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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 |
| Reference manual: | SuperLearner.pdf |
| Vignettes: |
Guide to SuperLearner |
| Package source: | SuperLearner_2.0-29.tar.gz |
| Windows binaries: | r-devel: SuperLearner_2.0-29.zip, r-release: SuperLearner_2.0-29.zip, r-oldrel: SuperLearner_2.0-29.zip |
| macOS binaries: | r-release (arm64): SuperLearner_2.0-29.tgz, r-oldrel (arm64): SuperLearner_2.0-29.tgz, r-release (x86_64): SuperLearner_2.0-29.tgz, r-oldrel (x86_64): SuperLearner_2.0-29.tgz |
| Old sources: | SuperLearner archive |
| Reverse depends: | ctmle, EScvtmle, polle, subsemble, survML, tmle |
| Reverse imports: | AIPW, amp, CausalGPS, CausalMetaR, causalweight, CIMTx, CRE, crossurr, DRDRtest, drpop, drtmle, evalITR, flevr, GPCERF, lmtp, nlpred, PSweight, Ricrt, RISCA, RobinCar, superMICE, tehtuner, tidyhte, vaccine, vimp |
| Reverse suggests: | biotmle, gKRLS, hal9001, ltmle, medflex, nestedcv, riskRegression, targeted, vglmer, WeightIt |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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