diff --git a/docs/cookbook_versioned_docs/version-2.0.0/structure.md b/docs/cookbook_versioned_docs/version-2.0.0/structure.md
index e9b28c0..3850237 100644
--- a/docs/cookbook_versioned_docs/version-2.0.0/structure.md
+++ b/docs/cookbook_versioned_docs/version-2.0.0/structure.md
@@ -86,7 +86,7 @@ classDiagram
 
 \
 \
-The classifiers themselves, except `LR`, all inherit from a base class called [`BaseClassifier`](/docs/base/baseclassifier) in the `base` module:
+The classifiers themselves all inherit from a base class called [`BaseClassifier`](/docs/base/baseclassifier) in the `base` module:
 
 ```mermaid
 classDiagram
@@ -108,6 +108,10 @@ classDiagram
         ...
     }
 
+    class LR {
+        ...
+    }
+
     class PLSDA {
         ...
     }
@@ -141,7 +145,13 @@ classDiagram
         __init__(...)
     }
 
+    class PCADataModel {
+        +array_scores: ndarray
+        __init__(..., array_scores)
+    }
+
     BaseDataModel *-- LLDFDataModel
+    BaseDataModel *-- PCADataModel
 ```
 
-This allows all the classifiers to use the `LLDF` data, or any other type of data as long as it follows the `BaseDataModel` template.
\ No newline at end of file
+This allows all the classifiers to use the `LLDF` data, dimension-reduced `PCA` data, or any other type of data as long as it follows the `BaseDataModel` template.
\ No newline at end of file
diff --git a/docs/cookbook_versioned_docs/version-2.1.0/case-study-classifier.md b/docs/cookbook_versioned_docs/version-2.1.0/case-study-classifier.md
deleted file mode 100644
index 8b1d3d3..0000000
--- a/docs/cookbook_versioned_docs/version-2.1.0/case-study-classifier.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-sidebar_position: 3
----
-
-# Case study: training a classifier from lab data
-
-:::note
-This case study is still **under construction**.
-:::
\ No newline at end of file
diff --git a/docs/cookbook_versioned_docs/version-2.1.0/case-study-hybrid.md b/docs/cookbook_versioned_docs/version-2.1.0/case-study-hybrid.md
deleted file mode 100644
index a7c4c5d..0000000
--- a/docs/cookbook_versioned_docs/version-2.1.0/case-study-hybrid.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-sidebar_position: 4
----
-
-# Case study: hybrid workflow
-
-:::note
-This case study is still **under construction**.
-:::
\ No newline at end of file
diff --git a/docs/cookbook_versioned_docs/version-2.1.0/case-study-realtime.md b/docs/cookbook_versioned_docs/version-2.1.0/case-study-realtime.md
deleted file mode 100644
index 8725eac..0000000
--- a/docs/cookbook_versioned_docs/version-2.1.0/case-study-realtime.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-sidebar_position: 5
----
-
-# Case study: real-time data classification
-
-:::note
-This case study is still **under construction**.
-:::
\ No newline at end of file
diff --git a/docs/cookbook_versioned_docs/version-2.1.0/introduction.md b/docs/cookbook_versioned_docs/version-2.1.0/introduction.md
deleted file mode 100644
index 3a4d2ef..0000000
--- a/docs/cookbook_versioned_docs/version-2.1.0/introduction.md
+++ /dev/null
@@ -1,26 +0,0 @@
----
-sidebar_position: 1
----
-
-# The ChemFuseKit Cookbook: an introduction
-
-*What is a cookbook, exactly?*
-
-> A cookbook is a comprehensive collection of recipes that guide users through
-the process of learning and mastering the use of a specific library or
-programming technique, by providing step-by-step instructions, explanations and
-examples.
-
-## What you'll learn
-
-In this cookbook you will learn the basic principles of operation of `ChemFuseKit` through practical examples and case studies. You will be shown that all modules follow a basic structure, and once you've learned it for one module, you will be able to reapply that knowledge for all modules.
-
-You will be shown how to use the library on its own, and also how to use it as a part of a bigger pipeline.
-
-## Cookbook sectioning
-
-Here we go:
-
-- first of all, you will be shown the basic principles and structure;
-- then, you will be shown three case studies;
-- finally, you'll receive instructions on how to modify and expand this library for your own purposes.
\ No newline at end of file
diff --git a/docs/cookbook_versioned_docs/version-2.1.0/structure.md b/docs/cookbook_versioned_docs/version-2.1.0/structure.md
deleted file mode 100644
index a1ec5d5..0000000
--- a/docs/cookbook_versioned_docs/version-2.1.0/structure.md
+++ /dev/null
@@ -1,44 +0,0 @@
----
-sidebar_position: 2
----
-
-# Project structure
-
-In this cookbook page, you will be shown how the project is structured, and the purpose of each module.
-
-## Project Hierarchy
-
-```
-chemfusekit
- |
- |＿lda
- |  |＿LDASettings
- |  |＿LDA
- |
- |＿lr
- |  |＿LRSettings
- |  |＿LR
- |
- |＿plsda
- |  |＿PLSDASettings
- |  |＿PLSDA
- | 
- |＿pca
- |  |＿PCASettings
- |  |＿PCA
- |
- |＿lldf
- |  |＿LLDFSettings
- |  |＿LLDF
- |  |＿LLDFModel
- |
- |＿svm
- |  |＿SVMSettings
- |  |＿SVM
- |
- |＿knn
-    |＿KNNSettings
-    |＿KNN
-```
-
-As you can see, each module contains a class with the same name of the module, and a settings class. That's because this project tries to be as modular and as regular as possible, for clarity and interoperability.
\ No newline at end of file
diff --git a/docs/cookbook_versioned_sidebars/version-2.1.0-sidebars.json b/docs/cookbook_versioned_sidebars/version-2.1.0-sidebars.json
deleted file mode 100644
index caea0c0..0000000
--- a/docs/cookbook_versioned_sidebars/version-2.1.0-sidebars.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "tutorialSidebar": [
-    {
-      "type": "autogenerated",
-      "dirName": "."
-    }
-  ]
-}
diff --git a/docs/cookbook_versions.json b/docs/cookbook_versions.json
index dc236fe..3aea034 100644
--- a/docs/cookbook_versions.json
+++ b/docs/cookbook_versions.json
@@ -1,4 +1,3 @@
 [
-  "2.1.0",
   "2.0.0"
 ]
diff --git a/docs/versioned_docs/version-2.0.0/base/baseclassifier.md b/docs/versioned_docs/version-2.0.0/base/baseclassifier.md
index d633359..9c80bcc 100644
--- a/docs/versioned_docs/version-2.0.0/base/baseclassifier.md
+++ b/docs/versioned_docs/version-2.0.0/base/baseclassifier.md
@@ -2,7 +2,7 @@
 sidebar_position: 1
 ---
 
-# KNN class
+# BaseClassifier class
 
 A base class from which all classifiers inherit.
