Implemented requested changes in docs

Author: x-tabdeveloping

docs/basics.md

@@ -284,8 +284,12 @@ model.print_topics()
 
 #### Datamapplot *(clustering models only)*
 
-You can interactively explore clusters using `datamapplot` directly in Turftopic!
-You will first have to install `datamapplot` for this to work.
+You can interactively explore clusters using [datamapplot](https://github.com/TutteInstitute/datamapplot) directly in Turftopic!
+You will first have to install `datamapplot` for this to work:
+
+```bash
+pip install turftopic[datamapplot]
+```
 
 ```python
 from turftopic import ClusteringTopicModel
@@ -309,7 +313,6 @@ fig
 </figure>
 
 
-
 #### topicwizard
 
 Turftopic integrates with [topicwizard](https://github.com/x-tabdeveloping/topicwizard), a package for interactive topic visualization.

docs/clustering.md

@@ -23,19 +23,19 @@ model = ClusteringTopicModel(dimensionality_reduction=TSNE())
 
 It is common practice to reduce the dimensionality of the embeddings before clustering them.
 This is to avoid the curse of dimensionality, an issue, which many clustering models are affected by.
-Dimensionality reduction by default is done with **TSNE** in Turftopic,
+Dimensionality reduction by default is done with [**TSNE**](https://scikit-learn.org/stable/modules/manifold.html#t-distributed-stochastic-neighbor-embedding-t-sne) in Turftopic,
 but users are free to specify the model that will be used for dimensionality reduction.
 
 !!! tip "Use openTSNE for better performance!"
-    By default, a scikit-learn implementation is used, but if you have the openTSNE package installed on your system, Turftopic will automatically use it.
+    By default, a scikit-learn implementation is used, but if you have the [openTSNE](https://github.com/pavlin-policar/openTSNE) package installed on your system, Turftopic will automatically use it.
     You can potentially speed up your clustering topic models by multiple orders of magnitude.
     ```bash
-    pip install opentsne
+    pip install turftopic[opentsne]
     ```
 
 ??? note "What reduction model should I choose?"
     Our knowledge about the impacts of choice of dimensionality reduction is limited, and has not yet been explored in the literature.
-    Top2Vec and BERTopic both use UMAP, which has a number of desirable properties over alternatives (arranging data points into cluster-like structures, better preservation of global structure than TSNE, speed).
+    Top2Vec and BERTopic both use [UMAP](https://umap-learn.readthedocs.io/en/latest/basic_usage.html), which has a number of desirable properties over alternatives (arranging data points into cluster-like structures, better preservation of global structure than TSNE, speed).
 
 ### Clustering
 
@@ -47,7 +47,7 @@ model = ClusteringTopicModel(clustering=HDBSCAN())
 ```
 
 After reducing the dimensionality of the embeddings, they are clustered with a clustering model.
-Turftopic uses **HDBSCAN** as its default.
+Turftopic uses [**HDBSCAN**](https://scikit-learn.org/stable/modules/clustering.html#hdbscan) as its default.
 
 ??? note "What clustering model should I choose?"
     Some clustering models are capable of discovering the number of clusters in the data (HDBSCAN, DBSCAN, OPTICS, etc.).
@@ -183,8 +183,12 @@ model.reset_topics()
 
 ### Visualization
 
-You can interactively explore clusters using `datamapplot` directly in Turftopic!
-You will first have to install `datamapplot` for this to work.
+You can interactively explore clusters using [datamapplot](https://github.com/TutteInstitute/datamapplot) directly in Turftopic!
+You will first have to install `datamapplot` for this to work:
+
+```bash
+pip install turftopic[datamapplot]
+```
 
 ```python
 from turftopic import ClusteringTopicModel