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Changelog

Source: NEWS.md

mlr3extralearners (development version)

  • regr.glm: added the tol and wtol parameters of stats::glm.control(), which are available from R 4.7.0 on.
  • Update CoxBoost version to 1.5.2 which fixes a prediction CIF bug and enabling prediction with one observation or time point from the upstream package (and so no need to handle this on the mlr3 side).
  • Use mlr3cmprsk version 0.0.6.

mlr3extralearners 1.7.0

New Features

Breaking Changes

  • classif.mlp was renamed to classif.mlp_rsnns because its id clashed with the classif.mlp learner in mlr3torch.

Other

  • classif.catboost and regr.catboost now report $internal_valid_scores, which were previously always empty, for the eval_metric and the loss_function.

  • classif.catboost, regr.catboost, classif.lightgbm, regr.lightgbm, surv.xgboost.aft and surv.xgboost.cox gained a $best_valid_scores field, so msr("best_valid_score") can be used with them.

  • fix: classif.lightgbm and regr.lightgbm reported $internal_valid_scores for the last boosting iteration, although LightGBM predicts with model$best_iter. The scores are now taken from model$best_iter and therefore describe the model that is used for prediction.

  • New learners classif.tabfm and regr.tabfm interfacing the tabfm Python package, the tabular foundation model from Google Research.

  • classif.tabpfn and regr.tabpfn: added support for the feature types character, factor, and ordered, which are encoded as categorical features by tabpfn. The level order of ordered features is not preserved. Previously the features were converted to a numeric matrix, which ruled out categorical features even though tabpfn supports them.

  • New learners classif.bam and regr.bam fitting fast generalized additive models for large datasets with mgcv::bam() (#355).

  • New learner classif.opls fitting orthogonal partial least squares discriminant analysis with ropls::opls() from the Bioconductor package ropls (#268).

  • New learner regr.grf fitting a generalized random forest with grf::regression_forest(), supporting se predictions, observation weights, and missing feature values.

  • New learner regr.polynomial fitting a polynomial regression with stats::lm() and stats::poly() (#420).

  • classif.tabpfn and regr.tabpfn: updated the parameter sets to match tabpfn 8.1.0, adding auto_scale_n_estimators, keep_cache_on_device, n_preprocessing_jobs, differentiable_input, and show_progress_bar (plus eval_metric and tuning_config for classification), adding the "batched" option to fit_mode, and correcting the informational default of n_estimators to 8.

  • Fixed a partial argument-matching bug in survival glmnet learners where stype could be matched to predict argument s, causing s to be overwritten by stype = 1 or 2 and predictions to be over-regularized.

mlr3extralearners 1.6.0

Breaking Changes

  • classif.priority_lasso, regr.priority_lasso, and surv.priority_lasso: the parameter sets were reduced to a focused subset of prioritylasso::prioritylasso() arguments. The glmnet::cv.glmnet() pass-through hyperparameters were removed because they were not consistently forwarded (#594).
  • surv.cv_glmnet: removed the parameters standardize.response, type.gaussian, type.logistic, and type.multinomial, which are not applicable to the Cox family (#594).
  • surv.glmnet: removed the parameters alignment, parallel, type.logistic, and type.multinomial, which are CV-only or not applicable to the Cox family, and renamed the predict parameter predict.gamma to gamma (#594).

