model now called keep_model.TaskSurv$kaplan methodsimsurv task that made it impossible to predict the target$distr called for a learner that does not support this return typeas_task_dens and as_prediction_denst_max and p_max to Graf, Schmid and Integrated Log-loss as an alternative to times. t_max is equivalent to times = seq(t_max) and p_max is the proportion of censoring to integrate up to in the dataset.mlr3extralearners from Suggestsresponse to as_prediction_survassert_survmlr3 is now in Depends not importsdistr predictions are now internally stored as matrices to significantly reduce prediction object sizessurv.graf, surv.schmid, and surv.intlogloss now allow training data to be passed to the score function with task and train_set to allow the censoring distribution to be estimated on the training data. This is automatically applied for resample and benchmark results.surv.graf, surv.schmid, and surv.intlogloss now include a parameter proper to determine what weighting scheme should be applied by the estimated censoring distribution, The current method (Graf, 1999) proper = FALSE, weights observations either by their event time or ‘current’ time depending if they’re dead or not, the new method proper = TRUE weights observations by event time. The proper = TRUE method is strictly proper when censoring and survival times are independent and G is estimated on large enough data. The proper = FALSE method is never proper. The default is currently proper = FALSE to enable backward compatibility, this will be changed to proper = TRUE in v0.6.0.rm_cens parameter in surv.logloss has been deprecated in favour of IPCW. rm_cens will be removed in v0.6.0. If rm_cens or IPCW are TRUE then censored observations are removed and the score is weighted by an estimate of the censoring distribution at individual event times. Otherwise if rm_cens and IPCW are FALSE then no deletion or weighting takes place. The IPCW = TRUE method is strictly proper when censoring and survival times are independent and G is estimated on large enough data. The ipcw = FALSE method is never proper.surv.dcalib for the D-Calibration measure from Haider et al. (2020)."interval2" task type not to work$aggregate"interval2"TaskSurv including times (observed survival times), status (observed survival indicator), unique_times (set of sorted unique outcome times), unique_event_times (set of sorted unique failure times), risk_set (set of observations alive ‘just before’ a given time)"interval2" censoring type has been removed from TaskSurv as this is covered by the other typestime and event arguments in TaskSurvPredictionDens can now include distr return type (equivalent to learner$model)PipeOpCrankCompositor updated to fix bottleneck in computation via mean. Now Inf or NA is replaced by 0 for response and imputed with the median for crankdistr predict types fixed that lead to fitting degenerate distributions and returning incorrect values for mean survival time and crankcompose_crank was previously returning ranks with the reverse ordering so that higher ranks implied higher risk not lower.MeasureSurvLoglossMeasureSurvCalibrationAlphaTaskDens now inherits from TaskUnsupervised which means target/truth has been removed. No specification of a target column is required, instead a one-column matrix-like object or numeric vector should be passed to the task backend and the density will be estimated for this column, or two columns and one set as weight.load_eruption to fix name of data columnspracma dependency in learnersPipeOpDistrCompositor, previously base distribution was only using the first predicted distribution, now the baseline is taken by averaging over all predictions with uniform weightsLearnerDensityKDE is now Epan to reduce importsMeasureSurvCalibrationBeta now returns NA not error if lp predict type not availablePredictionRegr causing masking issues with {mlr3}PipeOpDistrCompositor causing some cdf predictions to be lostmlr3pipelines: public train and predict methods to privategrace, actg, gbcs, whasoverwrite to crankcompositor pipeop and pipelinesurv.kaplan crank predictionMeasureSurvCindex added. Generalises all c-index measures with a fast C++ implementationmlr3learners/mlr3learners.probaMeasureSurvSchmidMeasureSurvCalibrationBeta and MeasureSurvCalibrationAlphasurv.brier alias added for surv.grafresponse parameter added to PipeOpCrankCompositor and crankcompositor to now optionally fill response predict type with same values as crankPipeOpProbregrCompostior and compose_probregr for composition to distr return type from (a) regression learner(s) predicting response and sePipeOpSurvAvg and surv_averager pipeline for weighted model averaging of distr, lp, crank, and response predictions.MeasureSurvCindex instead with following parameters: MeasureSurvBeggC, use defaults; MeasureSurvHarrellC, use defaults; MeasureSurvUnoC, use weight_meth = 'G/2'; MeasureSurvGonenC, use weight_method = 'GH'MeasureSurvGrafSE, MeasureSurvLoglossSE, MeasureSurvIntLoglossSE, MeasureSurvRMSESE, MeasureSurvMSESE, and MeasureSurvMAESE all deprecated and will be deleted in v0.4.0. Use msr("surv.graf", se = TRUE) instead (for example).surv.nagelkR2 is now surv.nagelk_r2, analogously for all R2, AUC, TPR, and TNR measures. Old constructors will be deleted in v0.4.0.distrcompose and crankcompose to distr_compose and crank_compose. Old ids will be deleted in v0.4.0.surv.nagelkR2 is now surv.nagelk_r2, analogously for all R2, AUC, TPR, and TNR measures. Old constructors will be deleted in v0.4.0.MeasureSurvGraf and MeasureSurvIntLogloss now have much faster C++ implementationLearnerSurvGlmnet, LearnerSurvCVGlmnet, LearnerSurvXgboost and LearnerSurvRanger have been moved to mlr-org/mlr3learners
LearnerSurvGBM has been moved to https://www.github.com/mlr3learners/mlr3learners.gbm
LearnerSurvMboost, LearnerSurvGlmBoost, LearnerSurvGamboost, LearnerSurvBlackboost have been moved to https://www.github.com/mlr3learners/mlr3learners.mboost
mboost family of learners: added gehan family, fixed parameters for cindex, added support for: weights, response predict type, importance, selected_featuresLearnerDensHist and LearnerDensKDE have been moved to the mlr3learners orgmlr3learners org, LearnerSurv: Flexible, ObliqueRSF, Penalized, RandomForestSRCLearnerSurvXgboost previously lp was erroneously returned as exp(lp)LearnerSurvParametric and LearnerSurvNelson moved to mlr3learners/mlr3learners.survival repoLearnerSurvCoxboost and LearnerSurvCVCoxboost moved to mlr3learners/mlr3learners.coxboost repoLearnerSurvSVM moved to mlr3learners/mlr3learners.survivalsvm repoLearnerSurvKaplan, LearnerSurvCoxPH, and LearnerDensHist will be moved to the mlr3learners orgTaskDens, LearnerDens, PredictionDens, and MeasureDens.mlr_tasks_faithful and mlr_tasks_precip for density task examplesmlr_task_generators_simdens for generating density tasksmlr3::mlr_learners$keys("^dens") for the full listtrain_internal, predict_internal, score_internal are now private methods .train,.predict,.scorelp in surv.parametric to include the intercept, which is in line with survival::survreg. Now exp(pred$lp) is equal to the predicted survival time for AFTsmboost to suggestsresponse predict type, which predicts the time until event. Currently only supported for AFT models in surv.parametricresponse predict type: MeasureSurvMAE, MeasureSurvMAESE, MeasureSurvMSE, MeasureSurvMSESE, MeasureSurvRMSE, MeasureSurvRMSESEmode option to crankcompositorR62S3 incompatibilitymethod argument to integrated scores and added weighting by bin-widthmethod to MeasureSurvIntegrated constructor and fieldsTaskSurv, MeasureSurvUnoCLearnerSurvRpart parameter parms and cost