Initial CRAN release

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3inferr
 Title: Inference on the Generalization Error
-Version: 0.0.0.9000
+Version: 0.1.0
 Authors@R: c(
     person("Sebastian", "Fischer", , "sebf.fischer@gmail.com", role = c("cre", "aut"),
            comment = c(ORCID = "0000-0002-9609-3197")),
@@ -43,8 +43,8 @@ Collate:
     'MeasureCIConZ.R'
     'MeasureCICorT.R'
     'MeasureCIHoldout.R'
-    'MeasureCINestedCV.R'
     'MeasureCIWaldCV.R'
+    'MeasureCiNestedCV.R'
     'ResamplingNestedCV.R'
     'ResamplingPairedSubsampling.R'
     'bibentries.R'

README.Rmd

@@ -104,6 +104,20 @@ content = content[, c("key", "label", "resamplings", "only pointwise loss")]
 knitr::kable(content, format = "markdown", col.names = tools::toTitleCase(names(content)))
 ```
 
+## Citing mlr3
+
+If we use mlr3inferr, please cite our paper:
+
+    @misc{kuempelfischer2024ciforge,
+          title={Constructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Study},
+          author={Hannah Schulz-Kümpel and Sebastian Fischer and Thomas Nagler and Anne-Laure Boulesteix and Bernd Bischl and Roman Hornung},
+          year={2024},
+          eprint={2409.18836},
+          archivePrefix={arXiv},
+          primaryClass={stat.ML},
+          url={https://arxiv.org/abs/2409.18836},
+    }
+
 
 ## Bugs, Questions, Feedback
 

README.md

@@ -89,23 +89,27 @@ Note that:
 
 ## Inference Methods
 
-``` r
-content = as.data.table(mlr3::mlr_measures, objects = TRUE)[startsWith(get("key"), "ci."),]
-content$resamplings = map(content$object, function(x) paste0(gsub("Resampling", "", x$resamplings), collapse = ", "))
-content[["only pointwise loss"]] = map_chr(content$object, function(object) {
-  if (get_private(object)$.requires_obs_loss) "yes" else "false"
-})
-content = content[, c("key", "label", "resamplings", "only pointwise loss")]
-knitr::kable(content, format = "markdown", col.names = tools::toTitleCase(names(content)))
-```
-
-| Key         | Label             | Resamplings       | Only Pointwise Loss |
-|:------------|:------------------|:------------------|:--------------------|
-| ci.con_z    | Conservative-Z CI | PairedSubsampling | false               |
-| ci.cor_t    | Corrected-T CI    | Subsampling       | false               |
-| ci.holdout  | Holdout CI        | Holdout           | yes                 |
+| Key        | Label             | Resamplings       | Only Pointwise Loss |
+|:-----------|:------------------|:------------------|:--------------------|
+| ci.con_z   | Conservative-Z CI | PairedSubsampling | false               |
+| ci.cor_t   | Corrected-T CI    | Subsampling       | false               |
+| ci.holdout | Holdout CI        | Holdout           | yes                 |
+| ci.ncv     | Nested CV CI      | NestedCV          | yes                 |
 | ci.wald_cv | Naive CV CI       | CV, LOO           | yes                 |
-| ci.ncv      | Nested CV CI      | NestedCV          | yes                 |
+
+## Citing mlr3
+
+If we use mlr3inferr, please cite our paper:
+
+    @misc{kuempelfischer2024ciforge,
+          title={Constructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Study},
+          author={Hannah Schulz-Kümpel and Sebastian Fischer and Thomas Nagler and Anne-Laure Boulesteix and Bernd Bischl and Roman Hornung},
+          year={2024},
+          eprint={2409.18836},
+          archivePrefix={arXiv},
+          primaryClass={stat.ML},
+          url={https://arxiv.org/abs/2409.18836},
+    }
 
 ## Bugs, Questions, Feedback