Other

  • Updated minimum versions of suggested packages, most notably glmnet (>= 5.0) (#594).
  • classif.fastai now pins its fastcore Python dependency to a version below 2.0.0 because fastai (<= 2.8.7) is incompatible with fastcore 2.0 but does not declare an upper bound.
  • The h2o learners no longer emit a spurious warning about an outdated H2O cluster version when training or predicting.
  • surv.cv_glmnet and surv.glmnet: updated for glmnet v5.0, added the train parameters cox.ties (initialized to "breslow" to keep the previous tie-handling behavior), maxp, and path, added the predict parameter exact (surv.cv_glmnet only), fixed predictions for relaxed fits (relax = TRUE), and added a read-only native_model field that returns the fitted glmnet model (#594).
  • surv.priority_lasso: added the train parameter cox.ties, initialized to "breslow" (#594).
  • surv.survdnn: added the .threads train parameter (#594).
  • regr.botorch_fullybayesian now declares its numpyro, jax, and jaxlib Python dependencies so they are installed automatically.
  • regr.bart’s hyperparameter sigdf was changed to type double.
  • regr.crs: added the train parameter max.eval for compatibility with crs (>= 0.15-45) (#601).

New Features

  • classif.priority_lasso, regr.priority_lasso, and surv.priority_lasso now support automatic block-priority derivation via adaptive.order = TRUE, following Herrmann et al. (2021), and prediction for automatic block ordering was fixed upstream in prioritylasso version 0.4.0.

mlr3extralearners 1.5.2

Other

  • Use CRAN version of survdistr.
  • Use mlr3cmprsk version 0.0.5.
  • Update crs parameters.

mlr3extralearners 1.5.1

Other

  • Skip fastai and botorch tests on Windows and macOS where the Python backends crash or time out.
  • Skip tabpfn tests until token work reliable again.
  • Skip blockForest tests on macOS where SE predictions fail sanity checks.
  • Skip h2o.glm classification tests on Windows due to Java NullPointerException.
  • Skip GPfit tests on Windows where they crash under R-devel.
  • Skip classif.aorsf sanity autotest due to inconsistent tie-breaking across predict types.
  • Skip surv.flexreg sanity autotest on Windows due to initial parameter estimation failure.

mlr3extralearners 1.5.0

New Features

  • New Learners:
    • LearnerCompRisksCoxboost
    • LearnerRegrGPfit
    • LearnerClassifMLP
    • LearnerClassifSaeDNN
    • LearnerClassifPlsdaCaret
    • LearnerSurvDNN
    • LearnerRegrH2ORandomForest
    • LearnerRegrH2OGLM
    • LearnerClassifH2OGLM
    • LearnerClassifH2OGBM
    • LearnerClassifH2ORandomForest
    • LearnerClassifH2ODeeplearning
    • LearnerRegrH2OGBM
    • LearnerRegrH2ODeeplearning
    • LearnerClassifLvq1
    • LearnerRegrBotorchFullyBayesian
  • Added kernel and input/output transformations for LearnerRegrBotorchSingleTaskGP and LearnerRegrBotorchMixedSingleTaskGP.

Breaking Changes

Other

  • Updated Extending vignette to incorporate information about skipping tests and considerations for testing Python learners
  • survdistr is now on Suggests (used for constant interpolation of the Kaplan-Meier predictions of the partykit survival learners)
  • Updated mlr3proba (0.8.8), pls and xgboost to the most recent CRAN versions

mlr3extralearners 1.4.0

New Features

  • New Learners:
    • LearnerSurvGamCox
    • LearnerSurvFlexReg
    • LearnerSurvNCVsurv
    • LearnerRegrRRF
    • LearnerRegrPcr
    • LearnerRegrPlsr
    • LearnerRegrLaGP
    • LearnerRegrFrbs
    • LearnerRegrBcart
    • LearnerRegrBgp
    • LearnerRegrBgpllm
    • LearnerRegrBlm
    • LearnerRegrBtgp
    • LearnerRegrBtgpllm
    • LearnerRegrBtlm
    • LearnerRegrNCVReg
    • LearnerClassifDbnDNN
    • LearnerClassifNNTrain
    • LearnerClassifSparseLDA
    • LearnerClassifNCVreg

Breaking Changes

  • lrn("surv.flexible") (LearnerSurvFlexible) was renamed to lrn("surv.flexsurvspline") (LearnerSurvFlexSpline) to properly reflect the wrapped train function (Royston/Parmar spline model).

