resolve merge conflict in R\piplines.R

.Rbuildignore

@@ -23,3 +23,4 @@ README.html
 ^\.vscode$
 ^\.lintr$
 ^\.pre-commit-config\.yaml$
+^pkgdown/_pkgdown\.yml$

.github/workflows/pkgdown.yml

@@ -42,9 +42,9 @@ jobs:
         run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
         shell: Rscript {0}
 
-      - name: Deploy
+      - name: Deploy to GitHub pages 🚀
         if: github.event_name != 'pull_request'
-        uses: JamesIves/github-pages-deploy-action@v4.6.4
+        uses: JamesIves/github-pages-deploy-action@v4.7.2
         with:
           clean: false
           branch: gh-pages

.gitignore

@@ -104,4 +104,5 @@ src/*.so
 src/*.dll
 CRAN-RELEASE
 .vscode
-check/*
+check/*
+docs

DESCRIPTION

@@ -1,49 +1,25 @@
 Package: mlr3proba
 Title: Probabilistic Supervised Learning for 'mlr3'
-Version: 0.6.8
-Authors@R:
-    c(person(given = "Raphael",
-             family = "Sonabend",
-             role = "aut",
-             email = "[email protected]",
-             comment = c(ORCID = "0000-0001-9225-4654")),
-      person(given = "Franz",
-             family = "Kiraly",
-             role = "aut",
-             email = "[email protected]"),
-      person(given = "Michel",
-             family = "Lang",
-             role = "aut",
-             email = "[email protected]",
-             comment = c(ORCID = "0000-0001-9754-0393")),
-      person(given = "Nurul Ain",
-             family = "Toha",
-             role = "ctb",
-             email = "[email protected]"),
-      person(given = "Andreas",
-             family = "Bender",
-             role = "ctb",
-             email = "[email protected]",
-             comment = c(ORCID = "0000-0001-5628-8611")),
-      person(given = "John",
-             family = "Zobolas",
-             role = c("cre", "aut"),
-             email = "[email protected]",
-             comment = c(ORCID = "0000-0002-3609-8674")),
-      person(given = "Lukas",
-             family = "Burk",
-             email = "[email protected]",
-             role = "ctb",
-             comment = c(ORCID = "0000-0001-7528-3795")),
-      person(given = "Philip",
-             family = "Studener",
-             role = "aut",
-             email = "[email protected]"),
-      person(given = "Maximilian",
-             family = "Muecke",
-             email = "[email protected]",
-             role = "ctb",
-             comment = c(ORCID = "0009-0000-9432-9795")))
+Version: 0.7.4
+Authors@R: c(
+    person("Raphael", "Sonabend", , "[email protected]", role = "aut",
+           comment = c(ORCID = "0000-0001-9225-4654")),
+    person("Franz", "Kiraly", , "[email protected]", role = "aut"),
+    person("Michel", "Lang", , "[email protected]", role = "aut",
+           comment = c(ORCID = "0000-0001-9754-0393")),
+    person("Nurul Ain", "Toha", , "[email protected]", role = "ctb"),
+    person("Andreas", "Bender", , "[email protected]", role = "ctb",
+           comment = c(ORCID = "0000-0001-5628-8611")),
+    person("John", "Zobolas", , "[email protected]", role = c("cre", "aut"),
+           comment = c(ORCID = "0000-0002-3609-8674")),
+    person("Lukas", "Burk", , "[email protected]", role = "ctb",
+           comment = c(ORCID = "0000-0001-7528-3795")),
+    person("Philip", "Studener", , "[email protected]", role = "aut"),
+    person("Maximilian", "Muecke", , "[email protected]", role = "ctb",
+           comment = c(ORCID = "0009-0000-9432-9795")),
+    person("Lee Xingzhuo", "Li", , "[email protected]", role = "ctb",
+           comment = c(ORCID = "0000-0001-5259-5198"))
+  )
 Description: Provides extensions for probabilistic supervised learning for
     'mlr3'.  This includes extending the regression task to probabilistic
     and interval regression, adding a survival task, and other specialized
@@ -60,38 +36,37 @@ Imports:
     distr6 (>= 1.8.4),
     ggplot2,
     mlr3misc (>= 0.7.0),
-    mlr3viz,
+    mlr3pipelines (>= 0.7.0),
     paradox (>= 1.0.0),
     R6,
     Rcpp (>= 1.0.4),
     survival
 Suggests:
-    bujar,
+    abind,
+    coxed,
     GGally,
     knitr,
     lgr,
-    mlr3pipelines (>= 0.3.4),
+    lifecycle,
+    mlr3learners,
+    mlr3viz,
+    pammtools,
     param6 (>= 0.2.4),
+    polspline,
     pracma,
     rpart,
     set6 (>= 0.2.6),
     simsurv,
     survAUC,
-    testthat (>= 3.0.0),
-    vdiffr,
-    abind,
-    Ecdat,
-    coxed,
-    mlr3learners,
-    pammtools
+    testthat (>= 3.0.0)
 LinkingTo:
     Rcpp
 Remotes:
     xoopR/distr6,
     xoopR/param6,
     xoopR/set6
-Config/testthat/edition: 3
 ByteCompile: true
+Config/testthat/edition: 3
 Encoding: UTF-8
 LazyData: true
 NeedsCompilation: no
@@ -118,6 +93,7 @@ Collate:
     'MeasureSurvDCalibration.R'
     'MeasureSurvGraf.R'
     'MeasureSurvHungAUC.R'
+    'MeasureSurvICI.R'
     'MeasureSurvIntLogloss.R'
     'MeasureSurvLogloss.R'
     'MeasureSurvMAE.R'
@@ -138,17 +114,12 @@ Collate:
     'PipeOpCrankCompositor.R'
     'PipeOpDistrCompositor.R'
     'PipeOpPredClassifSurvDiscTime.R'
-    'PipeOpTransformer.R'
-    'PipeOpPredTransformer.R'
-    'PipeOpPredRegrSurv.R'
-    'PipeOpPredSurvRegr.R'
+    'PipeOpPredClassifSurvIPCW.R'
     'PipeOpProbregrCompositor.R'
     'PipeOpResponseCompositor.R'
     'PipeOpSurvAvg.R'
-    'PipeOpTaskRegrSurv.R'
     'PipeOpTaskSurvClassifDiscTime.R'
-    'PipeOpTaskSurvRegr.R'
-    'PipeOpTaskTransformer.R'
+    'PipeOpTaskSurvClassifIPCW.R'
     'PredictionDataDens.R'
     'PredictionDataSurv.R'
     'PredictionDens.R'
@@ -177,7 +148,6 @@ Collate:
     'mlr3proba-package.R'
     'pecs.R'
     'pipelines.R'
-    'plot.R'
     'plot_probregr.R'
     'scoring_rule_erv.R'
     'surv_measures.R'

