# News for the SuperLearner package. #

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Version: 2.0-40-9000
Date: 2025-12-14

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Version: 2.0-40
Date: 2025-12-14
* Updated SL.xgboost to support the new version (3.1) of xgboost

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Version: 2.0-29
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)

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Version: 2.0-28
Date: 2021-04-13
* Updated maintainer email
* SL.gam added back, but now importing gam to avoid CRAN NOTE on usage or require()

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Version: 2.0-26
Date: 2019-08-12
* Added obsWeights and id arguments to the test-glmnet.R file, line 24

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Version: 2.0-25
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`

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Version: 2.0-24
Date: 2018-07-10
* remove multicore test in randomForest test. Was generating warning note on CRAN devel

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Version: 2.0-23
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


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Version: 2.0-22
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.


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Version: 2.0-21
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.

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Version: 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

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Version: 2.0-19
Date: 2016-02-02
* Updated contact information
* Added additional svm() arguments for SL.svm

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Version: 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

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Version: 2.0-17
Date: 2014-04-13
* Moved cvAUC to imports

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Version: 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

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Version: 2.0-15
Date: 2014-07-16
* Fixed check for method.AUC and family
* Moved SL.bart over to SuperLearneExtra because BayesTree package no longer on CRAN

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Version: 2.0-14
Date: 2014-07-14
* Added method.AUC, contributed by Erin LeDell

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Version: 2.0-13
Date: 2014-04-16
* added the SampleSplitSuperLearner function to allow sample split validation instead of V-fold cross-validation

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Version: 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.

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Version: 2.0-9
Date: 2012-09-10
* Updated help documents
* Added links to SuperLearnerExtra on Github

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Version: 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

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Version: 2.0-6
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.

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Version: 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.

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Version: 2.0-4
Date: 2011-10-01

* Minor changes to Rd files to cut build and check time. Time intensive examples now wrapped in \dontrun for CRAN.

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Version: 2.0-3
Date: 2011-08-05

* added plot.CV.SuperLearner

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Version: 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

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Version: 2.0-1
Date: 2011-05-17

* added predict.SuperLearner


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Version: 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.