Other

  • CoxBoost is now on CRAN, so we removed it from Remotes
  • lrn("surv.flexsurvspline") predicts linear predictors using predict.flexsurvreg(). We were doing manually the same exact prediction, so no functionality was changed.
  • compatibility: xgboost 3.1.2.1 (survival learners)
  • parameter updates for regr.lmer/glmer learners
  • updates for randomForestSRC 3.5.0 (use.uno parameter)
  • performance improvement: use of data.table::fifelse (@m-muecke)

mlr3extralearners 1.3.1

  • Update website to include citation information

mlr3extralearners 1.3.0

  • Add formula and anc params to surv.flexible learner, as well as response predict type (mean survival time).
  • Fix regr.gamboost regression predictions (#498).

mlr3extralearners 1.2.0

New Features

  • New Learners:
    • LearnerCompRisksRandomForestSRC
    • LearnerSurvBlockForest
    • Learner{Classif,Regr,Surv}BlockForest
    • Learner{Classif,Regr}ExhaustiveSearch
    • LearnerClassifFastai
    • Learner{Classif,Regr}Penalized
    • Learner{Classif,Regr}Bst
    • LearnerClassifAdabag
    • LearnerClassifAdaBoosting
    • Learner{Classif,Regr}Evtree
    • LearnerClassifKnn
    • LearnerClassifRotationForest
    • LearnerRegrCrs
    • LearnerClassifStepPlr
    • LearnerClassifMda
    • LearnerClassifRferns
    • LearnerClassifNeuralnet
    • LearnerRegrBrnn
    • LearnerRegrBotorchSingleTaskGP
    • LearnerRegrBotorchMixedSingleTaskGP
  • Add new control_custom_fun parameter in surv.aorsf
  • New function learner_is_runnable() to check whether the required packages to train a learner are available.
  • Added selected_features property to RandomForestSRC learners (prediction doesn’t work if vars.used = 'all.trees')

Bug fixes

  • Tests are now skipped when the suggested packages is not available. This will make local development much more convenient.
  • Removed parameters from RandomForestSRC learners that weren’t used + optimized tests
  • Removed discrete parameter from surv.parametric, so that it is impossible to return distr6::VectorDistribution survival predictions (softly deprecated in mlr3proba@v0.8.1)

Breaking Changes

  • All (extra) density learners are removed. These will be transferred to mlr3proba soon (see v0.8.2 or later).
  • The create_learner() generator was removed, because it was hard to maintain and boilerplate code in the age of LLMs is easy enough to write.
  • remove discrete parameter from surv.parametric, so that it is impossible to return distr6::VectorDistribution survival predictions (softly deprecated in mlr3proba@v0.8.1)
  • classif.lightgbm now works with encapsulation with multiclass tasks
  • the package no longer re-exports lrn and lrns, which should anyway be available to the user as the package depends on mlr3, where these functions are defined.
  • Removed various learners:
    • randomPlantedForest was removed, because there is currently no way to save the model.
    • The deep learning methods from survivalmodels were removed, because they also cannot be saved and because the upstream package is archived.

Other

  • The package now imports withr
  • mlr3proba is now an import and no longer a suggested package.
  • mlr3cmprsk is added as an import.
  • The package no longer uses set.seed() in the tests and instead uses withr::local_seed() This means the auto tests will be stochastic like they should be.
  • The CI now checks that RCMD-check passes when suggested packages are not available.
  • distr6 dependency is removed. partykit survival learners use constant interpolation of the predicted Kaplan-Meier curves via survdistr::vec_interp()

mlr3extralearners 1.1.0

New Features:

  • Support offset in learners regr|classif.mgcv, regr.glm and regr.lmer.
  • Added learners LearnerRegrQGam and LearnerRegrMQGam.
  • Added learners LearnerClassifTabPFN and LearnerRegrTabPFN.
  • Added the new version of learner weights to all learners that support weights
  • Added marshaling for surv.xgboost.cox.
  • Added learner LearnerClassifKnn.

