NEWS
SuperLearner 2.0-40-9000
Date: 2025-12-14
SuperLearner 2.0-40 (2025-12-21)
Date: 2025-12-14
- Updated SL.xgboost to support the new version (3.1) of xgboost
SuperLearner 2.0-29 (2024-02-20)
Date: 2024-02-06
- Added n.cores argument to SL.gbm
- fixed formula warning in SL.loess. Note the loess() function is limited to 1-4 features
- fixed version checks to be character instead of numeric
- removes SL.extraTrees (no longer on CRAN, available at SuperLearnerExtra)
SuperLearner 2.0-28 (2021-05-10)
Date: 2021-04-13
- Updated maintainer email
- SL.gam added back, but now importing gam to avoid CRAN NOTE on usage or require()
SuperLearner 2.0-26 (2019-12-10)
Date: 2019-08-12
- Added obsWeights and id arguments to the test-glmnet.R file, line 24
SuperLearner 2.0-25 (2019-08-09)
Date: 2019-08-05
- Updated listWrappers() to allow search results without SuperLearner loaded
- Fixed error in 'predict.SL.ksvm' and 'predict.SL.glmnet' when newdata is a single row (added drop = FALSE)
- Added control option to 'SuperLearner' to save the internal cross-validation algorithm fits as a list. default is FALSE.
- Removed dbarts from Suggests and the associated wrapper over to SuperLearnerExtra. dbarts no longer available on CRAN
- added prettydoc as a Depends for the vignette
- changed VignetteBuilder to rmarkdown
- Removed the SuperLearnerPresent.Rnw file, was a shortcut to create the SuperLearnerR vignette
- Changed .SL.require to use 'requireNamespace()' instead of 'require()'
- added '.SL.require("bibmemory")' to 'SL.biglasso.R'
- allowed SL.gam to use s() instead of gam::s() and 'require' instead of 'requireNamespace'
SuperLearner 2.0-24 (2018-08-11)
Date: 2018-07-10
- remove multicore test in randomForest test. Was generating warning note on CRAN devel
SuperLearner 2.0-23 (2018-03-12)
Date: 2018-03-09
- fixed transformation of outcome in SL.dbarts for binomial family
- SampleSplitSuperLearner(): support validation sample size of 1 when observation's row number is passed in via 'split'.
- Fixed case where single-column X in combination with more than one screening algorithm causes failure in SuperLearner(), snowSuperLearner(), mcSuperLearner(), SampleSplitSuperLearner().
- methods CC.* modified to handle duplicated columns better (PR #106)
- Updated S3 class name for gam::gam() to be Gam
SuperLearner 2.0-22 (2017-07-18)
Date: 2017-07-07
- Added model.matrix to SL.xgboost
- Fixed innerCvControl in CV.SuperLearner to allow multiple parameters. It must now be a list of lists.
- create.Learner(): support character arguments.
- Glmnet: support alternative loss functions; when predicting automatically add any missing covariates and remove covariates not in the original data.
- Added SL.kernelKnn
- Added SL.ksvm
- Added SL.ranger
- Added vignette: "Guide to SuperLearner"
- Added SL.biglasso
- Added SL.lm, SL.speedlm, and SL.speedglm
- Added SL.lda and SL.qda
- Added SL.dbarts for C++-based bayesian additive regression trees.
- SL.lm and SL.glm now have a model argument, defaulting to TRUE (matching glm and lm), but can be changed to FALSE to conserve memory. Both wrappers also explicitly convert X matrix to a data frame.
- Added SL.extraTrees for extremely randomized trees, a random forest variant.
- Fixes prediction when a learner fails for methods: NNLS, NNloglik, CC_nloglik, and AUC. NNLS2 and CC_LS still have this bug. This fix required that an additional optional argument "errorsInLibrary" be passed to methods. This argument is a vector set to TRUE for learners that failed during model fitting.
SuperLearner 2.0-21 (2016-12-01)
Date: 2016-10-03
- Add validRows option for CV.SuperLearner. Can now pass a cvControl for the outer CV and a list of cvControls, one for each cross-validation folds SuperLearner calls. default number of folds in CV.SuperLearner is now 10, matching the default with cvControl. If the user specifies both V and number of folds in cvControl(), an error message is returned.
