", email = "bcollica@berkeley.edu ",
person("Jamarcus", "Liu", email = "yanwei.liu@berkeley.edu ",
person("Mark", "van der Laan", email = "laan@berkeley.edu",
person("Sandrine", "Dudoit", email = "sandrine@stat.berkeley.edu",
Description: An efficient cross-validated approach for covariance matrix
estimation, particularly useful in high-dimensional settings. This
method relies upon the theory of loss-based estimator selection to
identify the optimal estimator of the covariance matrix from among a
Imports: matrixStats, Matrix, stats, methods, origami, MASS, coop,
Rdpack, rlang, dplyr, stringr, purrr, tibble, assertthat,
RSpectra, future, future.apply, ggplot2, ggpubr, RColorBrewer
Author: Philippe Boileau [aut, cre, cph]
Mark van der Laan [ctb, ths] (<https://orcid.org/0000-0003-1432-5511>),
Sandrine Dudoit [ctb, ths] (<https://orcid.org/0000-0002-6069-8629>)

Imports: matrixStats, Matrix, stats, methods, origami, coop, Rdpack,
rlang, dplyr, stringr, purrr, tibble, assertthat, RSpectra,
Suggests: future, future.apply, MASS, testthat, knitr, rmarkdown, covr,

Title: Cross-Validated Predictions from GEE
Description: Calculates predictions from generalized estimating equations and internally cross-validates them using the logarithmic, quadratic and spherical proper scoring rules; Kung-Yee Liang and Scott L. Zeger (1986) <doi:10.1093/biomet/73.1.13>.
Suggests: geepack, lattice, knitr, rmarkdown, pkgdown