Bugfixes:

  • lightgbm classifier now works with encapsulation (#437)

mlr3extralearners 1.0.0

  • Add “Prediction types” doc section for all 30 survival learners + make sure it is consistent #347
  • All survival learners have crank as main prediction type (and it is always returned) #331
  • Added minimum working version for all survival learners in DESCRIPTION file
  • Harmonized the use of times points for prediction as much as possible across survival learners #387
    • added gridify_times() function to coarse time points
    • fixed surv.parametric and surv.akritas use of ntime argument
  • surv.parametric is now used by default with discrete = TRUE (no survival learner returns now distr6 vectorized distribution by default)
  • Doc update for mlr3 (version 0.21.0)
  • Fixed custom and initial values across all learners documentation pages
  • Fixed doc examples that used learner$importance()
  • Set n_thread = 1 for surv.aorsf and use unique event time points for predicted S(t)
  • Add selected_features() for surv.penalized
  • Fix surv.prioritylasso learner + add distr predictions via Breslow #344
  • Survival SVM gamma.mu parameter was split to gamma and mu to enable easier tuning (surv.svm learner)

mlr3extralearners 0.9.0

  • Added response (i.e., survival time) prediction to aorsf learner
  • Updated support for flexsurv v2.3
  • Fixed bug in catboost that caused invalid probability levels during resample() or benchmark() (#353)
  • the $model slot of lrn("classif.abess") now contains the model of the upstream package again.
  • Add early stopping and validation support to learners lrn("surv.xgboost.aft") and lrn("surv.xgboost.cox").
  • Added early stopping and validation to catboost and lightgbm.
  • Added missing case.depth parameter to rfsrc learners.
  • mlr3 is now in Depends instead of Imports.
  • Deprecated learner lrn("surv.xgboost") was now removed. Use lrn("surv.xgboost.cox") or lrn("surv.xgboost.aft") instead.
  • Change xgboost default nrounds from 1 to 1000.
  • remove obliqueRSF Learner which was long superseded by aorsf
  • a lot of examples were added to the learners

mlr3extralearners 0.8.0

  • Added surv.xgboost.cox and surv.xgboost.aft separate survival learners. distr prediction on the cox xgboost learner is now estimated via Breslow by default and aft xgboost has now in addition a response prediction (survival time)
  • Ported surv.parametric code to survivalmodels, changed type parameter to form to avoid conflict with survivalmodels’s default parameter list
  • Fix: Replace hardcoded VectorDistributions from partykit and flexsurv survival learners with survival matrices (Matdist) (thanks to @bblodfon)
  • Feat: Add discrete parameter in surv.parametric learner to return Matdist survival predictions
  • Added method selected_features() to CoxBoost survival learners (thanks to @bblodfon)
  • Added the Random Planted Forest Learner (thanks to @jemus42)
  • re-added the catboost learner as it was requested (was previously removed because of installation issues)
  • surv.ranger now receives parameters during $predict() (thanks to @jemus42)
  • Feature: Learner surv.bart was added (thanks to @bblodfon)
  • Parameters of lrn("surv.aorsf") were updated (thanks to @bcjaeger)
  • Various minor doc improvements
  • Added the distr predict type to the surv.cv_glmnet and surv.glmnet learners (thanks to @bblodfon)
  • Feat: Added many new WEKA learners (thanks to @damirpolat)
  • Fix: I and F params from IBk learner are too interdependent (I can only be TRUE when F is FALSE and vice versa). Combined them into one factor param weight that has two levels – I and F.
  • Fix: U must be FALSE for S to be tunable in J48 learner.
  • Compatibility with upcoming ‘paradox’ release.

mlr3extralearners 0.7.1

  • Add parameter perf.type to rfsrc learners
  • Add vignette about “extending learners” which was previously in the mlr3book.
  • Remove the "multiclass" property from lrn("classif.gbm"), as this feature is broken.

