Super Learner Prediction

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

News

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

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

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

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

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

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