🌟 Initialize documentation

.github/workflows/documenter.yml

@@ -0,0 +1,30 @@
+name: Documentation
+
+on:
+  push:
+    branches:
+      - dev
+      - instate-docs
+    tags: '*'
+  pull_request:
+
+jobs:
+  build:
+    permissions:
+      contents: write
+      pull-requests: read
+      statuses: write
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: julia-actions/setup-julia@v2
+        with:
+          version: '1.10'
+      - uses: julia-actions/cache@v1
+      - name: Install dependencies
+        run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
+      - name: Build and deploy
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # If authenticating with GitHub Actions token
+          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # If authenticating with SSH deploy key
+        run: julia --project=docs/ docs/make.jl

docs/make.jl

@@ -25,6 +25,7 @@ makedocs(
     pages = [
         "Introduction" => "index.md",
         "Transformers" => Any[
+            "Numerical Transformers"=>"transformers/numerical.md",
             "Classical Encoders"=>"transformers/classical.md",
             "Neural-based Encoders"=>"transformers/neural.md",
             "Contrast Encoders"=>"transformers/contrast.md",

docs/src/index.md

@@ -35,16 +35,15 @@ Xnew = transform(mach, X)
 ```
 
 ## Available Transformers
-In `MLJTransforms` we define "encoders" to encompass models that specifically operate by encoding categorical variables; meanwhile, "transformers" refers to models that apply more generic transformations on columns that are not necessarily categorical. We define the following taxonomy for different models founds in `MLJTransforms`:
-
-| Genre | Definition | 
-|:----------:|:----------:|
-| **Classical Encoders** | Well known and commonly used categorical encoders | 
-| **Neural-based Encoders** | Categorical encoders based on neural networks | 
-| **Contrast Encoders** | Categorical encoders that could be modeled by a contrast matrix  | 
-| **Utility Encoders** | Categorical encoders meant to be used as preprocessors for other encoders or models | 
-| **Other Transformers** | More generic transformers that go beyond categorical encoding   | 
-
-
-
-
+In `MLJTransforms` we denote transformers that operate on columns with `Continuous` and/or `Count` [scientific types](https://juliaai.github.io/ScientificTypes.jl/dev/) as numerical transformers. Meanwhile, categorical transformers operate on `Multiclass` and/or `OrderedFactor` [scientific types](https://juliaai.github.io/ScientificTypes.jl/dev/). Most categorical transformers in this package operate by converting categorical values into numerical values or vectors, and are therefore considered categorical encoders.
+
+Based on this, we categorize the methods as follows, with further distinctions for categorical encoders:
+
+| **Category**                | **Description**                                                                 |
+|:---------------------------:|:-------------------------------------------------------------------------------:|
+| **Numerical Transformers**   | Transformers that operate on `Continuous` or `Count` columns in a given dataset.|
+| **Classical Encoders**       | Widely recognized and frequently utilized categorical encoders.                 |
+| **Neural-based Encoders**    | Categorical encoders based on neural networks.                                  |
+| **Contrast Encoders**        | Categorical encoders modeled via a contrast matrix.                             |
+| **Utility Encoders**         | Categorical encoders meant to be used as preprocessors for other encoders or models.|
+| **Other Transformers**       | Transformers that fall into other categories.                                   |

docs/src/transformers/contrast.md

@@ -5,10 +5,7 @@ Contrast Encoders include categorical encoders that could be modeled by a contra
 | [DummyEncoder](@ref) | Encodes by comparing each level to the reference level, intercept being the cell mean of the reference group | 
 | [SumEncoder](@ref) | Encodes by comparing each level to the reference level, intercept being the grand mean | 
 | [HelmertEncoder](@ref) | Encodes by comparing levels of a variable with the mean of the subsequent levels of the variable
-| 
-| [HelmertEncoder](@ref) | Encodes by comparing levels of a variable with the mean of the subsequent levels of the variable | 
 | [ForwardDifferenceEncoder](@ref) | Encodes by comparing adjacent levels of a variable (each level minus the next level)
-| 
 | [ContrastEncoder](@ref) | Allows defining a custom contrast encoder via a contrast matrix | 
 | [HypothesisEncoder](@ref) | Allows defining a custom contrast encoder via a hypothesis matrix | 
 

docs/src/transformers/neural.md

@@ -1,4 +1,4 @@
-Neural-based Encoders include ategorical encoders based on neural networks:
+Neural-based Encoders include categorical encoders based on neural networks:
 
 | Transformer | Brief Description |
 |:----------:|:----------:|

docs/src/transformers/numerical.md

@@ -0,0 +1,25 @@
+Other Transformers include more generic transformers that go beyond categorical encoding
+
+| Transformer | Brief Description | 
+|:----------:|:----------:|
+| [Standardizer](@ref) | Transforming columns of numerical features by standardization | 
+| [BoxCoxTransformer](@ref) | Transforming columns of numerical features by BoxCox transformation | 
+| [UnivariateBoxCoxTransformer](@ref) | Apply BoxCox transformation given a single vector | 
+| [InteractionTransformer](@ref) | Transforming columns of numerical features to create new interaction features |
+| [UnivariateDiscretizer](@ref) | Discretize a continuous vector into an ordered factor | 
+
+```@docs
+MLJTransforms.Standardizer
+```
+
+```@docs
+MLJTransforms.InteractionTransformer
+```
+
+```@docs
+MLJTransforms.BoxCoxTransformer
+```
+
+```@docs
+MLJTransforms.UnivariateDiscretizer
+```

docs/src/transformers/others.md

@@ -1,35 +1,14 @@
-Other Transformers include more generic transformers that go beyond categorical encoding
+Transformers that operate on columns with general or specialized scientific types.
 
 | Transformer | Brief Description | 
 |:----------:|:----------:|
-| [FillImputer](@ref) | Fill missing values of any features belonging to any scientific type | 
-| [Standardizer](@ref) | Transforming columns of numerical features by standardization | 
-| [BoxCoxTransformer](@ref) | Transforming columns of numerical features by BoxCox transformation | 
-| [InteractionTransformer](@ref) | Transforming columns of numerical features by creating new interaction features |
-| [UnivariateBoxCoxTransformer](@ref) | Apply BoxCox transformation given a single vector | 
-| [UnivariateDiscretizer](@ref) | Discretize a continuous vector into an ordered factor | 
+| [FillImputer](@ref) | Fill missing values of features belonging to any scientific type | 
 | [UnivariateTimeTypeToContinuous](@ref) | Transform a vector of time type into continuous type | 
 
 ```@docs
 MLJTransforms.FillImputer
 ```
 
-```@docs
-MLJTransforms.Standardizer
-```
-
-```@docs
-MLJTransforms.BoxCoxTransformer
-```
-
-```@docs
-MLJTransforms.InteractionTransformer
-```
-
-
-```@docs
-MLJTransforms.UnivariateDiscretizer
-```
 
 ```@docs
 MLJTransforms.UnivariateTimeTypeToContinuous

docs/src/transformers/utility.md

@@ -2,8 +2,8 @@ Utility Encoders include categorical encoders meant to be used as preprocessors
 
 | Transformer | Brief Description | 
 |:----------:|:----------:|
-| [CardinalityReducer](@ref) | Reduce cardinality of high cardinality features by grouping infrequent categories |
-| [MissingnessEncoder](@ref) | Encode missing values of categorical columns into new values |
+| [CardinalityReducer](@ref) | Reduce cardinality of high cardinality categorical features by grouping infrequent categories |
+| [MissingnessEncoder](@ref) | Encode missing values of categorical features into new values |
 
 ```@docs
 MLJTransforms.CardinalityReducer