mlr3proba 0.7.5

• feat: add PEM (Piece-wise Exponential Model) reduction method, via a survival => poisson regression pipeline, from Bender et al. (2018)
• feat: add na.rm parameter to msr("surv.calib_index") to avoid NaN scores
• fix: allow cloning of measures objects
• fix: survival measure labels are now printed and the obs_loss property is supported

mlr3proba 0.7.3

• feat: added new calibration measure => msr("surv.calib_index")
• refactor: 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: type = "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 (see stackoverflow question).
• 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, refactoring/simplify code, set minimum latest version from CRAN (0.7.0) in DESCRIPTION
• Add new reduction method, a survival => classification pipeline (via IPCW, Vock et al. 2016)
• Improved the way integrated survival scores (eg surv.graf) handles the times argument and the t_max (results are the same as before if the times argument is not used)
• Improved documentation of integrated survival scores and some pipelines (add references)
• Add experimental lifecycle badge for some pipelines and pipeops - 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)
• 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

• Some fixes from v0.6.5

mlr3proba 0.6.5

• Compatibility with paradox@v1.0.0
• t_max updates and fixes on surv.cindex and surv.ibrier metrics
• New methods to TaskSurv
• coxed task generator
• Lots of refactoring
• Support for discrete-time survival analysis

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 0.5.7

What's Changed

• 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
• 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
• 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).
• Fix bottlenecks in Dcalib and RCLL