diff --git a/artlib/common/VAT.py b/artlib/common/VAT.py
index 3ae48b8..17d8b79 100644
--- a/artlib/common/VAT.py
+++ b/artlib/common/VAT.py
@@ -1,9 +1,8 @@
 """VAT.
 
-Bezdek, J. C., & Hathaway, R. J. (2002).
-VAT: A tool for visual assessment of cluster tendency.
-Proceedings of the 2002 International Joint Conference on Neural Networks.
-doi:10.1109/IJCNN.2002.1007487
+.. # Bezdek, J. C., & Hathaway, R. J. (2002). .. # VAT: A tool for visual assessment of
+cluster tendency. .. # Proceedings of the 2002 International Joint Conference on Neural
+Networks. .. # doi:10.1109/IJCNN.2002.1007487
 
 """
 import numpy as np
@@ -17,6 +16,21 @@ def VAT(
 ) -> Tuple[np.ndarray, np.ndarray]:
     """Visual Assessment of Cluster Tendency (VAT) algorithm.
 
+    VAT was originally designed as a visualization tool for clustering behavior of data.
+    When the VAT-reordered distance matrix is plotted as an image, clusters will appear
+    in visually distinct groups along the diagonal. However, it has since been
+    discovered that the reordering significantly improves the results of order-dependent
+    clustering methods like ART. It is therefore recommended to pre-process data with
+    VAT prior to presentation when possible.
+
+    .. # Bezdek, J. C., & Hathaway, R. J. (2002).
+    .. # VAT: A tool for visual assessment of cluster tendency.
+    .. # Proceedings of the 2002 International Joint Conference on Neural Networks.
+    .. # doi:10.1109/IJCNN.2002.1007487
+
+    .. bibliography:: ../../references.bib
+       :filter: citation_key == "bezdek2002vat"
+
     Parameters
     ----------
     data : np.ndarray
diff --git a/artlib/cvi/iCVIFuzzyArt.py b/artlib/cvi/iCVIFuzzyArt.py
index 8a18d22..4e3c37a 100644
--- a/artlib/cvi/iCVIFuzzyArt.py
+++ b/artlib/cvi/iCVIFuzzyArt.py
@@ -1,4 +1,10 @@
-"""TODO: Add Reference in correct format.
+"""iCVI Fuzzy ART
+
+.. # da Silva, Leonardo Enzo Brito, Nagasharath Rayapati, and Donald C. Wunsch.
+.. # "iCVI-ARTMAP: using incremental cluster validity indices and adaptive resonance
+.. # theory reset mechanism to accelerate validation and achieve multiprototype
+.. # unsupervised representations."
+.. # IEEE Transactions on Neural Networks and Learning Systems 34.12 (2022): 9757-9770.
 
 The original matlab code can be found at
 https://github.com/ACIL-Group/iCVI-toolbox/tree/master
@@ -7,6 +13,9 @@
  Pages 314-316 and 319-320 Extended icvi offline mode can be found at
 https://ieeexplore.ieee.org/document/9745260
 
+.. bibliography:: ../../references.bib
+   :filter: citation_key == "da2022icvi"
+
 """
 import numpy as np
 from typing import Optional, Literal, Callable
@@ -15,7 +24,19 @@
 
 
 class iCVIFuzzyART(FuzzyART):
-    """ICVI Fuzzy Art For Clustering."""
+    """ICVI Fuzzy Art For Clustering.
+
+    .. # da Silva, Leonardo Enzo Brito, Nagasharath Rayapati, and Donald C. Wunsch.
+    .. # "iCVI-ARTMAP: using incremental cluster validity indices and adaptive resonance
+    .. # theory reset mechanism to accelerate validation and achieve multiprototype
+    .. # unsupervised representations."
+    .. # IEEE Transactions on Neural Networks and Learning Systems
+    .. # 34.12 (2022): 9757-9770.
+
+    .. bibliography:: ../../references.bib
+       :filter: citation_key == "da2022icvi"
+
+    """
 
     CALINSKIHARABASZ = 1
 
diff --git a/artlib/elementary/ART1.py b/artlib/elementary/ART1.py
index f14c42f..bc6b1c2 100644
--- a/artlib/elementary/ART1.py
+++ b/artlib/elementary/ART1.py
@@ -1,10 +1,10 @@
-"""ART1.
+"""ART1 :cite:`carpenter1987massively`."""
 