mlr3extralearners 0.7.0

  • Add new parameters to lightgbm learners
  • Add feature type "factor" to gam learners
  • Add new parameter min.bucket to ranger
  • Remove catboost learner (because the developers don’t properly take care of the R package)
  • Add argument nthreads to dbarts learners; set verbose to FALSE by default (thanks to @ck37)
  • Add new parameters to prioritylasso
  • Fix: available levels for parameter of imbalanced random forest (typo)

mlr3extralearners 0.6.1

  • BREAKING CHANGE: lightgbm’s early stopping mechanism now uses the task’s test set.
  • feat: Add two new learners regr.abess and classif.abess (thanks to @bbayukari)
  • feat: Added learner LearnerClassifImbalancedRandomForestSRC (thanks to
  • Feat: Added learners LearnerClassifPriorityLasso, LearnerRegrPriorityLasso, LearnerSurvPriorityLasso (thanks to

mlr3extralearners 0.6.0

  • Feat: Added learner LearnerClassifGlmer (https://github.com/mlr-org/mlr3extralearners/issues/243)
  • Fix: Failing xgboost parameter test
  • Fix: Add arguments nei and ncv.thread that were added to mgcv::gam() in version 1.8.41
  • Fix: Added missing property "weights" to LearnerClassifGlmer and LearnerRegrLmer
  • Fix: lightgbm uses the param_vals stored in the state for hotstarting
  • Fix: Rely on state$data_prototype to get ordering of features via ordered_features() like in mlr3learners and therefore obviate the need to store feature_names in the state
  • Fix: extralearners are removed from mlr_learners when unloading mlr3extralearners

mlr3extralearners 0.5.49

  • Added missing feature type "integer" to classif.randomForest
  • Added missing feature type "logical" to {classif, regr}.randomForest

mlr3extralearners 0.5.48

  • Add rsm learner
  • fix list_mlr3learners() function. Now slower but correct.
  • Remove catboost from DESCRIPTION until it can be installed with pak
  • Fix typos in test templates
  • Update README

mlr3extralearners 0.5.47

  • Add mlr3proba dependencies into remotes (no longer on CRAN)
  • Correct documentation of gbm learner: default was incorrectly documented and the parameter was incorrectly referred to as keep_data instead of keep.data
  • Add catboost to the dependencies
  • Added LearnerSurvAorsf with key surv.aorsf. See https://github.com/bcjaeger/aorsf for more details on aorsf

mlr3extralearners 0.5.46

mlr3extralearners 0.5.45

  • Minor corrections in create_learner and the learner template.

mlr3extralearners 0.5.44

  • Corrected parameters in lightgbm learners
  • Implemented hotstarting for lightgbm learners
  • Adjusted lightgbm train and predict methods to changes in lightgbm dev version (https://github.com/mlr-org/mlr3extralearners/issues/217)
  • Added paramtests for lightgbm through webscraping

mlr3extralearners 0.5.43

  • Clean up test files
  • Fix installation of catboost in CI
  • Fix the create_learner function
  • Adjust templates for creation of learner
  • Split up “Parameter Changes” in sections “Custom mlr3 parameters” and “Custom mlr3 defaults”

mlr3extralearners 0.5.42

  • Fix bug in C50 learner: Weights were not passed correctly

  • Remove kerdiest Learner because it is not being maintained on CRAN anymore

mlr3extralearners 0.5.41

  • Fix bugs in learners lmer and J48

  • Remove predict type proba from J48

  • Delay loading of mlr3proba learners

mlr3extralearners 0.5.40

  • lightgbm:

    • Add parameter convert_categoricals
    • Validation split not respects grouping / stratification
    • Fixed bug

  • Docs: Renamed section “Custom mlr3 defaults” to “Parameter Changes”

