Skip to contents

Changelog

Source: NEWS.md

mlr3proba 0.8.4

  • Add autoplot for surv.coxph learner

mlr3proba 0.8.3

  • Moved all density learners from mlr3extralearners here.

mlr3proba 0.8.2

Breaking Changes

  • Removed all competing risks support, we now have a separate package in the mlr3 universe for it, see mlr3cmprsk.

New Features

  • Add default_fallback()
  • Add importance, weights and selected_features properties in Kaplan-Meier for compatibility with other survival learners when it is used as a fallback learner
  • The pipeops PipeOpTaskSurvRegrPEM and PipeOpTaskSurvClassifDiscTime now apply the data transformation on an internal validation task if it exists. This enables the use of e.g xgboost regression or classification learners with early_stopping on the corresponding pipelines.

Other

  • coxed package was removed from CRAN so now we install the latest working CRAN version (0.3.3) from GitHub
  • Renamed .surv_return() to surv_return() as it is exported and used by other packages (e.g. mlr3extralearners)
  • Update mlr3 to v1.2.0 and mlr3misc to v0.19.0

mlr3proba 0.8.1

  • feat: surv.logloss and surv.rcll now use linear interpolation of S(t) to calculate the density f(t)
  • fix: surv.mae/surv.mse/surv.rmse scores return NA when test set has only censored observations
  • fix: fix bug in msr(surv.brier) that resulted in 0 division instead of eps division (Inf values are filtered out so this was kinda masking the inflation of ISBS)
  • refactor: remove se argument from most of the scores (not practically used)
  • refactor: remove method argument from integrated survival scores (the previous default, method = 2, time-weighted integration, is now always used)
  • BREACKING CHANGE: we removed all experimental proper scoring rules (and remove_obs argument). Scores yield the same results as before with the default option proper = FALSE
  • refactor: all private functions start with . now and are adequately (privately) documented. Code was refactored for clarity
  • refactor: all internal Rcpp measure functions
  • refine doc in lots of measures

mlr3proba 0.8.0

  • Compatibility with mlr3 v1.0.0 (weights_learner) and mlr3pipelines v0.8.0
  • fix: allow cloning of measure objects
  • fix: as.data.table() for PredictionSurv objects holds now one survival curve per observation as it should
  • refactor: TaskSurv uses only right, left or interval censoring, simplified code a lot in the methods
  • feat: add TaskCompRisks class and as_task_cmprk() S3 methods (support for right-censored data only)
  • feat: New PipeOps: PipeOpTaskSurvRegrPEM, PipeOpPredRegrPEM
  • feat: New pipeline (reduction method): pipeline_survtoregr_pem

mlr3proba 0.7.4

  • fix + update MeasureSurv: survival measure labels are now printed and the obs_loss property is now supported
  • feat: add na.rm parameter to msr("surv.calib_index") to avoid NaN scores

mlr3proba 0.7.3

  • feat: added new calibration measure => msr("surv.calib_index")
  • refactor + feat: autoplot.PredictionSurv
    • The default "calib" plot uses the survival matrix directly now which is faster
    • "dcalib" has extra barplot + better documentation
    • Added new type = "scalib" which constructs the smoothed calibration plots as in Austin et al. (2020)
    • BREAKING CHANGE: "preds" is now called "isd" (individual survival distribution). row_ids can now be used to filter the observations for which you draw the survival curves.

mlr3proba 0.7.2

  • fix: lrn("surv.coxph") is now trained with model=TRUE which fixes an issue with using observation weights stackoverflow link.
  • cleanup: remove tsk("unemployment") and associated files
  • cleanup: remove unused references

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

CRAN release: 2022-04-25

  • 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

CRAN release: 2022-02-16

  • 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

CRAN release: 2022-01-22

  • 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

CRAN release: 2021-10-22

  • Patch for linux

mlr3proba 0.4.1

CRAN release: 2021-09-07

  • 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

CRAN release: 2021-04-18

  • 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

CRAN release: 2021-03-15

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

mlr3proba 0.3.1

CRAN release: 2021-02-03

  • Reverted removal of "interval2"

mlr3proba 0.3.0

CRAN release: 2021-02-02

  • 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

CRAN release: 2020-12-04

  • Minor internal fixes

mlr3proba 0.2.5

CRAN release: 2020-11-18

  • 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

CRAN release: 2020-11-11

  • 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

CRAN release: 2020-11-01

  • 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

CRAN release: 2020-09-23

  • 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

CRAN release: 2020-08-28

  • 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

CRAN release: 2020-07-25

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

CRAN release: 2020-06-05

  • 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

CRAN release: 2020-04-16

  • 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

CRAN release: 2020-03-20

  • 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

CRAN release: 2020-02-20

  • 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

CRAN release: 2020-01-31

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

mlr3proba 0.1.1

CRAN release: 2020-01-08

  • 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

CRAN release: 2019-12-22

  • Initial upload to CRAN.