# News for the SuperLearner package. # --- Version: 2.0-30-9000 Date: 2024-02-06 --- 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) --- 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() --- Version: 2.0-26 Date: 2019-08-12 * Added obsWeights and id arguments to the test-glmnet.R file, line 24 --- 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` --- Version: 2.0-24 Date: 2018-07-10 * remove multicore test in randomForest test. Was generating warning note on CRAN devel --- 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 --- 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. --- 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. --- 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 --- Version: 2.0-19 Date: 2016-02-02 * Updated contact information * Added additional svm() arguments for SL.svm --- 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 --- Version: 2.0-17 Date: 2014-04-13 * Moved cvAUC to imports --- 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 --- 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 --- Version: 2.0-14 Date: 2014-07-14 * Added method.AUC, contributed by Erin LeDell --- Version: 2.0-13 Date: 2014-04-16 * added the SampleSplitSuperLearner function to allow sample split validation instead of V-fold cross-validation --- 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. --- Version: 2.0-9 Date: 2012-09-10 * Updated help documents * Added links to SuperLearnerExtra on Github --- 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 --- 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. --- 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. --- 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. --- Version: 2.0-3 Date: 2011-08-05 * added plot.CV.SuperLearner --- 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 --- Version: 2.0-1 Date: 2011-05-17 * added predict.SuperLearner --- 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.