  • Added labels to learners

mlr3extralearners 0.5.39

  • Remove extraTrees because it is no longer on CRAN and GH version has errors

  • Remove sketch_eps parameter from xgboost because it is no longer listed in the docs

mlr3extralearners 0.5.38

  • Added regr.lmer

mlr3extralearners 0.5.37

  • Improve docs and change doc layout
  • Fix typo in man-roxygen templates
  • Port mlr3proba learners (mlr3proba is no longer on CRAN)
  • Exclude relevant files in precommit

mlr3extralearners 0.5.36

  • Add missing ‘threads’ tag to respective parameters.

mlr3extralearners 0.5.35

  • Full installation in workflow ‘test_selection’ (is faster than the previous approach, where selected packages were installed from CRAN)

mlr3extralearners 0.5.34

  • remove explicit mlr3misc:: (is imported)

mlr3extralearners 0.5.33

  • consistency: Use params in train and predict calls, even in learners that currently don’t have predict / train params. This allows easier correction of parameters by users.

mlr3extralearners 0.5.32

  • chore: add new parameters for kde and rfsrc

  • temporarily disable feat_all test for obliqueRSF (failed in $score() stage, because issue only happened in CI and could not be reproduced

mlr3extralearners 0.5.31

  • Many non-standard tags were included in the learners, these are removed
  • Some bugs in learners were fixed (survival rfsrc: “estimator” was incorrectly handled in .predict)
  • Minor refactorings in train methods of learners
  • Avoid partial argument matching: Some learners used “tag = …” instead of the correct “tags = …”

mlr3extralearners 0.5.30

  • Revert to using mlr3proba and survivalmodels CRAN version

mlr3extralearners 0.5.29

  • Change in vignette

mlr3extralearners 0.5.28

  • update randomForestSRC

mlr3extralearners 0.5.27

  • Update learner status page

mlr3extralearners 0.5.26

  • Fixed survivalmodel learners

mlr3extralearners 0.5.25

  • Introduce parameter early_stopping_split for lightgbm learners
  • Tidy description of R package
  • Update NEWS.md for previous releases

mlr3extralearners 0.5.24

  • Don’t allow integer for density estimator dens.plug

mlr3extralearners 0.5.23

  • Fix bug in lightgbm

mlr3extralearners 0.5.22

  • Style package using the mlr3 style

mlr3extralearners 0.5.21

  • Update files for creation of new learner
  • Fixes regarding create_learner
  • CI modifications

mlr3extralearners 0.5.20

  • Fix all parameter tests (run_paramtest was updated in mlr3 in November 2021)
  • paramtests were moved from inst/paramtest to tests/testthat
  • Change in the CI files: parameter tests and learner tests are now run together
  • formatting and other minor corrections

mlr3extralearners 0.5.19

  • Provide correct range for neighbors argument for Cubist

mlr3extralearners 0.5.18

  • Allow integer as feature types for RWeka learners
  • Correction of RWeka tests

mlr3extralearners 0.5.17

  • Improve vignette

mlr3extralearners 0.5.16

  • Fix bug in AdaBoostM1 (control arg)

mlr3extralearners 0.5.15

  • Change in maintainer

mlr3extralearners 0.5.14

  • Fix bug regarding Weka control args.

mlr3extralearners 0.5.13

  • Fix categorical_features in {lightgbm} learners

mlr3extralearners 0.5.12

  • Patch for lightgbm updates

mlr3extralearners 0.5.11

  • Add option to not open files with create_learner

mlr3extralearners 0.5.10

  • Added params ignored_features and one_hot_max_size to classif.catboost

mlr3extralearners 0.5.9

  • Fixed bug that didn’t allow C parameter to be set for nu-regression

mlr3extralearners 0.5.8

  • Add regr.rvm and classif.lssvm

mlr3extralearners 0.5.7

mlr3extralearners 0.5.6

  • Fix learners requiring distr6. distr6 1.6.0 now forced and param6 added to suggests