 
@@ -21,13 +21,13 @@ BaseClassifier(settings: BaseSettings, data: BaseDataModel)
 ## Fields
 
 - `settings`: object of type [`KNNSettings`](/tesi/docs/knn/knnsettings). Contains the settings for
-  the `KNN` object. 
+  the `BaseClassifier` object. 
 - `data`: object of type [`BaseDataModel`](../base/basedatamodel.md). Contains the data to be analyzed.
 - `model`: a `sklearn` model from `scikit-learn`. Defaults to `None`.
 
 ## Methods
 
-- `knn(self)`: trains the k-Neighbors Analysis model
-- `predict(self, x_data)`: performs LDA prediction once the model is trained.
+- `import_model(import_path: str)`: loads a model from file
+- `export_model(export_path: str)`: exports a model to file
   - *raises*:
-    - `RuntimeError("The kNN model is not trained yet!")` if the `KNN` model hasn't been trained yet
+    - `RuntimeError("You haven't trained the model yet! You cannot export it now.")` when trying to export an untrained model
diff --git a/docs/versioned_docs/version-2.0.0/lldf/index.mdx b/docs/versioned_docs/version-2.0.0/lldf/index.mdx
index 4e0508d..01c1d16 100644
--- a/docs/versioned_docs/version-2.0.0/lldf/index.mdx
+++ b/docs/versioned_docs/version-2.0.0/lldf/index.mdx
@@ -27,14 +27,14 @@ classDiagram
     class LLDF {
         +LLDFSettings settings
         +Table[] tables
-        +LLDFModel | None fused_data
+        +LLDFDataModel | None fused_data
         lldf()
         -_snv()
         +export_data(export_path: str)
         __init__(settings, tables[])
     }
 
-    class LLDFModel {
+    class LLDFDataModel {
         +x_data: pd.DataFrame
         +x_train: pd.DataFrame
         +y: np.ndarray
diff --git a/docs/versioned_docs/version-2.0.0/lldf/lldfmodel.md b/docs/versioned_docs/version-2.0.0/lldf/lldfmodel.md
index 91bf654..7232264 100644
--- a/docs/versioned_docs/version-2.0.0/lldf/lldfmodel.md
+++ b/docs/versioned_docs/version-2.0.0/lldf/lldfmodel.md
@@ -11,7 +11,7 @@ It inherits from the [`BaseDataModel`](../base/basedatamodel.md).
 ## Syntax
 
 ```python
-LLDFModel(x_data: pd.DataFrame, x_train: pd.DataFrame, y: pd.DataFrame)
+LLDFModel(x_data: pd.DataFrame, x_train: pd.DataFrame, y: np.ndarray)
 ```
 
 ## Fields and constructor parameters
@@ -19,6 +19,7 @@ LLDFModel(x_data: pd.DataFrame, x_train: pd.DataFrame, y: pd.DataFrame)
 The first two are `Pandas` `DataFrame` objects:
 - `x_data`
 - `x_train`
+
 The last is a `NumPy` `ndarray`:
 - `y`
 
diff --git a/docs/versioned_docs/version-2.1.0/pca/pcadatamodel.md b/docs/versioned_docs/version-2.0.0/pca/pcadatamodel.md
similarity index 100%
rename from docs/versioned_docs/version-2.1.0/pca/pcadatamodel.md
rename to docs/versioned_docs/version-2.0.0/pca/pcadatamodel.md
diff --git a/docs/versioned_docs/version-2.1.0/base/_category_.json b/docs/versioned_docs/version-2.1.0/base/_category_.json
deleted file mode 100644
index ac3f2d9..0000000
--- a/docs/versioned_docs/version-2.1.0/base/_category_.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "label": "Base module",
-  "position": 9,
-  "link": {
-    "type": "generated-index",
-    "description": "A module containing base classes for all the other modules."
-  }
-}
diff --git a/docs/versioned_docs/version-2.1.0/base/baseclassifier.md b/docs/versioned_docs/version-2.1.0/base/baseclassifier.md
deleted file mode 100644
index 9c80bcc..0000000
--- a/docs/versioned_docs/version-2.1.0/base/baseclassifier.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-sidebar_position: 1
----
-
-# BaseClassifier class
-
-A base class from which all classifiers inherit.
-
-## Syntax
-
-```python
-BaseClassifier(settings: BaseSettings, data: BaseDataModel)
-```
-
-## Constructor parameters
-
-- `settings`: object of type [`BaseSettings`](./basesettings.md). Contains the settings for
-  the `BaseClassifier` object.
-- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md). Contains the data to be analyzed.
-
-## Fields
-
-- `settings`: object of type [`KNNSettings`](/tesi/docs/knn/knnsettings). Contains the settings for
-  the `BaseClassifier` object. 
-- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md). Contains the data to be analyzed.
-- `model`: a `sklearn` model from `scikit-learn`. Defaults to `None`.
-
-## Methods
-
-- `import_model(import_path: str)`: loads a model from file
-- `export_model(export_path: str)`: exports a model to file
-  - *raises*:
-    - `RuntimeError("You haven't trained the model yet! You cannot export it now.")` when trying to export an untrained model
diff --git a/docs/versioned_docs/version-2.1.0/base/basedatamodel.md b/docs/versioned_docs/version-2.1.0/base/basedatamodel.md
deleted file mode 100644
index 727c520..0000000
--- a/docs/versioned_docs/version-2.1.0/base/basedatamodel.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-sidebar_position: 3
----
-
-# BaseDataModel class
-
-This class models the output data for all data-outputting operations (currently, the [`LLDF`](../lldf/lldf-class.md) operation and the [`PCA`](../pca/pca.md) operation).
-
-## Syntax
-
-```python
-BaseDataModel(x_data: pd.DataFrame, x_train: pd.DataFrame, y: pd.DataFrame)
-```
-
-## Fields and constructor parameters
-
-The first two are `Pandas` `DataFrame` objects:
-- `x_data`
-- `x_train`
-The last is a `NumPy` `ndarray`:
-- `y`
diff --git a/docs/versioned_docs/version-2.1.0/base/basesettings.md b/docs/versioned_docs/version-2.1.0/base/basesettings.md
deleted file mode 100644
index 740581b..0000000
--- a/docs/versioned_docs/version-2.1.0/base/basesettings.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-sidebar-position: 1
----
-
-# BaseSettings class
-
-Holds the settings for all classifier object. It's not meant for direct usage, only for inheritance.
