add note about dcalib use

Author: RaphaelS1

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3proba
 Title: Probabilistic Supervised Learning for 'mlr3'
-Version: 0.4.0.9000
+Version: 0.4.0
 Authors@R:
     c(person(given = "Raphael",
              family = "Sonabend",

NEWS.md

@@ -1,14 +1,11 @@
-# mlr3proba 0.4.0.9000
-
-- Internal changes only.
-
 
 # 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
 

R/MeasureSurvDCalibration.R

@@ -19,6 +19,10 @@
 #' is well-calibration. If `chisq = FALSE` and `m` is the predicted value then you can manually
 #' compute the p.value with `pchisq(m, B - 1, lower.tail = FALSE)`.
 #'
+#' NOTE: This measure is still experimental both theoretically and in implementation. Results
+#' should therefore only be taken as an indicator of performance and not for
+#' conclusive judgements about model calibration.
+#'
 #' @references
 #' `r format_bib("haider_2020")`
 #'