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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.
Cross-Validated Area Under the ROC Curve Confidence Intervals
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.
An Ensemble Method for Combining Subset-Specific Algorithm Fits
The Subsemble algorithm 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 k-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble. The paper, "Subsemble: An ensemble method for combining subset-specific algorithm fits" is authored by Stephanie Sapp, Mark J. van der Laan & John Canny (2014)
Super Learner Prediction
Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.
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), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).
Interface to 'H2O4GPU'
Interface to 'H2O4GPU' < https://github.com/h2oai/h2o4gpu>, a collection of 'GPU' solvers for machine learning algorithms.
Computerized Adaptive Testing for Survey Research
Provides methods of computerized adaptive testing for survey researchers. See Montgomery and Rossiter (2020)
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).
Scaffolding Interfaces to Packages in Other Programming Languages
Comprehensive set of tools for scaffolding R interfaces to modules, classes, functions, and documentations written in other programming languages, such as 'Python'.
Combining Subset MCMC Samples to Estimate a Posterior Density
See Miroshnikov and Conlon (2014)