-
-
-## Syntax
-
-```python
-BaseSettings(output: GraphMode, test_split: false)
-```
-
-## Fields and constructor parameters
-- `output`: toggles graph output mode. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
-- `test_split`: toggles the training split test phase. Defaults to `False`. Requires `output` to be set to `True` to work.
-
-The constructor raises:
-- `Warning("You selected test_split but it won't run because you disabled the output.")` if `test_split` is run with `output` set to false (split tests only produce graphical output, and are useless when run with disabled output).
diff --git a/docs/versioned_docs/version-2.1.0/complete-workflow.md b/docs/versioned_docs/version-2.1.0/complete-workflow.md
deleted file mode 100644
index f607520..0000000
--- a/docs/versioned_docs/version-2.1.0/complete-workflow.md
+++ /dev/null
@@ -1,31 +0,0 @@
----
-sidebar-position: 7
----
-
-# Complete workflow
-
-Here's a sequence diagram to represent an example workflow, from the raw data
-tables to classification, including data fusion, PCA and training.
-
-```plantuml
-actor User
-participant LLDF
-participant PCA
-participant Classifier
-
-User -> LLDF : Upload training tables
-User -> LLDF : Set parameters
-User -> Classifier : (optional) Upload model
-
-LLDF -> PCA  : Pass preprocessed / fused tables
-LLDF --> User  : Download fused tables
-LLDF -> Classifier  : Pass preprocessed / fused tables \nRun classification
-PCA -> Classifier : (optional) Set number of components
-
-Classifier --> User : classification results, graphs
-PCA --> User : classification results, graphs
-Classifier --> User : (optional) download trained model
-
-User -> Classifier : pass data to classify
-Classifier --> User : classification results
-```
diff --git a/docs/versioned_docs/version-2.1.0/knn/_category_.json b/docs/versioned_docs/version-2.1.0/knn/_category_.json
deleted file mode 100644
index e670264..0000000
--- a/docs/versioned_docs/version-2.1.0/knn/_category_.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "label": "kNN module",
-  "position": 6,
-  "link": {
-    "type": "generated-index",
-    "description": "A module for k-nearest neighbors analysis."
-  }
-}
diff --git a/docs/versioned_docs/version-2.1.0/knn/knn.md b/docs/versioned_docs/version-2.1.0/knn/knn.md
deleted file mode 100644
index 2cecad5..0000000
--- a/docs/versioned_docs/version-2.1.0/knn/knn.md
+++ /dev/null
@@ -1,47 +0,0 @@
----
-sidebar_position: 1
----
-
-# KNN class
-
-A class to store the data, methods and artifacts for _k-Nearest Neighbors Analysis_.
-
-## Syntax
-
-```python
-KNN(settings: KNNSettings, data: LLDFModel)
-```
-
-## Constructor parameters
-
-- `settings`: object of type [`KNNSettings`](knnsettings.md). Contains the settings for
-  the `KNN` object.
-- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md). Contains the data to be analyzed.
-
-## Fields
-
-- `settings`: object of type [`KNNSettings`](/tesi/docs/knn/knnsettings). Contains the settings for
-  the `KNN` object. 
-- `fused_data`: onject of type ['LLDFModel`](/tesi/docs/lldf/lldfmodel). Contains the
-  artifacts from the data fusion process.
-- `model`: a `KNeighborsClassifier` model from `scikit-learn`. Defaults to `None`.
-
-## Methods
-
-- `knn(self)`: trains the k-Neighbors Analysis model
-- `predict(self, x_data)`: performs LDA prediction once the model is trained.
-  - *raises*:
-    - `RuntimeError("The kNN model is not trained yet!")` if the `KNN` model hasn't been trained yet
-
-## Example
-
-```python
-from chemfusekit.knn import KNN
-
-# Initialize and run the LDA class
-knn = KNN(settings, lldf.fused_data)
-knn.knn()
-
-# Run predictions
-knn.predict(x_data)
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/knn/knnsettings.md b/docs/versioned_docs/version-2.1.0/knn/knnsettings.md
deleted file mode 100644
index 81f9486..0000000
--- a/docs/versioned_docs/version-2.1.0/knn/knnsettings.md
+++ /dev/null
@@ -1,64 +0,0 @@
----
-sidebar-position: 1
----
-
-# KNNSettings class
-
-Holds the settings for the [`KNN`](knn.md) object.
-
-Inherits from [`BaseSettings`](../base/basesettings.md).
-
-## Syntax
-
-```python
-KNNSettings(
-  n_neighbors: int,
-  metric: str | Callable,
-  weights: str | Callable,
-  algorithm: str,
-  output: GraphMode,
-  test_split: false
-)
-```
-
-## Fields and constructor parameters
-- `n_neighbors`: the amount of components to be used in the `KNN` model. Defaults to 15.
-- `metric`: the distance metric for the model. It can take one of the following values:
-    - `minkwoski`
-    - `precomputed`
-    - `euclidean`
-  or be a callable object.
-- `weights`: the weight metric for the model. It can take one of the following values:
-    - `uniform`
-    - `distance`
-  or be a callable object.
-- `algorithm`: the algorithm for the model. It can take one of the following values:
-    - `auto`
-    - `ball_tree`
-    - `kd_tree`
-    - `brute`
-  or be a callable object.
-- `output`: toggles graph output mode. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
-- `test_split`: toggles the training split test phase. Defaults to `False`. Requires `output` to be set to `True` to work.
-
-The constructor raises:
-- `ValueError("Invalid n_neighbors number: should be a positive integer.")` if the number of components is not valid.
-- `ValueError("Invalid metric: should be 'minkwoski', 'precomputed', 'euclidean' or a callable.")` if the chosen metric is neither available nor a callable function.
-- `ValueError("Invalid weight: should be 'uniform', 'distance' or a callable")` if the chosen weight is neither available nor a callable function.
-- `ValueError("Invalid algorithm: should be 'auto', 'ball_tree', 'kd_tree' or 'brute'.")` if the chosen algotithm does not exist.
-- `Warning("You selected test_split but it won't run because you disabled the output.")` if `test_split` is run with `output` set to false (split tests only produce graphical output, and are useless when run with disabled output).
-
-## Example
-
-```python
-from chemfusekit.knn import KNNSettings, GraphMode
-
-settings = KNNSettings(
-    n_neighbors=20,     # pick 20 neighbors
-    metric='minkowski', # choose the metric
-    weights='distance', # choose the weight metric
-    algorithm='auto',   # the best algorithm gets chosen automatically
-    output=GraphMode.GRAPHIC, # graph output is enabled
-    test_split=True # the model will be split-tested at the end of the training
-)
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/lda/_category_.json b/docs/versioned_docs/version-2.1.0/lda/_category_.json
deleted file mode 100644
index 8d1f6af..0000000
--- a/docs/versioned_docs/version-2.1.0/lda/_category_.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "label": "LDA module",
-  "position": 4,
-  "link": {
-    "type": "generated-index",
-    "description": "A module for linear discriminant analysis."
