🌟 Initialize documentation

docs/make.jl

@@ -31,6 +31,7 @@ makedocs(
             "Contrast Encoders"=>"transformers/contrast.md",
             "Utility Encoders"=>"transformers/utility.md",
             "Other Transformers"=>"transformers/others.md",
+            "API Index" => "transformers/all_transformers.md",
         ],
         "Extended Examples" => Any[
             "Tutorial A" => "tutorials/T1.md",

docs/src/index.md

@@ -1,6 +1,6 @@
 # MLJTransforms.jl
 
-A Julia package providing a wide range of categorical encoders and transformers to be used with the [MLJ](https://juliaai.github.io/MLJ.jl/dev/) package.
+A Julia package providing a wide range of categorical encoders and transformers to be used with the [MLJ](https://juliaai.github.io/MLJ.jl/dev/) package. Transformers help convert raw features into a representation that's better suited for downstream models. Meanwhile, categorical encoders are a type of transformer that specifically encodes categorical features into numerical forms. 
 
 ## Installation
 
@@ -24,9 +24,11 @@ X = RDatasets.dataset("HSAUR", "Forbes2000");
 # 2. Load the model
 FrequencyEncoder = @load FrequencyEncoder pkg="MLJTransforms"
 encoder = FrequencyEncoder(
-    features=[:Country, :Category], 
-    ignore=false, ordered_factor = false, 
-    normalize=true)
+    features=[:Country, :Category],     # The categorical columns to select
+    ignore=false,                       # Whether to exclude or include selected columns
+    ordered_factor = false,             # Whether to also encode columns of ordered factor elements
+    normalize=true                      # Whether to normalize the frequencies used for encoding
+    )
 
 
 # 3. Wrap it in a machine and fit
@@ -35,15 +37,16 @@ Xnew = transform(mach, X)
 ```
 
 ## Available Transformers
-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.
+In `MLJTransforms` we denote transformers that can 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:
+Based on this, we categorize the methods in this package 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.                                   |
+| [Numerical Transformers](transformers/numerical)   | Transformers that operate on `Continuous` or `Count` columns in a given dataset.|
+| [Classical Encoders](transformers/classical.md)       | Traditional categorical encoding algorithms and techniques.                 |
+| [Neural-based Encoders](transformers/neural)    | Categorical encoders based on neural networks.                                  |
+| [Contrast Encoders](transformers/contrast.md)        | Categorical encoders that could be modeled via a contrast matrix.                             |
+| [Utility Encoders](transformers/utility.md)         | Categorical encoders meant to be used as preprocessors for other transformers or models.|
+| [Other Transformers](transformers/others.md)       | Transformers that operate on scientific types that are neither `Finite` nor `Infinite`                                 |
+

docs/src/transformers/all_transformers.md

@@ -0,0 +1,80 @@
+| 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 | 
+| [FillImputer](@ref) | Fill missing values of features belonging to any scientific type | 
+| [UnivariateTimeTypeToContinuous](@ref) | Transform a vector of time type into continuous type | 
+| [OneHotEncoder](@ref) | Encode categorical variables into one-hot vectors | 
+| [ContinuousEncoder](@ref) | Adds type casting functionality to OnehotEncoder | 
+| [OrdinalEncoder](@ref) | Encode categorical variables into ordered integers | 
+| [FrequencyEncoder](@ref) | Encode categorical variables into their normalized or unormalized frequencies | 
+| [TargetEncoder](@ref) | Encode categorical variables into relevant target statistics | 
+| [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
+| [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 | 
+| [EntityEmbedders](@ref) | Encode categorical variables into dense embedding vectors |
+| [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; canonical = false
+MLJTransforms.Standardizer
+```
+
+```@docs; canonical = false
+MLJTransforms.InteractionTransformer
+```
+
+```@docs; canonical = false
+MLJTransforms.BoxCoxTransformer
+```
+
+```@docs; canonical = false
+MLJTransforms.UnivariateDiscretizer
+```
+
+```@docs; canonical = false
+MLJTransforms.FillImputer
+```
+
+```@docs; canonical = false
+MLJTransforms.UnivariateTimeTypeToContinuous
+```
+
+```@docs; canonical = false
+MLJTransforms.OneHotEncoder
+```
+
+```@docs; canonical = false
+MLJTransforms.ContinuousEncoder
+```
+
+```@docs; canonical = false
+MLJTransforms.OrdinalEncoder
+```
+
+```@docs; canonical = false
+MLJTransforms.FrequencyEncoder
+```
+
+```@docs; canonical = false
+MLJTransforms.TargetEncoder
+```
+
+```@docs; canonical = false
+MLJTransforms.ContrastEncoder
+```
+
+```@docs; canonical = false
+MLJTransforms.CardinalityReducer
+```
+
+```@docs; canonical = false
+MLJTransforms.MissingnessEncoder
+```

docs/src/transformers/neural.md

@@ -2,7 +2,7 @@ Neural-based Encoders include categorical encoders based on neural networks:
 
 | Transformer | Brief Description |
 |:----------:|:----------:|
-| [EntityEmbedders](@ref) | Encode categorical variables into dense embedding vectors |
+| [EntityEmbedder](@ref) | Encode categorical variables into dense embedding vectors |
 
 
 Entity Embedder docstring will go here.

docs/src/transformers/numerical.md

@@ -7,6 +7,7 @@ Other Transformers include more generic transformers that go beyond categorical
 | [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 | 
+| [FillImputer](@ref) | Fill missing values of features belonging to any finite or infinite scientific type | 
 
 ```@docs
 MLJTransforms.Standardizer
@@ -23,3 +24,7 @@ MLJTransforms.BoxCoxTransformer
 ```@docs
 MLJTransforms.UnivariateDiscretizer
 ```
+
+```@docs
+MLJTransforms.FillImputer
+```

docs/src/transformers/others.md

@@ -1,14 +1,9 @@
-Transformers that operate on columns with general or specialized scientific types.
+ransformers that operate on scientific types that are neither `Finite` nor `Infinite`.
 
 | Transformer | Brief Description | 
 |:----------:|:----------:|
-| [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.UnivariateTimeTypeToContinuous