SuperLearner: Super Learner Prediction

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

Version: 2.0-29
Depends: R (≥ 2.14.0), nnls, gam (≥ 1.15)
Imports: cvAUC, methods
Suggests: arm, bartMachine, biglasso, bigmemory, caret, class, devtools, e1071, earth, gbm, genefilter, ggplot2, glmnet, ipred, KernelKnn, kernlab, knitr, lattice, LogicReg, MASS, mlbench, nloptr, nnet, party, polspline, prettydoc, quadprog, randomForest, ranger, RhpcBLASctl, ROCR, rmarkdown, rpart, SIS, speedglm, spls, sva, testthat, xgboost (≥ 0.6)
Published: 2024-02-20
Author: Eric Polley [aut, cre], Erin LeDell [aut], Chris Kennedy [aut], Sam Lendle [ctb], Mark van der Laan [aut, ths]
Maintainer: Eric Polley <epolley at uchicago.edu>
License: GPL-3
URL: https://github.com/ecpolley/SuperLearner
NeedsCompilation: no
Materials: NEWS ChangeLog
In views: Bayesian, MachineLearning
CRAN checks: SuperLearner results

Documentation:

Reference manual: SuperLearner.pdf
Vignettes: Guide to SuperLearner

Downloads:

Package source: SuperLearner_2.0-29.tar.gz
Windows binaries: r-devel: SuperLearner_2.0-28.1.zip, r-release: SuperLearner_2.0-28.1.zip, r-oldrel: SuperLearner_2.0-28.1.zip
macOS binaries: r-release (arm64): SuperLearner_2.0-28.1.tgz, r-oldrel (arm64): SuperLearner_2.0-29.tgz, r-release (x86_64): SuperLearner_2.0-29.tgz
Old sources: SuperLearner archive

Reverse dependencies:

Reverse depends: ctmle, EScvtmle, polle, subsemble, survML, tmle
Reverse imports: AIPW, amp, CausalGPS, CausalMetaR, causalweight, CIMTx, concrete, CRE, crossurr, DRDRtest, drpop, drtmle, evalITR, flevr, GPCERF, lmtp, nlpred, PSweight, Ricrt, superMICE, tehtuner, tidyhte, vaccine, vimp
Reverse suggests: biotmle, gKRLS, hal9001, ltmle, medflex, nestedcv, riskRegression, stacks, targeted, vglmer, WeightIt

Linking:

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