-  }
-}
diff --git a/docs/versioned_docs/version-2.1.0/lda/lda.md b/docs/versioned_docs/version-2.1.0/lda/lda.md
deleted file mode 100644
index 2b8f19e..0000000
--- a/docs/versioned_docs/version-2.1.0/lda/lda.md
+++ /dev/null
@@ -1,48 +0,0 @@
----
-sidebar_position: 1
----
-
-# LDA class
-
-A class to store the data, methods and artifacts for _Linear Discriminant Analysis_.
-
-## Syntax
-
-```python
-LDA(settings: LDASettings, data: BaseDataModel)
-```
-
-## Constructor parameters
-
-- `settings`: object of type [`LDASettings`](./ldasettings.md). Contains the settings for
-  the `LDA` object.
-- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md). Contains the data to be analyzed.
-
-## Fields
-
-- `settings`: object of type [`LDASettings`](./ldasettings.md). Contains the settings for
-  the `LDA` object. 
-- Fused data fields:
-  - `x_data` 
-  - `x_train`
-  - `y`
-- `model`: a `LinearDiscriminantAnalysis` model from `scikit-learn`. Defaults to `None`.
-
-## Methods
-
-- `lda(self)`: performs Linear Discriminant Analysis
-- `__print_prediction_graphs(self, y_test, y_pred)`: helper function to print
-  graphs and stats about LDA predictions
-- `predict(self, x_data)`: performs LDA prediction once the model is trained.
-  - *raises*:
-    - `RuntimeError("The LDA model is not trained yet!")` if the LDA model hasn't been trained yet
-
-## Example
-
-```python
-from chemfusekit.lda import LDA
-
-# Initialize and run the LDA class
-lda = LDA(lldf.fused_data, settings)
-lda.lda()
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/lda/ldasettings.md b/docs/versioned_docs/version-2.1.0/lda/ldasettings.md
deleted file mode 100644
index c9c850e..0000000
--- a/docs/versioned_docs/version-2.1.0/lda/ldasettings.md
+++ /dev/null
@@ -1,39 +0,0 @@
----
-sidebar_position: 2
----
-
-# LDASettings class
-
-Holds the settings for the [`LDA`](./lda.md) object.
-
-Inherits from [`BaseSettings`](../base/basesettings.md).
-
-## Syntax
-
-```python
-LDASettings(components: int, output: GraphMode, split_test: bool)
-```
-
-## Fields and constructor parameters
-
-- `components`: the amount of components to be used in the LDA model. Defaults to 3.
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
-- `test_split`: toggles split testing. Defaults to `False`.
-
-
-The constructor raises:
-- `ValueError("Invalid component number: must be a > 1 integer.")` if the number of
-  components is not valid.
-- `Warning("You selected test_split but it won't run because you disabled the output.")` if split tests are run with `output` disabled
-
-## Example
-
-```python
-from chemfusekit.lda import LDASettings, GraphMode
-
-settings = LDASettings(
-    components=(pca.components - 1),    # one less component than the number determined by PCA
-    output=GraphMode.GRAPHIC, # graphs will be printed
-    test_split=True # split testing is enabled
-)
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/lldf/_category_.json b/docs/versioned_docs/version-2.1.0/lldf/_category_.json
deleted file mode 100644
index 096ed0c..0000000
--- a/docs/versioned_docs/version-2.1.0/lldf/_category_.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "label": "LLDF Module",
-  "position": 2,
-  "link": {
-    "type": "doc",
-    "id": "index"
-  }
-}
diff --git a/docs/versioned_docs/version-2.1.0/lldf/index.mdx b/docs/versioned_docs/version-2.1.0/lldf/index.mdx
deleted file mode 100644
index 01c1d16..0000000
--- a/docs/versioned_docs/version-2.1.0/lldf/index.mdx
+++ /dev/null
@@ -1,47 +0,0 @@
-import DocCardList from '@theme/DocCardList';
-
-# LLDF Module
-
-A module for low-level data fusion.
-
-# Members
-
-<DocCardList />
-
-# UML
-
-```mermaid
-classDiagram
-    class Table {
-        +str file_path
-        +str sheet_name
-        +str preprocessing
-        __init__(file_path, sheet_name, preprocessing)
-    }
-
-    class LLDFSettings {
-        +GraphOutput output
-        __init__(output)
-    }
-
-    class LLDF {
-        +LLDFSettings settings
-        +Table[] tables
-        +LLDFDataModel | None fused_data
-        lldf()
-        -_snv()
-        +export_data(export_path: str)
-        __init__(settings, tables[])
-    }
-
-    class LLDFDataModel {
-        +x_data: pd.DataFrame
-        +x_train: pd.DataFrame
-        +y: np.ndarray
-        __init__(x_data, x_train, y)
-    }
-
-    LLDF *-- LLDFModel
-    LLDF *-- Table
-    LLDF *-- LLDFSettings
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/lldf/lldf-class.md b/docs/versioned_docs/version-2.1.0/lldf/lldf-class.md
deleted file mode 100644
index 1a0be38..0000000
--- a/docs/versioned_docs/version-2.1.0/lldf/lldf-class.md
+++ /dev/null
@@ -1,67 +0,0 @@
----
-sidebar_position: 1
----
-
-# LLDF class
-
-The `LLDF` class is used for _low-level data fusion_.
-
-## Syntax
-
-```python
-LLDF(tables: List[Table], lldf_settings: LLDFSettings)
-```
-
-## Constructor parameters
-
-- `tables`: `List[`[`Table`](./table.md)`]`
-
-  A list of `Table` objects containing info about the files to import
-
-- `lldf_settings`: [`LLDFSettings`](./lldfsettings)
-  
-  The settings for the LLDF object.
-
-## Fields
-
-- `settings`: [`LLDFSettings`](./lldfsettings)
-  
-  The settings for the LLDF object.
-
-- `tables`: `List[`[`Table`](./table.md)`]`
-
-  A list of `Table` objects containing info about the files to import
-
-- `fused_data`: [`LLDFModel`](./lldfmodel.md) 
-
-  The resulting model containing the data fusion artifacts.