NAMESPACE

@@ -27,10 +27,8 @@ S3method(is_missing_prediction_data,PredictionDataDens)
 S3method(is_missing_prediction_data,PredictionDataSurv)
 S3method(pecs,PredictionSurv)
 S3method(pecs,list)
-S3method(plot,LearnerSurv)
 S3method(plot,TaskDens)
 S3method(plot,TaskSurv)
-export(.c_get_unique_times)
 export(.c_weight_survival_score)
 export(.surv_return)
 export(LearnerDens)
@@ -52,6 +50,7 @@ export(MeasureSurvCindex)
 export(MeasureSurvDCalibration)
 export(MeasureSurvGraf)
 export(MeasureSurvHungAUC)
+export(MeasureSurvICI)
 export(MeasureSurvIntLogloss)
 export(MeasureSurvLogloss)
 export(MeasureSurvMAE)
@@ -72,17 +71,12 @@ export(PipeOpBreslow)
 export(PipeOpCrankCompositor)
 export(PipeOpDistrCompositor)
 export(PipeOpPredClassifSurvDiscTime)
-export(PipeOpPredRegrSurv)
-export(PipeOpPredSurvRegr)
-export(PipeOpPredTransformer)
+export(PipeOpPredClassifSurvIPCW)
 export(PipeOpProbregr)
 export(PipeOpResponseCompositor)
 export(PipeOpSurvAvg)
-export(PipeOpTaskRegrSurv)
 export(PipeOpTaskSurvClassifDiscTime)
-export(PipeOpTaskSurvRegr)
-export(PipeOpTaskTransformer)
-export(PipeOpTransformer)
+export(PipeOpTaskSurvClassifIPCW)
 export(PredictionDens)
 export(PredictionSurv)
 export(TaskDens)
@@ -99,8 +93,8 @@ export(assert_surv_matrix)
 export(breslow)
 export(get_mortality)
 export(pecs)
+export(pipeline_survtoclassif_IPCW)
 export(pipeline_survtoclassif_disctime)
-export(pipeline_survtoregr)
 export(plot_probregr)
 import(checkmate)
 import(data.table)
@@ -111,14 +105,19 @@ import(mlr3misc)
 import(paradox)
 importFrom(R6,R6Class)
 importFrom(Rcpp,sourceCpp)
-importFrom(graphics,plot)
-importFrom(mlr3viz,fortify)
-importFrom(stats,complete.cases)
+importFrom(mlr3pipelines,"%>>%")
+importFrom(mlr3pipelines,Graph)
+importFrom(mlr3pipelines,as_graph)
+importFrom(mlr3pipelines,gunion)
+importFrom(mlr3pipelines,pipeline_greplicate)
+importFrom(mlr3pipelines,po)
+importFrom(mlr3pipelines,ppl)
 importFrom(stats,density)
+importFrom(stats,median)
 importFrom(stats,model.frame)
 importFrom(stats,model.matrix)
 importFrom(stats,predict)
-importFrom(stats,reformulate)
+importFrom(stats,quantile)
 importFrom(stats,sd)
 importFrom(survival,Surv)
 importFrom(utils,data)

NEWS.md

@@ -1,26 +1,72 @@
+# 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](https://stackoverflow.com/questions/79297386/mlr3-predicted-values-for-surv-coxph-learner-with-case-weights).
+* cleanup: remove `tsk("unemployment")` and associated files
+* cleanup: remove unused references
+
+# mlr3proba 0.7.1
+
+* cleanup: removed all `PipeOp`s 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 `PipeOp`s: `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)
+* `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`
+* 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
+* Small fixes and refactoring to the discrete-time pipeops
 
 # mlr3proba 0.6.5
 
 * Add support for discrete-time survival analysis
 * New `PipeOp`s: `PipeOpTaskSurvClassifDiscTime`, `PipeOpPredClassifSurvDiscTime`
-* New pipeline: `pipeline_survtoclassif`
+* New pipeline (**reduction method**): `pipeline_survtoclassif_disctime`
 
 # mlr3proba 0.6.4