readme, website, chores

.editorconfig

@@ -0,0 +1,21 @@
+# See http://editorconfig.org
+root = true
+
+[*]
+charset = utf-8
+end_of_line = lf
+insert_final_newline = true
+indent_style = space
+trim_trailing_whitespace = true
+
+[*.{r,R,md,Rmd}]
+indent_size = 2
+
+[*.{c,h}]
+indent_size = 4
+
+[*.{cpp,hpp}]
+indent_size = 4
+
+[{NEWS.md,DESCRIPTION,LICENSE}]
+max_line_length = 80

.lintr

@@ -0,0 +1,10 @@
+linters: linters_with_defaults(
+    # lintr defaults: https://lintr.r-lib.org/reference/default_linters.html
+    # the following setup changes/removes certain linters
+    assignment_linter = NULL, # do not force using <- for assignments
+    object_name_linter = object_name_linter(c("snake_case", "CamelCase")), # only allow snake case and camel case object names
+    cyclocomp_linter = NULL, # do not check function complexity
+    commented_code_linter = NULL, # allow code in comments
+    line_length_linter = line_length_linter(120L),
+    object_length_linter = object_length_linter(40L)
+    )

README.Rmd

@@ -22,7 +22,7 @@ lgr::get_logger("mlr3")$set_threshold("warn")
 
 Statistical inference methods for {mlr3}.
 
-Package website: [dev](https://mlr3inference.mlr-org.com/dev/)
+[Package website](https://mlr3inference.mlr-org.com/)
 
 <!-- badges: start -->
 [![RCMD Check](https://github.com/mlr-org/mlr3inference/actions/workflows/r-cmd-check.yml/badge.svg)](https://github.com/mlr-org/mlr3inference/actions/workflows/r-cmd-check.yml)
@@ -63,6 +63,23 @@ ci_default = msr("ci", "classif.acc")
 rr$aggregate(ci_default)
 ```
 
+With [`mlr3viz`](https://mlr3viz.mlr-org.com), it is also possible to visualize multiple confidence intervals.
+Below, we compare a random forest with a decision tree and a featureless learner:
+
+
+```{r, dpi = 300, out.width = "70%", fig.align = "center"}
+library(mlr3learners)
+library(mlr3viz)
+
+bmr = benchmark(benchmark_grid(
+  tsks(c("sonar", "german_credit")),
+  lrns(c("classif.rpart", "classif.ranger", "classif.featureless")),
+  rsmp("subsampling")
+))
+
+autoplot(bmr, "ci", msr("ci", "classif.ce"))
+```
+
 Note that:
 
 * Confidence Intervals can only be obtained for measures that are based on pointwise loss functions, i.e. have an `$obs_loss` field.

README.md

@@ -40,6 +40,8 @@ rr = resample(tsk("sonar"), lrn("classif.rpart"), rsmp("holdout"))
 # 0.05 is also the default
 ci = msr("ci.holdout", "classif.acc", alpha = 0.05)
 rr$aggregate(ci)
+#>       classif.acc classif.acc.lower classif.acc.upper 
+#>         0.7391304         0.6347628         0.8434981
 ```
 
 It is also possible to select the default inference method for a certain
@@ -48,8 +50,29 @@ It is also possible to select the default inference method for a certain
 ``` r
 ci_default = msr("ci", "classif.acc")
 rr$aggregate(ci_default)
+#>       classif.acc classif.acc.lower classif.acc.upper 
+#>         0.7391304         0.6347628         0.8434981
 ```
 
+With [`mlr3viz`](https://mlr3viz.mlr-org.com), it is also possible to
+visualize multiple confidence intervals. Below, we compare a random
+forest with a decision tree and a featureless learner:
+
+``` r
+library(mlr3learners)
+library(mlr3viz)
+
+bmr = benchmark(benchmark_grid(
+  tsks(c("sonar", "german_credit")),
+  lrns(c("classif.rpart", "classif.ranger", "classif.featureless")),
+  rsmp("subsampling")
+))
+
+autoplot(bmr, "ci", msr("ci", "classif.ce"))
+```
+
+<img src="man/figures/README-unnamed-chunk-5-1.png" width="70%" style="display: block; margin: auto;" />
+
 Note that:
 
 - Confidence Intervals can only be obtained for measures that are based

pkgdown/_pkgdown.yml

@@ -27,7 +27,7 @@ navbar:
       href: https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/
     github:
       icon: fa fa-github
-      href: https://github.com/mlr-org/mlr3fda
+      href: https://github.com/mlr-org/mlr3inference
     book:
       text: mlr3book
       icon: fa fa-link