-
-## Methods
-
-- `_snv(self, input_data)`: static method to rescale input arrays
-- `lldf(self)`: performs low-level data fusion on the data passed in the settings
-  - *raises*:
-    - `FileNotFoundError("Error opening the selected files.")`
-      if the files specified in the settings are not valid
-    - `SyntaxError("LLDF: this type of preprocessing does not exist")`
-      if the preprocessing method specified in the settings is not valid
-- `export_data(self, export_path)`: exports the data fusion artifacts to an Excel file
-  - *raises*:
-    - `RuntimeError("Cannot export data before data fusion.")` if export is
-      attempted before fusing the data
-    - `RuntimeError("Could not export data to the selected path.")` if any error
-      happens during the export phase
-
-
-## Example
-
-```python
-from chemfusekit.lldf import LLDF
-
-# Initialize and run low-level data fusion
-lldf = LLDF(tables, lldf_settings)
-lldf.lldf()
-
-# Export the LLDF data to an Excel file
-lldf.export_data('output_file.xlsx')
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/lldf/lldfmodel.md b/docs/versioned_docs/version-2.1.0/lldf/lldfmodel.md
deleted file mode 100644
index 7232264..0000000
--- a/docs/versioned_docs/version-2.1.0/lldf/lldfmodel.md
+++ /dev/null
@@ -1,25 +0,0 @@
----
-sidebar_position: 3
----
-
-# LLDFDataModel class
-
-This class models the output data from the [`LLDF`](./lldf-class.md) operation.
-
-It inherits from the [`BaseDataModel`](../base/basedatamodel.md).
-
-## Syntax
-
-```python
-LLDFModel(x_data: pd.DataFrame, x_train: pd.DataFrame, y: np.ndarray)
-```
-
-## Fields and constructor parameters
-
-The first two are `Pandas` `DataFrame` objects:
-- `x_data`
-- `x_train`
-
-The last is a `NumPy` `ndarray`:
-- `y`
-
diff --git a/docs/versioned_docs/version-2.1.0/lldf/lldfsettings.md b/docs/versioned_docs/version-2.1.0/lldf/lldfsettings.md
deleted file mode 100644
index 30f985f..0000000
--- a/docs/versioned_docs/version-2.1.0/lldf/lldfsettings.md
+++ /dev/null
@@ -1,25 +0,0 @@
----
-sidebar_position: 2
----
-
-# LLDFSettings class
-
-Holds the settings for the [`LLDF`](./lldf-class.md) object.
-
-## Syntax
-
-```python
-LLDFSettings(output: GraphMode)
-```
-
-## Fields and constructor parameters
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
-
-## Example
-
-```python
-from chemfusekit.lldf import LLDFSettings
-
-# Initialize the settings for low-level data fusion
-lldf_settings = LLDFSettings(output=GraphMode.TEXT)
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/lldf/table.md b/docs/versioned_docs/version-2.1.0/lldf/table.md
deleted file mode 100644
index 782da64..0000000
--- a/docs/versioned_docs/version-2.1.0/lldf/table.md
+++ /dev/null
@@ -1,39 +0,0 @@
----
-sidebar_position: 4
----
-
-# Table class
-
-Holds the information for a single table to import.
-
-The [`LLDF`](./lldf-class.md) object takes a list of `Table` as a parameter.
-
-## Syntax
-
-```python
-Table(
-    file_path: str
-    sheet_name: str
-    preprocessing: str
-)
-```
-
-## Fields and constructor parameters
-
-- `file_path`: a `str` containing the path to the Excel datasheet
-- `sheet_name`: a `str` containing the name of the sheet to select within the Excel file
-- `preprocessing`: a `str` with the name of the preprocessing to be applied to the table.
-   Available options: `snv` (normalization), `savgol` (Savitski-Golay smoothing), `savgol+snv` (both), `none` (no processing).
-
-## Example
-
-```python
-from chemfusekit.lldf import Table
-
-# Create a table
-table1 = Table(
-    file_path='tests/qepas.xlsx',
-    sheet_name='Sheet1',
-    preprocessing='snv'  # normalization preprocessing; other options: savgol, both or none
-)
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/lr/_category_.json b/docs/versioned_docs/version-2.1.0/lr/_category_.json
deleted file mode 100644
index c9a9071..0000000
--- a/docs/versioned_docs/version-2.1.0/lr/_category_.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "label": "LR module",
-  "position": 6,
-  "link": {
-    "type": "generated-index",
-    "description": "A module for logistic regression."
-  }
-}
diff --git a/docs/versioned_docs/version-2.1.0/lr/lr.md b/docs/versioned_docs/version-2.1.0/lr/lr.md
deleted file mode 100644
index f7ded6f..0000000
--- a/docs/versioned_docs/version-2.1.0/lr/lr.md
+++ /dev/null
@@ -1,51 +0,0 @@
----
-sidebar_position: 1
----
-
-# LR class
-
-A class to store the data, methods and artifacts for _Logistic Regression_.
-
-## Syntax
-
-```python
-LR(settings: LRSettings, array_scores: np.ndarray, y: np.ndarray):
-```
-
-## Constructor parameters
-
-- `settings`: object of type [`LRSettings`](./lrsettings.md). Contains the settings for
-  the `LR` object.
-- `array_scores`: `np.ndarray`, product of [`PCA` analysis](../pca/).
-- `y`: `np.ndarray`, product of [`PCA` analysis](../pca/).
-
-## Fields
-
-- `settings`: object of type [`LRSettings`](./lrsettings.md). Contains the settings for
-  the `LR` object. 
-- `array_scores`: product of [`PCA` analysis](../pca/).
-- `y`: product of [`PCA` analysis](../pca/).
-- `model`: A `LR` model from `scikit-learn`. Defaults to `None`.
-
-## Methods
-
-- `lr(self)`: performs Logistic Regression.
-- `predict(self, x_sample)`: performs LR-based classification on input data.
-    - *raises*:
-        - `RuntimeError("The LR model is not trained yet!")` if prediction is
-          started without training the model first;
-        - `raise TypeError("X data for LDA prediction must be non-empty.")` if
-          the data passed as argument is null.
-
-## Example
-
-```python
-from chemfusekit.lr import LR
-
-# Initialize and train the LR class
-lr = LR(settings, array_scores, y)
-lr.lr()
-
-# Perform prediction
-lr.predict(x_sample)
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/lr/lrsettings.md b/docs/versioned_docs/version-2.1.0/lr/lrsettings.md
deleted file mode 100644
index 847c002..0000000
--- a/docs/versioned_docs/version-2.1.0/lr/lrsettings.md
+++ /dev/null
@@ -1,44 +0,0 @@
----
-sidebar_position: 2
----
-
-# LRSettings class
-
-Holds the settings for the [`LR`](./lr.md) object.
-
-Inherits from [`BaseSettings`](../base/basesettings.md).
-
-## Syntax
-
-```python
-LRSettings(algorithm: str, output: GraphMode, test_split: bool)
-```
-
-## Fields and constructor parameters
-
-- `algorithm`: the amount of components to be used in the LDA model. Defaults to
-  `liblinear`.  Other available options:
-    - `lbfgs`
-    - `newton-cg`
-    - `newton-cholesky`
-    - `sag`
-    - `saga`
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
-- `test_split`: toggles split testing. Defaults to `False`.
