JuliaTrustworthyAI · pat-alt · Dec 9, 2022 · Dec 9, 2022 · Dec 9, 2022
diff --git a/README.md b/README.md
@@ -5,7 +5,13 @@
 
 `ConformalPrediction.jl` is a package for Uncertainty Quantification (UQ) through Conformal Prediction (CP) in Julia. It is designed to work with supervised models trained in [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) (Blaom et al. 2020). Conformal Prediction is distribution-free, easy-to-understand, easy-to-use and model-agnostic.
 
-# 📖 Background
+## 🏃 Quick Tour
+
+> First time here? Take a quick interactive [tour](https://binder.plutojl.org/v0.19.12/open?url=https%253A%252F%252Fraw.githubusercontent.com%252Fpat-alt%252FConformalPrediction.jl%252Fmain%252Fdev%252Fquick_tour%252Fnotebook.jl) to see what this package can do.
+
+The [link](https://binder.plutojl.org/v0.19.12/open?url=https%253A%252F%252Fraw.githubusercontent.com%252Fpat-alt%252FConformalPrediction.jl%252Fmain%252Fdev%252Fquick_tour%252Fnotebook.jl) takes you to a `Pluto.jl` notebook hosted on binder.org. In my own experience, this may take a while to load, certainly enough time to get yourself a hot beverage ☕. Alternatively, you can clone this repo and run the notebook contained in `dev/quick-tour` locally or skip the tour for now and read on here.
+
+## 📖 Background
 
 Conformal Prediction is a scalable frequentist approach to uncertainty quantification and coverage control. It promises to be an easy-to-understand, distribution-free and model-agnostic way to generate statistically rigorous uncertainty estimates. Interestingly, it can even be used to complement Bayesian methods.
 
@@ -82,13 +88,13 @@ ŷ[1:show_first]
 ```
 
     5-element Vector{Tuple{Float64, Float64}}:
-     (0.325476135568554, 2.5420611849529986)
-     (-0.8093221495344456, 1.513229072277355)
-     (0.24246467414510378, 2.531848511672005)
-     (-0.37629465789570005, 1.9144457517084361)
-     (-0.5411423339519135, 1.712803571302072)
+     (0.5113539995719073, 2.7791173590180245)
+     (0.15501260477711076, 2.491986075800726)
+     (-0.32783606947941524, 1.9302674946467009)
+     (-0.13732511816023366, 2.141708832043786)
+     (0.5089900787456267, 2.7771571126470387)
 
-For simple models like this one, we can call `Plots.plot` on our instance, fit result and data to generate the chart below:
+For simple models like this one, we can call a custom `Plots` recipe on our instance, fit result and data to generate the chart below:
 
 ``` julia
 using Plots
@@ -117,13 +123,13 @@ println("SSC: $(round(_eval.measurement[2], digits=3))")
     ┌───────────────────────────────────────────────────────────┬───────────┬───────
     │ measure                                                   │ operation │ meas ⋯
     ├───────────────────────────────────────────────────────────┼───────────┼───────
-    │ emp_coverage (generic function with 1 method)             │ predict   │ 0.95 ⋯
-    │ size_stratified_coverage (generic function with 1 method) │ predict   │ 0.90 ⋯
+    │ emp_coverage (generic function with 1 method)             │ predict   │ 0.94 ⋯
+    │ size_stratified_coverage (generic function with 1 method) │ predict   │ 0.76 ⋯
     └───────────────────────────────────────────────────────────┴───────────┴───────
                                                                    3 columns omitted
 
-    Empirical coverage: 0.957
-    SSC: 0.907
+    Empirical coverage: 0.947
+    SSC: 0.762
 
 ## 🔁 Status
 

diff --git a/README_files/figure-commonmark/cell-11-output-1.svg b/README_files/figure-commonmark/cell-11-output-1.svg
diff --git a/README_files/figure-commonmark/cell-7-output-1.svg b/README_files/figure-commonmark/cell-7-output-1.svg
diff --git a/_freeze/docs/src/index/execute-results/md.json b/_freeze/docs/src/index/execute-results/md.json
diff --git a/_freeze/docs/src/index/figure-commonmark/cell-11-output-1.svg b/_freeze/docs/src/index/figure-commonmark/cell-11-output-1.svg
diff --git a/_freeze/docs/src/index/figure-commonmark/cell-7-output-1.svg b/_freeze/docs/src/index/figure-commonmark/cell-7-output-1.svg
diff --git a/docs/src/_intro.qmd b/docs/src/_intro.qmd
@@ -10,7 +10,13 @@ using ConformalPrediction
 
 `ConformalPrediction.jl` is a package for Uncertainty Quantification (UQ) through Conformal Prediction (CP) in Julia. It is designed to work with supervised models trained in [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) [@blaom2020mlj]. Conformal Prediction is distribution-free, easy-to-understand, easy-to-use and model-agnostic. 
 