mlr3extralearners 0.5.5

  • Bugfix regr.gausspr

mlr3extralearners 0.5.4

mlr3extralearners 0.5.3

  • Fixed bugs in catboost for classification
  • Removed factor feature types from catboost
  • Added install_catboost to make installation from catboost simpler

mlr3extralearners 0.5.2

  • Fixed learner tests

mlr3extralearners 0.5.1

  • Fixes bug in base parameter of {bart} learners

mlr3extralearners 0.5.0

  • Deprecated liblinear learners now removed
  • Internal changes to ParamSet representation
  • checkmate now imported

mlr3extralearners 0.4.9

  • Minor internal changes

mlr3extralearners 0.4.8

  • Added LearnerRegrCubist and LearnerRegrMars

mlr3extralearners 0.4.7

mlr3extralearners 0.4.6

  • Updates default cores for rfsrc learners to 1

mlr3extralearners 0.4.5

  • Fix RWeka tests (stochastic failures, implementation unaffected)

mlr3extralearners 0.4.3

  • Add support for custom families in all remaining mboost learners

mlr3extralearners 0.4.2

  • Fix broken partykit tests

mlr3extralearners 0.4.0

  • Added LearnerRegrGam and LearnerClassifGam with keys regr.gam and classif.gam from package mgcv.

mlr3extralearners 0.3.6

  • surv.coxboost now uses the GitHub version instead of CRAN (archived)

mlr3extralearners 0.3.4

  • Add support for custom families to regr.glmboost

mlr3extralearners 0.3.1

  • surv.svm now supports all feature types

mlr3extralearners 0.3.0

  • Added LearnerRegrLightGBM and LearnerClassifLightGBM with keys regr.lightgbm and classif.lightgbm respectively. Copied from mlr3learners.lightgbm
  • LearnerRegrLiblineaRX and LearnerClassifLiblineaRX deprecated in favour of only two learners (LearnerRegrLiblineaR and LearnerClassLiblineaR) with added hyper-parameters. Deprecated learners will be removed in v0.3.0.
  • Deprecated classif.nnet will be removed in v0.4.0.
  • Deprecated liblinearX will be removed in v0.4.0.

mlr3extralearners 0.2.0

  • dist = "logistic" has been removed from surv.parametric as it is unclear what this was previously predicting.
  • Added type = "tobit" for dist = "gaussian" so predictions can correspond with survival::survreg.
  • Added LearnerRegrGlm with the unique key regr.glm from package stats, which allows users to change the family hyperparameter when fitting generalized linear regression models.
  • Minor internal changes
  • Removed keeptrees parameter from classif.bart as this is forced internally
  • Fixed incorrect response and probability predictions in classif.bart
  • Added hyper-parameters to classif.earth and regr.earth
  • Added se predict type to regr.earth
  • Fixed predictions in regr.knn and classif.knn

mlr3extralearners 0.1.3

  • mlr3proba moved to Suggests
  • install_learners now additionally installs required mlr3 packages
  • Bugfix in surv.parametric occurring if feature names are switched between training and predicting
  • Deprecated classif.nnet, in the future please load from mlr3learners

mlr3extralearners 0.1.2

  • Fixes in crank and distr computation of all survival learners

mlr3extralearners 0.1.1

  • Patch for bugs in surv learners that were reversing the order of crank, see this issue for full details: https://github.com/mlr-org/mlr3proba/issues/165
  • response is no longer returned by surv.mboost, surv.blackboost, surv.glmboost, surv.gamboost or surv.parametric
  • Bugfix in surv.parametric with ph form
  • Bugfix in survivalmodelslearners which weren’t returning distr
  • surv.coxboost and surv.coxboost_cv can now only handle integer and numeric feature types, previous automated internal coercions were inconsistent with mlr3 design.

mlr3extralearners 0.1.0

  • Initial release. mlr3extralearners contains all learners from the mlr3learners organization, which is now archived.