-
-The constructor raises:
-- `ValueError("This algorithm does not exist.")` if the selected `algorithm`
-  is not a valid option.
-- `Warning("You selected test_split but it won't run because you disabled the output.")` if split tests are run with `output` disabled
-
-## Example
-
-```python
-from chemfusekit.lr import LRSettings, GraphMode
-
-settings = LRSettings(
-    algorithm='newton-cg',
-    output=GraphMode.GRAPHIC,   # graphs will be printed
-    test_split=True # split testing is enabled
-)
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/pca/_category_.json b/docs/versioned_docs/version-2.1.0/pca/_category_.json
deleted file mode 100644
index 266d36f..0000000
--- a/docs/versioned_docs/version-2.1.0/pca/_category_.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "label": "PCA module",
-  "position": 3,
-  "link": {
-    "type": "generated-index",
-    "description": "A module for principal component analysis."
-  }
-}
diff --git a/docs/versioned_docs/version-2.1.0/pca/pca.md b/docs/versioned_docs/version-2.1.0/pca/pca.md
deleted file mode 100644
index 1a07127..0000000
--- a/docs/versioned_docs/version-2.1.0/pca/pca.md
+++ /dev/null
@@ -1,46 +0,0 @@
----
-sidebar_position: 1
----
-
-# PCA class
-
-A class to store the data, methods and artifacts for _Principal Component Analysis_.
-
-## Syntax
-
-```python
-PCA(settings: PCASettings, data: BaseDataModel)
-```
-
-## Constructor parameters
-
-- `settings`: object of type [`PCASettings`](./pcasettings.md). Contains the settings for
-  the `PCA` object.
-- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md). Contains the data to be analyzed.
-
-## Fields
-
-- `fused_data`: object of type [`LLDF`](../lldf/lldf-class.md). Contains the data to be analyzed.
-- `components`: Number of components for the PCA analysis. Defaults to 0.
-- `pca_model`: A `PCA` model from `scikit-learn`. Defaults to `None`.
-- `settings`: object of type [`PCASettings`](./pcasettings.md). Contains the settings for
-  the `PCA` object. 
-
-## Methods
-
-- `pca(self)`: performs Principal Component Analysis
-- `pca_stats(self)` produces PCA-related statistics and graphs.
-
-## Example
-
-```python
-from chemfusekit.pca import PCA
-
-# Initialize and run the PCA class
-pca = PCA(lldf.fused_data, pca_settings)
-pca.pca()
-
-# Print the number of components and the statistics
-print(pca.components)
-pca.pca_stats()
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/pca/pcasettings.md b/docs/versioned_docs/version-2.1.0/pca/pcasettings.md
deleted file mode 100644
index ca533ea..0000000
--- a/docs/versioned_docs/version-2.1.0/pca/pcasettings.md
+++ /dev/null
@@ -1,41 +0,0 @@
----
-sidebar_position: 2
----
-
-# PCASettings class
-
-Holds the settings for the [`PCA`](./pca.md) object.
-
-## Syntax
-
-```python
-PCASettings(
-  target_variance: float,
-  confidence_level: float,
-  initial_components: int,
-  output: GraphMode
-)
-```
-
-## Fields and constructor parameters
-
-- `target_variance`: the minimum cumulative explained variance to reach in the analysis.
-  Defaults to 0.95.
-- `confidence_level`: the confidence level for statistical tests. Defaults to 0.05.
-- `initial_components`: the minimum amount of components to be used in the PCA model.
-  Defaults to 10.
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
-
-## Example
-
-```python
-from chemfusekit.pca import PCASettings, GraphMode
-
-# Initialize the settings for Principal Component Analysis
-pca_settings = PCASettings(
-    target_variance=0.99,
-    confidence_level=0.05,
-    initial_components=10,
-    output=GraphMode.GRAPHIC  # graphs will be printed
-)
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/plsda/_category_.json b/docs/versioned_docs/version-2.1.0/plsda/_category_.json
deleted file mode 100644
index aaf0cbf..0000000
--- a/docs/versioned_docs/version-2.1.0/plsda/_category_.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "label": "PLSDA module",
-  "position": 7,
-  "link": {
-    "type": "generated-index",
-    "description": "A module for partial least squares discriminant analysis."
-  }
-}
diff --git a/docs/versioned_docs/version-2.1.0/plsda/plsda.md b/docs/versioned_docs/version-2.1.0/plsda/plsda.md
deleted file mode 100644
index 3287fd6..0000000
--- a/docs/versioned_docs/version-2.1.0/plsda/plsda.md
+++ /dev/null
@@ -1,47 +0,0 @@
----
-sidebar_position: 1
----
-
-# PLSDA class
-
-A class to store the data, methods and artifacts for _Partial Least Squares Discriminant Analysis_.
-
-## Syntax
-
-```python
-PLSDA(settings: PLSDASettings, data: BaseDataModel)
-```
-
-## Constructor parameters
-
-- `settings`: object of type [`PLSDASettings`](plsdasettings.md). Contains the settings for
-  the `PLSDA` object.
-- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md). Contains the data to be analyzed.
-
-## Fields
-
-- `settings`: object of type [`PLSDASettings`](./plsdasettings.md). Contains the settings for
-  the `PLSDA` object. 
-- `fused_data`: onject of type ['LLDFModel`](../lldf/lldfmodel.md). Contains the
-  artifacts from the data fusion process.
-- `model`: a `PLSRegression` model from `scikit-learn`. Defaults to `None`.
-
-## Methods
-
-- `plsda(self)`: trains the Partial Least Squares Discriminant Analysis model.
-- `predict(self, x_data)`: performs PLSDA prediction once the model is trained.
-  - *raises*:
-    - `RuntimeError("The PLSDA model is not trained yet!")` if the `PLSDA` model hasn't been trained yet
-
-## Example
-
-```python
-from chemfusekit.knn import PLSDA
-
-# Initialize and run the LDA class
-plsda = PLSDA(settings, lldf.fused_data)
-plsda.plsda()
-
-# Run predictions
-plsda.predict(x_data)
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/plsda/plsdasettings.md b/docs/versioned_docs/version-2.1.0/plsda/plsdasettings.md
deleted file mode 100644
index 7b547a7..0000000
--- a/docs/versioned_docs/version-2.1.0/plsda/plsdasettings.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-sidebar_position: 2
----
-
-# PLSDASettings class
-
-Holds the settings for the [`PLSDA`](./plsda.md) object.
-
-Inherits from [`BaseSettings`](../base/basesettings.md).
