Changelog

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 crank
Bug in distr predict types fixed that lead to fitting degenerate distributions and returning incorrect values for mean survival time and crank

CRITICAL BUG FIX - compose_crank was previously returning ranks with the reverse ordering so that higher ranks implied higher risk not lower.

All learners that previously lived in the mlr3learners organisation are now in the mlr3extralearners repository.
Fixed bottleneck in MeasureSurvLogloss
Bugfix in MeasureSurvCalibrationAlpha
Patch for valgrind
TaskDens 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.
Fixed bug in load_eruption to fix name of data columns
Added calibration plot for comparing average predicted survival distribution to Kaplan-Meier to mlr3viz
Removed unneccessary pracma dependency in learners
Fix in PipeOpDistrCompositor, previously base distribution was only using the first predicted distribution, now the baseline is taken by averaging over all predictions with uniform weights

Default kernel for LearnerDensityKDE is now Epan to reduce imports
Minor internal patches for mlr3 0.6.0
Bug fix in MeasureSurvCalibrationBeta now returns NA not error if lp predict type not available

MeasureSurvCindex added. Generalises all c-index measures with a fast C++ implementation
Akritas estimator added to mlr3learners/mlr3learners.proba
Added scoring rule MeasureSurvSchmid
Addd calibration measures MeasureSurvCalibrationBeta and MeasureSurvCalibrationAlpha
surv.brier alias added for surv.graf
response parameter added to PipeOpCrankCompositor and crankcompositor to now optionally fill response predict type with same values as crank
Added PipeOpProbregrCompostior and compose_probregr for composition to distr return type from (a) regression learner(s) predicting response and se
Added PipeOpSurvAvg and surv_averager pipeline for weighted model averaging of distr, lp, crank, and response predictions.

The following measures are deprecated use 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).
Measures renamed such that 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.
Renamed distrcompose and crankcompose to distr_compose and crank_compose. Old ids will be deleted in v0.4.0.

Measures renamed such that 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++ implementation

LearnerSurvGlmnet, 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

Early release due to backward compatibility error introduced by an upstream dependency
Minor updates to mboost family of learners: added gehan family, fixed parameters for cindex, added support for: weights, response predict type, importance, selected_features
Minor internal changes
All density learners except LearnerDensHist and LearnerDensKDE have been moved to the mlr3learners org
The following survival learners have been moved to the mlr3learners org, LearnerSurv: Flexible, ObliqueRSF, Penalized, RandomForestSRC
Bugfix in LearnerSurvXgboost previously lp was erroneously returned as exp(lp)
Now licenced under LPGL-3

LearnerSurvParametric and LearnerSurvNelson moved to mlr3learners/mlr3learners.survival repo
LearnerSurvCoxboost and LearnerSurvCVCoxboost moved to mlr3learners/mlr3learners.coxboost repo
LearnerSurvSVM moved to mlr3learners/mlr3learners.survivalsvm repo
In the next release, all learners except for LearnerSurvKaplan, LearnerSurvCoxPH, and LearnerDensHist will be moved to the mlr3learners org
Minor internal changes

Density estimation has now been added to mlr3proba, see TaskDens, LearnerDens, PredictionDens, and MeasureDens.
Added mlr_tasks_faithful and mlr_tasks_precip for density task examples
Added mlr_task_generators_simdens for generating density tasks
Added learners for density estimation, see mlr3::mlr_learners$keys("^dens") for the full list
In line with mlr3 0.1.7, public methods train_internal, predict_internal, score_internal are now private methods .train,.predict,.score
Converted to roxygen2 R6 documentation

Changed lp 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 AFTs
Moved mboost to suggests
Added response predict type, which predicts the time until event. Currently only supported for AFT models in surv.parametric
Added measures for response predict type: MeasureSurvMAE, MeasureSurvMAESE, MeasureSurvMSE, MeasureSurvMSESE, MeasureSurvRMSE, MeasureSurvRMSESE

Added method argument to integrated scores and added weighting by bin-width
Added notes to IGS documentation regarding default methods and comparison to other packages
Added method to MeasureSurvIntegrated constructor and fields
Fixed mistake in documentation of: TaskSurv, MeasureSurvUnoC
Added missing LearnerSurvRpart parameter parms and cost
Fixed errors in r-patched-solaris and r-devel debian-clang

mlr3proba 0.2.6 2020-12-04