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.

R Interface for the 'H2O' Scalable Machine Learning Platform

R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), Cox Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).

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.

Super Learner Prediction

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

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.

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.

Spatial Modeling on Stream Networks

Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., 2010. .) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included.

Computerized Adaptive Testing for Survey Research

Provides methods of computerized adaptive testing for survey researchers. See Montgomery and Rossiter (2019) . Includes functionality for data fit with the classic item response methods including the latent trait model, Birnbaum`s three parameter model, the graded response, and the generalized partial credit model. Additionally, includes several ability parameter estimation and item selection routines. During item selection, all calculations are done in compiled C++ code.

Rcmdr plugin for quality control course

This package provides an Rcmdr "plug-in" based on the Quality control class Stat 4300

Interface to 'H2O4GPU'

Interface to 'H2O4GPU' < https://github.com/h2oai/h2o4gpu>, a collection of 'GPU' solvers for machine learning algorithms.