SuperLearner 2.0-20
Date: 2016-08-09
- Added shrinkage parameter to SL.gbm
- fixed mtry default in SL.randomForest
- in CV.SuperLearner, fixed order for checking parallel options and folds argument in parLapply (thanks Chris Kennedy)
- updated method.AUC to change defaults on the optimization and add warnings for non-convergence
- Added wrapper for xgboost (thanks Chris Kennedy)
- Added wrapper for bartMachine (thanks Chris Kennedy)
- Added travis.ci checks
- Added environments for SuperLearner() and CV.SuperLearner() wrappers search path (includes SL.*, screen.*, and method.* wrappers)
- Added binary outcomes for SL.cforest
SuperLearner 2.0-19 (2016-02-04)
Date: 2016-02-02
- Updated contact information
- Added additional svm() arguments for SL.svm
SuperLearner 2.0-18
Date: 2014-04-25
- Added recombineSL and recombineCVSL functions to re-fit the ensemble using a new metalearner in a computationally efficient manner
- For all wrappers, converted to format package::function when calling functions from other namespaces
- Added S3 method declarations for all predict.SL.* functions
- Added a 'SL.nnls' and 'predict.SL.nnls' functions
SuperLearner 2.0-17
Date: 2014-04-13
SuperLearner 2.0-16
Date: 2014-08-07
- Fixed error when computeCoef was re-run because of algorithms failing on full data
- Fixed Description field in Description file for CRAN policy
SuperLearner 2.0-15 (2014-07-21)
Date: 2014-07-16
- Fixed check for method.AUC and family
- Moved SL.bart over to SuperLearneExtra because BayesTree package no longer on CRAN
SuperLearner 2.0-14
Date: 2014-07-14
- Added method.AUC, contributed by Erin LeDell
SuperLearner 2.0-13
Date: 2014-04-16
- added the SampleSplitSuperLearner function to allow sample split validation instead of V-fold cross-validation
SuperLearner 2.0-11
Date: 2013-12-31
- fixed package requirement in CV.SuperLearner from multicore to parallel
- Fixed a conflict with the reorder function in plot.CV.SuperLearner (between the stats and gdata namespace)
- Fixed a bug in SL.svm when family is binomial to grab the correct predicted probabilities (thanks to Jeremy Coyle)
- Added .Rbuildignore to not include the README.md file from GitHub on CRAN
- Removed SuperLearner.Rnw
- Moved vignettes to vignettes folder
- Changed cluster example to use PSOCK instead of MPI in SuperLearner.Rd
- removed the ":::" in plot.CV.SuperLearner
- moved quadprog from depends to suggests as it is only called if the user uses method = "method.NNLS2" not the default.
- Added method.CC_LS and method.CC_nloglik. These provide true convex combination optimization for the 2 loss functions. Contributed by Sam Lendle.
SuperLearner 2.0-9 (2012-09-11)
Date: 2012-09-10
- Updated help documents
- Added links to SuperLearnerExtra on Github
SuperLearner 2.0-7
Date: 2012-04-04
- Switched from snow and multicore to parallel package
- fixed bug in CV.SuperLearner for leave-one-out cross-validation
- fixed bug in snowSuperLearner when only one screening algorithm is present
- method.NNloglik now reports the average -log likelihood instead of the sum to be consistent with NNLS
SuperLearner 2.0-6 (2012-03-01)
Date: 2012-02-29
- Added SL.leekasso (see http://simplystatistics.tumblr.com/post/18132467723/prediction-the-lasso-vs-just-using-the-top-10 for details)
- fixed parallel argument in CV.SuperLearner. Now always a character variable, no longer accepts FALSE.
- fixed SL.gam to call gam::gam.control in case the mgcv package is also loaded after gam.
SuperLearner 2.0-5
Date: 2011-10-12
- Fixed bug in CV.SuperLearner not saving SuperLearner objects (watch out for ifelse() statements).
- Added minbucket to SL.rpart.
- Added SL.rpartPrune, a version of SL.rpart with built-in pruning.
SuperLearner 2.0-4 (2011-09-27)
Date: 2011-10-01
- Minor changes to Rd files to cut build and check time. Time intensive examples now wrapped in \dontrun for CRAN.
SuperLearner 2.0-3
Date: 2011-08-05
- added plot.CV.SuperLearner
SuperLearner 2.0-2
Date: 2011-06-07
- fixed bug when one of the algorithms in SL.library has an error.
- fixed mcSuperLearner and snowSuperLearner not saving fitLibrary.
- added a placeholder Sweave vignette (SuperLearnerPresent.Rnw) to contain the SuperLearner presentation so the file can be found using the vignette() and browseVignettes() functions.
- CV.SuperLearner now outputs 'LibraryNames', 'SL.library', 'method' and 'Y'.
- summary.CV.SuperLearner has returned
SuperLearner 2.0-1
Date: 2011-05-17
- added predict.SuperLearner
SuperLearner 2.0-0
Date: 2010-12-27
- Version 2.* represents a complete rewrite of the SuperLearner package.
- Details on the changes from Version 1.* to 2.* can be found in ChangeLog.