subsemble: 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) <doi:10.1080/02664763.2013.864263>.

Version: 0.1.0
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
Published: 2022-01-24
DOI: 10.32614/CRAN.package.subsemble
Author: Erin LeDell [cre], Stephanie Sapp [aut], Mark van der Laan [aut]
Maintainer: Erin LeDell <oss at>
License: Apache License (== 2.0)
NeedsCompilation: no
Materials: NEWS
CRAN checks: subsemble results


Reference manual: subsemble.pdf


Package source: subsemble_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): subsemble_0.1.0.tgz, r-oldrel (arm64): subsemble_0.1.0.tgz, r-release (x86_64): subsemble_0.1.0.tgz, r-oldrel (x86_64): subsemble_0.1.0.tgz
Old sources: subsemble archive


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