-
-## Syntax
-
-```python
-PLSDASettings(n_components: int, output: GraphMode, test_split: bool)
-```
-
-## Fields and constructor parameters
-
-- `n_components`: number of components for the PLSDA analysis. Defaults to 3.
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
-- `test_split`: toggles the training split test phase. Defaults to `False`. Requires `output` to not be set to `GraphMode.NONE` to work.
-
-The constructor raises:
-- `ValueError("Invalid n_components number: should be a positive integer.")` if the number of components is below 1.
-- `Warning("You selected test_split but it won't run because you disabled the output.")` if `test_split` is run with `output` set to `GraphOutput.NONE` (split tests only produce graphical output, and are useless when run with disabled output).
-
-## Example
-
-```python
-from chemfusekit.plsda import PLSDASettings, GraphMode
-
-# Initialize the settings for Partial Least Squares Discriminant Analysis
-plsda_settings = PLSDASettings(
-    n_components=5,
-    output=GraphMode.GRAPHIC,   # graphs will be printed
-    test_split=False    # no split testing
-)
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/svm/_category_.json b/docs/versioned_docs/version-2.1.0/svm/_category_.json
deleted file mode 100644
index f0ebda8..0000000
--- a/docs/versioned_docs/version-2.1.0/svm/_category_.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "label": "SVM module",
-  "position": 5,
-  "link": {
-    "type": "generated-index",
-    "description": "A module for support vector machine analysis."
-  }
-}
diff --git a/docs/versioned_docs/version-2.1.0/svm/svm.md b/docs/versioned_docs/version-2.1.0/svm/svm.md
deleted file mode 100644
index b984ff7..0000000
--- a/docs/versioned_docs/version-2.1.0/svm/svm.md
+++ /dev/null
@@ -1,49 +0,0 @@
----
-sidebar_position: 1
----
-
-# SVM class
-
-A class to store the data, methods and artifacts for _Support Vector Machine Analysis_.
-
-## Syntax
-
-```python
-SVM(settings: SVMSettings, data: BaseDataModel)
-```
-
-## Constructor parameters
-
-- `settings`: object of type [`SVMSettings`](./svmsettings.md). Contains the settings for
-  the `SVM` object.
-- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md). Contains the data to be analyzed.
-
-The constructor raises:
-- `ValueError("Fused data input cannot be empty.")` if the input data is null
-- `valueError("Settings cannot be empty.")` if the settings are null
-
-## Fields
-
-- `fused_data`: object of type [`LLDFModel`](../lldf/lldfmodel.md). Contains the data to be analyzed.
-- `settings`: object of type [`SVMSettings`](./svmsettings.md). Contains the settings for
-  the `PCA` object. 
-- `pca_model`: an `SVM` model from `scikit-learn`. Defaults to `None`.
-
-## Methods
-
-- `svm(self)`: performs Support Vector Machine analysis.
-  - *raises*:
-    - `ValueError(SVM: this type of kernel does not exist.")` if the kernel type is invalid
-- `predict(self, x_data)`: performs classification based on SVM
-  - *raises*:
-    - `RuntimeError("The model hasn't been trained yet!")` if the model is null
-
-## Example
-
-```python
-from chemfusekit.svm import SVM
-
-# Initialize and run the SVM class
-svm = LDA(lldf.fused_data, settings)
-svm.svm()
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/svm/svmsettings.md b/docs/versioned_docs/version-2.1.0/svm/svmsettings.md
deleted file mode 100644
index 7ebf852..0000000
--- a/docs/versioned_docs/version-2.1.0/svm/svmsettings.md
+++ /dev/null
@@ -1,43 +0,0 @@
----
-sidebar_position: 2
----
-
-# SVMSettings class
-
-Holds the settings for the [`SVM`](./svm.md) object.
-
-Inherits from [`BaseSettings`](../base/basesettings.md).
-
-## Syntax
-
-```python
-SVMSettings(kernel: str, output: GraphMode, test_split: bool)
-```
-
-## Fields and constructor parameters
-
-- `kernel`: the type of kernel to use in the SVM analysis. Available options:
-  - `linear`
-  - `poly`
-  - `gaussian`
-  - `sigmoid`
-  Defaults to `linear`.
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
-- `test_split`: toggles split testing. Defaults to `False`.
-
-The constructor raises:
-- `ValueError("Invalid type: must be linear, poly, gaussian or sigmoid")` if the selected kernel is not one of the available
-- `Warning("You selected test_split but it won't run because you disabled the output.")` if split tests are run with `output` disabled
-
-## Example
-
-```python
-from chemfusekit.svm import SVMSettings, GraphMode
-
-# Initialize the settings for Support Vector Machine
-svm_settings = SVMSettings(
-    type='linear',
-    output=GraphMode.GRAPHIC,   # graphs will be printed
-    test_split=True # split testing is enabled
-)
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/tutorial.md b/docs/versioned_docs/version-2.1.0/tutorial.md
deleted file mode 100644
index 7945b21..0000000
--- a/docs/versioned_docs/version-2.1.0/tutorial.md
+++ /dev/null
@@ -1,109 +0,0 @@
----
-sidebar_position: 1
----
-
-# Tutorial
-
-Let's discover **Data Fusion**.
-
-As a simple example, we will train an LDA model and use it for classification.
-
-First of all, let's install the package:
-```bash
-pip install chemfusekit 
-```
-
-## First step: data fusion
-
-We will load the `LLDFSettings` with the paths to some Excel datasheets, containing
-respectively the data from a QEPAS spectrometer and a GC chromatographer.
-
-We will pick normalization as the preprocessing technique for the data.
-
-The `LLDF` class will take these settings and perform low-level data fusion on the
-two Excel tables we picked.
-
-```python
-from chemfusekit.lldf import LLDFSettings, LLDF
-
-# Initialize the settings for low-level data fusion
-lldf_settings = LLDFSettings(
-    qepas_path='tests/qepas.xlsx',
-    qepas_sheet='Sheet1',
-    rt_path='tests/rt.xlsx',
-    rt_sheet='Sheet1',
-    preprocessing='snv'  # normalization preprocessing; other options: savgol or both
-)
-
-# Initialize and run low-level data fusion
-lldf = LLDF(lldf_settings)
-lldf.lldf()
-```
-
-Optionally, we can export the fused data into a new, single Excel datasheet:
-
-```python
-# (optional) export the LLDF data to an Excel file
-lldf.export_data('output_file.xlsx')
-```
-
-## Second step: PCA
-
-A run of Principal Component Analysis (`PCA`) will help us pick the right number
-of components for the subsequent `LDA` analysis step.
-
-As in the previous case, we will set it up with the help of the `PCASettings` class.
