An Ensemble Method for Combining Subset-Specific Algorithm Fits R package subsemble version 0.1.0 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) <doi:10.1080/02664763.2013.864263>. Software Depends: R (>= 2.14.0), SuperLearner Suggests: arm, caret, class, cvAUC, e1071, earth, gam, gbm, glmnet, Hmisc, ipred, lattice, LogicReg, MASS, mda, mlbench, nnet, parallel, party, polspline, quadprog, randomForest, rpart, SIS, spls, stepPlr Erin LeDell <oss@ledell.org> Comprehensive R Archive Network (CRAN) Erin LeDell [cre], Stephanie Sapp [aut], Mark van der Laan [aut] Apache License (== 2.0) 2022-01-24 application/tgz https://CRAN.R-project.org/package=subsemble doi:10.32614/CRAN.package.subsemble