An R package to perform LPUE standardization and stock
assessment of the English Channel cuttlefish stock using a
two-stage biomass model
This package can be used to standardize abundance indices using the delta-GLM method and to model the English Channel cuttlefish stock using a two-stage biomass model

Cross-Validated Area Under the ROC Curve Confidence Intervals
Erin LeDell, Maya Petersen, Mark van der Laan
This package contains various 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
function, cvAUC, which computes cross-validated AUC, is a
wrapper for functions from the ROCR package.

This package contains various 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.

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.