mlr3proba 0.4.3

• Massive speed-up in distrcompositor PipeOp/pipeline
• More informative error given if $distr called for a learner that does not support this return type
• Fix massive bottleneck in scoring rule measures
• Add Density coercions as_task_dens and as_prediction_dens
• Measures now use parameter sets like learners. This streamlines the interface but unfortunately means ids can no longer be set dynamically.
• Add parameters t_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.
• Fix bug in Rcpp code that was causing erroneous values for calculating the cindex in datasets greater than 20,000 observations.

mlr3proba 0.4.2

• Patch for linux

mlr3proba 0.4.1

• Remove mlr3extralearners from Suggests
• Add response to as_prediction_surv
• Now exported a couple cpp functions and assert_surv
• mlr3 is now in Depends not imports
• distr predictions are now internally stored as matrices to significantly reduce prediction object sizes
• Tasks now support strata property

mlr3proba 0.4.0

• Deprecated measures from 0.2.0 have now been deleted.
• IPCW measures such as surv.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.
• IPCW measures such as 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.
• The 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.
• Add surv.dcalib for the D-Calibration measure from Haider et al. (2020).

mlr3proba 0.3.2

• Patched bug causing "interval2" task type not to work
• Fixed bug causing pipelines not to function correctly in $aggregate

mlr3proba 0.3.1

mlr3proba 0.3.1

• Reverted removal of "interval2"

mlr3proba 0.3.0

• Commonly used survival quantities have been added as active bindings to 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 types
• Default values have now been given to the time and event arguments in TaskSurv
• PredictionDens can now include distr return type (equivalent to learner$model)

mlr3proba 0.2.6

• Minor internal fixes

mlr3proba 0.2.5

• 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

mlr3proba 0.2.4

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

mlr3proba 0.2.3

mlr3proba 0.2.3

• 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

mlr3proba 0.2.2

• 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