PipeOpCrankCompositor updated to fix bottleneck in computation via mean. Now Inf or NA is replaced by 0 for response and 1000 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