-# 📖 Background
+## 🏃 Quick Tour
+
+> First time here? Take a quick interactive [tour](https://binder.plutojl.org/v0.19.12/open?url=https%253A%252F%252Fraw.githubusercontent.com%252Fpat-alt%252FConformalPrediction.jl%252Fmain%252Fdev%252Fquick_tour%252Fnotebook.jl) to see what this package can do. 
+
+The [link](https://binder.plutojl.org/v0.19.12/open?url=https%253A%252F%252Fraw.githubusercontent.com%252Fpat-alt%252FConformalPrediction.jl%252Fmain%252Fdev%252Fquick_tour%252Fnotebook.jl) takes you to a `Pluto.jl` notebook hosted on binder.org. In my own experience, this may take a while to load, certainly enough time to get yourself a hot beverage ☕. Alternatively, you can clone this repo and run the notebook contained in `dev/quick-tour` locally or skip the tour for now and read on here.
+
+## 📖 Background
 
 Conformal Prediction is a scalable frequentist approach to uncertainty quantification and coverage control. It promises to be an easy-to-understand, distribution-free and model-agnostic way to generate statistically rigorous uncertainty estimates. Interestingly, it can even be used to complement Bayesian methods.
 

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -9,7 +9,13 @@ Documentation for [ConformalPrediction.jl](https://github.com/pat-alt/ConformalP
 
 `ConformalPrediction.jl` is a package for Uncertainty Quantification (UQ) through Conformal Prediction (CP) in Julia. It is designed to work with supervised models trained in [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) (Blaom et al. 2020). Conformal Prediction is distribution-free, easy-to-understand, easy-to-use and model-agnostic.
 
-# 📖 Background
+## 🏃 Quick Tour
+
+> First time here? Take a quick interactive [tour](https://binder.plutojl.org/v0.19.12/open?url=https%253A%252F%252Fraw.githubusercontent.com%252Fpat-alt%252FConformalPrediction.jl%252Fmain%252Fdev%252Fquick_tour%252Fnotebook.jl) to see what this package can do.
+
+The [link](https://binder.plutojl.org/v0.19.12/open?url=https%253A%252F%252Fraw.githubusercontent.com%252Fpat-alt%252FConformalPrediction.jl%252Fmain%252Fdev%252Fquick_tour%252Fnotebook.jl) takes you to a `Pluto.jl` notebook hosted on binder.org. In my own experience, this may take a while to load, certainly enough time to get yourself a hot beverage ☕. Alternatively, you can clone this repo and run the notebook contained in `dev/quick-tour` locally or skip the tour for now and read on here.
+
+## 📖 Background
 
 Conformal Prediction is a scalable frequentist approach to uncertainty quantification and coverage control. It promises to be an easy-to-understand, distribution-free and model-agnostic way to generate statistically rigorous uncertainty estimates. Interestingly, it can even be used to complement Bayesian methods.
 
@@ -86,13 +92,13 @@ ŷ[1:show_first]
 ```
 
     5-element Vector{Tuple{Float64, Float64}}:
-     (0.5113539995719073, 2.7791173590180245)
-     (0.15501260477711076, 2.491986075800726)
-     (-0.32783606947941524, 1.9302674946467009)
-     (-0.13732511816023366, 2.141708832043786)
-     (0.5089900787456267, 2.7771571126470387)
+     (0.325476135568554, 2.5420611849529986)
+     (-0.8093221495344456, 1.513229072277355)
+     (0.24246467414510378, 2.531848511672005)
+     (-0.37629465789570005, 1.9144457517084361)
+     (-0.5411423339519135, 1.712803571302072)
 
-For simple models like this one, we can call `Plots.plot` on our instance, fit result and data to generate the chart below:
+For simple models like this one, we can call a custom `Plots` recipe on our instance, fit result and data to generate the chart below:
 
 ``` julia
 using Plots
@@ -121,13 +127,13 @@ println("SSC: $(round(_eval.measurement[2], digits=3))")
     ┌───────────────────────────────────────────────────────────┬───────────┬───────
     │ measure                                                   │ operation │ meas ⋯
     ├───────────────────────────────────────────────────────────┼───────────┼───────
-    │ emp_coverage (generic function with 1 method)             │ predict   │ 0.94 ⋯
-    │ size_stratified_coverage (generic function with 1 method) │ predict   │ 0.76 ⋯
+    │ emp_coverage (generic function with 1 method)             │ predict   │ 0.95 ⋯
+    │ size_stratified_coverage (generic function with 1 method) │ predict   │ 0.90 ⋯
     └───────────────────────────────────────────────────────────┴───────────┴───────
                                                                    3 columns omitted
 
-    Empirical coverage: 0.947
-    SSC: 0.762
+    Empirical coverage: 0.957
+    SSC: 0.907
 
 ## 🔁 Status
 

diff --git a/docs/src/index_files/figure-commonmark/cell-11-output-1.svg b/docs/src/index_files/figure-commonmark/cell-11-output-1.svg