-
-```python
-from chemfusekit.pca import PCASettings, PCA
-
-# Initialize the settings for Principal Component Analysis
-pca_settings = PCASettings(
-    target_variance=0.99,   # the minimum acceptable level of cumulative explained covariance
-    confidence_level=0.05,  # the desired level of confidence
-    initial_components=10,  # the initial amount of components for the iterative analysis
-    output=GraphMode.GRAPHIC    # graphs will be printed
-)
-
-# Initialize and run the PCA class
-pca = PCA(lldf.fused_data, pca_settings)
-pca.pca()
-
-# Print the number of components and the statistics
-print(pca.components)
-pca.pca_stats()
-```
-
-## Third step: LDA training
-
-In this step we will set up the `LDASettings` and then run the `LDA` analysis with one less
-component than what we figured out from the `PCA` analysis of the previous step.
-
-```python
-from chemfusekit.lda import LDASettings, LDA
-
-settings = LDASettings(
-    components=(pca.components - 1),    # one less component than the number determined by PCA
-    output=GraphMode.GRAPHIC,   # graphs will be printed
-    test_split=True # Split testing is enabled
-)
-
-# Initialize and run the LDA class
-lda = LDA(lldf.fused_data, settings)
-lda.lda()
-```
-
-## Fourth step: prediction
-
-We will pick a random sample from the dataset and see whether the trained `LDA` model
-can identify it correctly.
-
-```python
-# Let's pick a random sample from the dataset and see if it gets recognized correctly:
-x_data_sample = lldf.fused_data.x_train.iloc[119] # should be DMMP
-x_data_sample = x_data_sample.iloc[1:].to_frame().transpose()
-
-# Let's run the prediction:
-predictions = lda.predict(x_data_sample)
-print(predictions)
-```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/utils/_category_.json b/docs/versioned_docs/version-2.1.0/utils/_category_.json
deleted file mode 100644
index 322950e..0000000
--- a/docs/versioned_docs/version-2.1.0/utils/_category_.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "label": "Utilities module",
-  "position": 8,
-  "link": {
-    "type": "generated-index",
-    "description": "A module containing helper functions."
-  }
-}
diff --git a/docs/versioned_docs/version-2.1.0/utils/graphmode.md b/docs/versioned_docs/version-2.1.0/utils/graphmode.md
deleted file mode 100644
index bdfaa0a..0000000
--- a/docs/versioned_docs/version-2.1.0/utils/graphmode.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-sidebar-position: 1
----
-
-# GraphMode enum
-
-The `GraphMode` enum defines three possible values that the output of other classes can take:
-
-- `GRAPHIC`: graphs, tables and stats will be rendered with `Plotly`, `MatPlotLib` or `Seaborn`. Best used with `Jupyter Notebook`;
-- `TEXT`: output will be rendered as plain text. The best option for offline batch processing;
-- `NONE`: output will be suppressed completely.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/utils/graphoutput.md b/docs/versioned_docs/version-2.1.0/utils/graphoutput.md
deleted file mode 100644
index 362bd7f..0000000
--- a/docs/versioned_docs/version-2.1.0/utils/graphoutput.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-sidebar-position: 2
----
-
-# `graph_output` function
-
-A (partially) reusable graphing function shared by different classes. Not meant for direct usage.
-
-## Syntax
-
-```python
-graph_output(scores, model, name: str, mode: GraphMode)
-```
-
-## Parameters
-
-- `scores`: the scores that are output by the model fitting function
-- `model`: a `scikit-learn` classification model
-- `name`: a `str` representing the name of the analysis technique
-- `mode`: a [`GraphMode`](./graphmode.md) enum that acts as an output selector
diff --git a/docs/versioned_docs/version-2.1.0/utils/printconfusionmatrix.md b/docs/versioned_docs/version-2.1.0/utils/printconfusionmatrix.md
deleted file mode 100644
index 826c2f2..0000000
--- a/docs/versioned_docs/version-2.1.0/utils/printconfusionmatrix.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-sidebar-position: 5
----
-
-# `print_confuson_matrix` function
-
-A multimodal confusion matrix and classification report printer utility. Not meant for direct usage.
-
-## Syntax
-
-```python
-print_confusion_matrix(y1, y2, title: str, mode: GraphMode)
-```
-
-## Parameters
-
-- `y1` and `y2`: the true and predicted values
-- `title`: a `str` representing the title for the confusion matrix and classification report
-- `mode`: a [`GraphMode`](./graphmode.md) enum that acts as an output selector
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/utils/printtable.md b/docs/versioned_docs/version-2.1.0/utils/printtable.md
deleted file mode 100644
index a07206e..0000000
--- a/docs/versioned_docs/version-2.1.0/utils/printtable.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-sidebar-position: 3
----
-
-# `print_table` function
-
-A multimodal table printing utility. It can output tables as `Plotly` plots or as plain text. Not meant for direct usage.
-
-## Syntax
-
-```python
-print_table(header_values, cell_values, title: str, mode: GraphMode)
-```
-
-## Parameters
-
-- `header_values`: the column titles
-- `cell_values`: a row array of column arrays
-- `title`: a `str` containing the title for the table
-- `mode`: a [`GraphMode`](./graphmode.md) enum that acts as an output selector
\ No newline at end of file
diff --git a/docs/versioned_docs/version-2.1.0/utils/runsplittests.md b/docs/versioned_docs/version-2.1.0/utils/runsplittests.md
deleted file mode 100644
index 410eb65..0000000
--- a/docs/versioned_docs/version-2.1.0/utils/runsplittests.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-sidebar-position: 4
----
-
-# `run_split_tests` function
-
-A reusable function for split testing a generic model. Not meant for direct usage.
-
-## Syntax
-
-```python
-run_split_test(x, y, model, extended=False, mode: GraphMode)
-```
-
-## Parameters
-
-- `x` and `y`: the regressor and target arrays
-- `model`: a `scikit-learn` classifier
-- `extended`: a `bool` that selects whether a longer split analysis will be carried out
-- `mode`: a [`GraphMode`](./graphmode.md) enum that acts as an output selector
diff --git a/docs/versioned_sidebars/version-2.1.0-sidebars.json b/docs/versioned_sidebars/version-2.1.0-sidebars.json
deleted file mode 100644
index caea0c0..0000000
--- a/docs/versioned_sidebars/version-2.1.0-sidebars.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "tutorialSidebar": [
-    {
-      "type": "autogenerated",
-      "dirName": "."
-    }
-  ]
-}
diff --git a/docs/versions.json b/docs/versions.json
index b619bc6..77b48ba 100644
--- a/docs/versions.json
+++ b/docs/versions.json
@@ -1,5 +1,4 @@
 [
-  "2.1.0",
   "2.0.0",
   "1.2.0",
   "1.1.3"