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mlr3proba 0.7.1

  • cleanup: removed all PipeOps and pipelines related to survival => regression reduction techniques (see #414)
  • fix: $predict_type of survtoclassif_disctime and survtoclassif_IPCW was prob (classification type) and not crank (survival type)
  • fix: G(t) is not filtered when t_max|p_max is specified in scoring rules (didn't influence evaluation at all)
  • docs: Clarified the use and impact of using t_max in scoring rules, added examples in scoring rules and AUC scores
  • feat: Added new argument remove_obs in scoring rules to remove observations with observed time t > t_max as a processing step to alleviate IPCW issues. This was before 'hard-coded' which made the Integrated Brier Score (msr("surv.graf")) differ minimally from other implementations and the original definition.

mlr3proba 0.7.0

  • Add mlr3pipelines to Imports and set minimum latest version from CRAN (0.7.0)
  • Refactor code to minimize namespace calling and imports such as mlr3pipelines:: or R6::
  • Doc updates: add experimental badge in a some PipeOps + add references in others
  • Add argument scale_lp for AFT distrcompose pipeop + respective pipeline

mlr3proba 0.6.9

  • New PipeOps: PipeOpTaskSurvClassifIPCW, PipeOpPredClassifSurvIPCW
  • New pipeline (reduction method): pipeline_survtoclassif_IPCW
  • Improved the way Integrated Brier score handles the times argument and the t_max, especially when the survival matrix has one time point (column)
  • Improved documentation of integrated survival scores
  • Improved documentation of all pipelines
  • Temp fix of math-rendering issue in package website
  • Add experimental lifecycle badge for 3 pipelines (survtoregr, distrcompositor and probregr) - these are currently either not supported by literature or tested enough.

mlr3proba 0.6.8

  • Rcpp code optimizations
  • Fixed ERV scoring to comply with mlr3 dev version (no bugs before)
  • Skipping survtoregr pipelines due to bugs (to be refactored in the future)

mlr3proba 0.6.7

  • Deprecate crank to distr composition in distrcompose pipeop (only from lp => distr works now)
  • Add get_mortality() function (from survivalmodels::surv_to_risk()
  • Add Rcpp function assert_surv_matrix()
  • Update and simplify crankcompose pipeop and respective pipeline (no response is created anymore)
  • Add responsecompositor pipeline with rmst and median

mlr3proba 0.6.6

  • Small fixes and refactoring to the discrete-time pipeops

mlr3proba 0.6.5

  • Add support for discrete-time survival analysis
  • New PipeOps: PipeOpTaskSurvClassifDiscTime, PipeOpPredClassifSurvDiscTime
  • New pipeline (reduction method): pipeline_survtoclassif_disctime

mlr3proba 0.6.4

  • Add useR! 2024 tutorial
  • Lots of refactoring, improving code quality, migration to testthat v3, etc. (thanks to @m-muecke)

mlr3proba 0.6.3

  • Add new tasks from survival package: veteran, pbc, mgus, gbsg
    • Refined docs for loaded tasks, task$help() works as it should now
    • All loaded tasks have now complete cases (no features with missing values)
  • Refined docs for task generators
  • Added task generator from coxed package
  • Added new methods for TaskSurv: cens_prop(), admin_cens_prop(), dep_cens_prop() and prop_haz()

mlr3proba 0.6.2

  • Updates in surv.cindex measure
    • added p_max (same as surv.graf)
    • refactor cutoff to t_max

mlr3proba 0.6.1

  • Compatibility with upcoming 'paradox' release.
  • Fix bug when applying t_max in surv.graf and similar time-integrated scores.

mlr3proba 0.6.0

  • Optimized surv.logloss and calib_alpha measures (bypassing distr6)
  • Update/refine all measure docs (naming conventions from upcoming scoring rules paper) + doc templates
  • fix very rare bugs in calib_alpha, surv.logloss and surv.graf (version with proper = FALSE)

mlr3proba 0.5.9

  • Fix several old issues (#348, #301, #281)
  • distrcompositor and crankcompositor deprecated functions were completely removed

mlr3proba 0.5.8

  • Fix Breslow issues (phash warning and Inf lp predictions)

mlr3proba 0.5.7

  • Add breslow function for estimating the cumulative baseline hazard of proportional hazard models
  • Add PipeOpBreslow to wrap a survival learner and generate distr predictions from lp predictions
  • Add option breslow estimator option in distrcompositor

mlr3proba 0.5.6

  • Add extend_quantile to autoplot.PredictionSurv for type = "dcalib", which imputes NAs with the maximum observed survival time
  • Fixes default in autoplot.PredictionSurv, now "calib"
  • Update msr("surv.dcalib") default for truncate to Inf

mlr3proba 0.5.5

  • Add $reverse() method to TaskSurv, which returns the same task but with 1-status.
  • Add reverse parameter to TaskSurv$kaplan() method, which calculates Kaplan-Meier on the censoring distribution of the task (1-status).

