Cross-Validated Area Under the ROC Curve Confidence Intervals

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.

Super Learner Prediction

Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.

Evaluation Metrics for Machine Learning

An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.

R Interface for H2O Sparkling Water

An extension package for 'sparklyr' that provides an R interface to H2O Sparkling Water machine learning library (see < https://github.com/h2oai/sparkling-water> for more information).

An Ensemble Method for Combining Subset-Specific Algorithm Fits

Subsemble is a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a unique form of V-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble.

Retrieve data sets from the HealthData.gov data API

An R interface for the HealthData.gov data API. For each data resource, you can filter results (server-side) to select subsets of data.