-Carpenter, G. A., & Grossberg, S. (1987a). A massively parallel architecture for a self-
-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image
-Processing, 37, 54 – 115. doi:10. 1016/S0734-189X(87)80014-2.
-
-"""
+# Carpenter, G. A., & Grossberg, S. (1987a).
+# A massively parallel architecture for a self-organizing neural pattern
+# recognition machine.
+# Computer Vision, Graphics, and Image
+# Processing, 37, 54 – 115. doi:10. 1016/S0734-189X(87)80014-2.
 
 import numpy as np
 from typing import Optional, List, Tuple, Union, Dict
@@ -13,13 +13,18 @@
 
 
 class ART1(BaseART):
-    """ART1 for Clustering.
+    """ART1 for Binary Clustering.
+
+    This module implements ART1 as first published in: :cite:`carpenter1987massively`.
+
+
+    .. # Carpenter, G. A., & Grossberg, S. (1987a).
+    .. # A massively parallel architecture for a self-organizing neural pattern
+    .. # recognition machine.
+    .. # Computer Vision, Graphics, and Image
+    .. # Processing, 37, 54 – 115. doi:10. 1016/S0734-189X(87)80014-2.
 
-    This module implements ART1 as first published in Carpenter, G. A., & Grossberg, S.
-    (1987a). A massively parallel architecture for a self-organizing neural pattern
-    recognition machine. Computer Vision, Graphics, and Image Processing, 37, 54 – 115.
-    doi:10. 1016/S0734-189X(87)80014-2. ART1 is intended for binary data clustering
-    only.
+    ART1 is exclusively for clustering binary data.
 
     """
 
diff --git a/artlib/elementary/ART2.py b/artlib/elementary/ART2.py
index 6c37b3b..e734410 100644
--- a/artlib/elementary/ART2.py
+++ b/artlib/elementary/ART2.py
@@ -1,21 +1,26 @@
-"""ART2.
+"""ART2 :cite:`carpenter1987art`, :cite:`carpenter1991art`.
 
-Carpenter, G. A., & Grossberg, S. (1987b).
-ART 2: self-organization of stable category recognition codes for analog input patterns.
-Appl. Opt., 26, 4919–4930. doi:10.1364/AO.26.004919.
+::
 
-Carpenter, G. A., Grossberg, S., & Rosen, D. B. (1991b).
-ART 2-A: An adaptive resonance algorithm for rapid category learning and recognition.
-Neural Networks, 4, 493 – 504. doi:10.1016/0893-6080(91) 90045-7.
-
-==================================================================
-DISCLAIMER: DO NOT USE ART2!!!
-IT DOES NOT WORK
-It is provided for completeness only.
-Stephan Grossberg himself has said ART2 does not work.
-==================================================================
+    ==================================================================
+    DISCLAIMER: DO NOT USE ART2!!!
+    IT DOES NOT WORK
+    It is provided for completeness only.
+    Stephan Grossberg himself has said ART2 does not work.
+    ==================================================================
 
 """
+
+# Carpenter, G. A., & Grossberg, S. (1987b).
+# ART 2: self-organization of stable category recognition codes for analog input
+# patterns.
+# Appl. Opt., 26, 4919–4930. doi:10.1364/AO.26.004919.
+
+# Carpenter, G. A., Grossberg, S., & Rosen, D. B. (1991b).
+# ART 2-A: An adaptive resonance algorithm for rapid category learning and
+# recognition.
+# Neural Networks, 4, 493 – 504. doi:10.1016/0893-6080(91) 90045-7.
+
 import numpy as np
 from typing import Optional, List
 from warnings import warn
@@ -25,14 +30,24 @@
 class ART2A(BaseART):
     """ART2-A for Clustering.
 
-    This module implements ART2-A as first published in
-    Carpenter, G. A., Grossberg, S., & Rosen, D. B. (1991b).
-    ART 2-A: An adaptive resonance algorithm for rapid category learning and
-    recognition.
-    Neural Networks, 4, 493 – 504. doi:10.1016/0893-6080(91) 90045-7.
+    This module implements ART2-A as first published in:
+    :cite:`carpenter1987art`, :cite:`carpenter1991art`
+
+
+    .. # Carpenter, G. A., & Grossberg, S. (1987b).
+    .. # ART 2: self-organization of stable category recognition codes for analog input
+    .. # patterns.
+    .. # Appl. Opt., 26, 4919–4930. doi:10.1364/AO.26.004919.
+
+    .. # Carpenter, G. A., Grossberg, S., & Rosen, D. B. (1991b).
+    .. # ART 2-A: An adaptive resonance algorithm for rapid category learning and
+    .. # recognition.
+    .. # Neural Networks, 4, 493 – 504. doi:10.1016/0893-6080(91) 90045-7.
+
 
-    ART2-A is similar to ART1 but designed for analog data. This method is implemented
-    for historical purposes and is not recommended for use.
+    ART2-A is similar to :class:`~artlib.elementary.ART1.ART1` but designed for analog
+    data. This method is implemented for historical purposes and is not recommended
+    for use.
 