mlr3proba 0.5.4

  • Fix bottlenecks in Dcalib and RCLL

mlr3proba 0.5.3

  • Add support for learners that can predict multiple posterior distributions by using distr6::Arrdist

mlr3proba 0.5.2

  • Add plot_probregr for plotting probabilistic regression distribution predictions
  • Fix big bug in surv.rcll creating erroneous results as distributions were not being subsetted correctly

mlr3proba 0.5.1

  • Add regr.logloss

mlr3proba 0.5.0

  • Possibly small breaking change, renamed PipeOpProbregrCompositor to PipeOpProbregr and default distribution now "Uniform".
  • Renamed probregrcompositor pipeline to probregr and default distribution now "Uniform".
  • Bumped minor version to highlight all fixes in measures below
  • Bugfix in setting Uno's AUC parameters
  • Bugfix in RCLL when no censoring
  • Bugfix in all learners when making single predictions

mlr3proba 0.4.17

  • Fix bug in surv.rcll when individual scores are NA.

mlr3proba 0.4.16

  • Remove theme_mlr3 from plots.
  • Fix bug in surv.logloss when using IPCW = TRUE

mlr3proba 0.4.15

  • Fix bug in surv.cindex caused when probability of censoring was 0. Added eps parameter to control this.

mlr3proba 0.4.14

  • Fix bug in converting distr6 distributions to matrices when creating PredictionSurv
  • Fix bug in RCLL

mlr3proba 0.4.13

  • Fix minor bug in 'SG' cindex method

mlr3proba 0.4.12

  • Fix bug in survavg pipeline causing unequal weights not to be applied

mlr3proba 0.4.11

  • Fix minor bug in scoring rules

mlr3proba 0.4.10

  • Added ERV parameter to scoring rule measures to return more interpretable scoring rules. Explained Residual Variation is the percentage decrease between a scoring rule comparing a Kaplan-Meier baseline to the learner of interest.

mlr3proba 0.4.9

  • Fixed bug in surv.logloss causing IPCW weighting to not be applied correctly

mlr3proba 0.4.8

  • Bug fixes in AUC measures

mlr3proba 0.4.7

  • Add right-censored log loss
  • Fix bug in {rpart} where model was being discarded when set to be kept. Parameter model now called keep_model.

mlr3proba 0.4.6

  • Patch for upstream breakages
  • Add TaskSurv$kaplan method
  • {survivalmodels} now imported (previously suggested)

mlr3proba 0.4.5

  • Improved reduction from survival matrix predictions to ranking predictions
  • Fixed cindex bug when all predictions equal
  • Fix for valgrind

mlr3proba 0.4.4

  • Minor change to how distributions are created to better support improper distributions
  • Fixed bug in simsurv task that made it impossible to predict the target

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

  • 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

  • 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

mlr3proba 0.2.1

  • Removed PredictionRegr causing masking issues with {mlr3}
  • Bug fix in PipeOpDistrCompositor causing some cdf predictions to be lost
  • Internal fixes for mlr3pipelines: public train and predict methods to private
  • Added four datasets and tasks: grace, actg, gbcs, whas
  • Add overwrite to crankcompositor pipeop and pipeline
  • Bug fix in surv.kaplan crank prediction

mlr3proba 0.2.0

Added Functionality

  • 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.

Deprecated Functionality

  • 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.

Edited Functionality

  • 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

Moved Functionality

mlr3proba 0.1.6

  • 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

mlr3proba 0.1.5

  • 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

mlr3proba 0.1.4

  • 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

mlr3proba 0.1.3

  • 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

mlr3proba 0.1.2

  • Fixed error in r-patched-solaris
  • Added mode option to crankcompositor
  • Fixes bug resulting from R62S3 incompatibility

mlr3proba 0.1.1

  • 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 0.1.0

  • Initial upload to CRAN.