     """
 
diff --git a/artlib/elementary/BayesianART.py b/artlib/elementary/BayesianART.py
index bc2ecd9..6b19f9b 100644
--- a/artlib/elementary/BayesianART.py
+++ b/artlib/elementary/BayesianART.py
@@ -1,9 +1,9 @@
-"""Bayesian ART.
+"""Bayesian ART :cite:`vigdor2007bayesian`."""
+# Vigdor, B., & Lerner, B. (2007).
+# The Bayesian ARTMAP.
+# IEEE Transactions on Neural
+# Networks, 18, 1628–1644. doi:10.1109/TNN.2007.900234.
 
-Vigdor, B., & Lerner, B. (2007). The Bayesian ARTMAP. IEEE Transactions on Neural
-Networks, 18, 1628–1644. doi:10.1109/TNN.2007.900234.
-
-"""
 import numpy as np
 from typing import Optional, Iterable, List, Callable, Literal, Tuple, Union, Dict
 import operator
@@ -15,11 +15,17 @@
 class BayesianART(BaseART):
     """Bayesian ART for Clustering.
 
-    This module implements Bayesian ART as first published in Vigdor, B., & Lerner, B.
-    (2007). The Bayesian ARTMAP. IEEE Transactions on Neural Networks, 18, 1628–1644.
-    doi:10.1109/TNN.2007.900234. Bayesian ART clusters data in Bayesian Distributions
-    (Hyper-ellipsoids) and is similar to Gaussian ART but differs in that it allows
-    arbitrary rotation of the hyper-ellipsoid.
+    This module implements Bayesian ART as first published in:
+    :cite:`vigdor2007bayesian`.
+
+     .. # Vigdor, B., & Lerner, B. (2007).
+    .. # The Bayesian ARTMAP.
+    .. # IEEE Transactions on Neural
+    .. # Networks, 18, 1628–1644. doi:10.1109/TNN.2007.900234.
+
+    Bayesian ART clusters data in Bayesian Distributions (Hyper-ellipsoids) and is
+    similar to :class:`~artlib.elementary.GaussianART.GaussianART` but differs in that
+    it allows arbitrary rotation of the hyper-ellipsoid.
 
     """
 
diff --git a/artlib/elementary/EllipsoidART.py b/artlib/elementary/EllipsoidART.py
index 1425c5a..0c5b9d2 100644
--- a/artlib/elementary/EllipsoidART.py
+++ b/artlib/elementary/EllipsoidART.py
@@ -1,15 +1,14 @@
-"""Ellipsoid ART.
+"""Ellipsoid ART :cite:`anagnostopoulos2001a`, :cite:`anagnostopoulos2001b`."""
+# Anagnostopoulos, G. C., & Georgiopoulos, M. (2001a).
+# Ellipsoid ART and ARTMAP for incremental clustering and classification.
+# In Proc. IEEE International Joint Conference on Neural Networks (IJCNN)
+# (pp. 1221–1226). volume 2. doi:10.1109/IJCNN.2001.939535.
 
-Anagnostopoulos, G. C., & Georgiopoulos, M. (2001a). Ellipsoid ART and ARTMAP for
-incremental clustering and classification. In Proc. IEEE International Joint Conference
-on Neural Networks (IJCNN) (pp. 1221–1226). volume 2. doi:10.1109/IJCNN.2001.939535.
+# Anagnostopoulos, G. C., & Georgiopoulos, M. (2001b).
+# Ellipsoid ART and ARTMAP for incremental unsupervised and supervised learning.
+# In Aerospace/Defense Sensing, Simulation, and Controls (pp. 293– 304).
+# International Society for Optics and Photonics. doi:10.1117/12.421180.
 
-Anagnostopoulos, G. C., & Georgiopoulos, M. (2001b). Ellipsoid ART and ARTMAP for
-incremental unsupervised and supervised learning. In Aerospace/Defense Sensing,
-Simulation, and Controls (pp. 293– 304). International Society for Optics and Photonics.
-doi:10.1117/12.421180.
-
-"""
 import numpy as np
 from typing import Optional, List, Tuple, Union, Dict
 from matplotlib.axes import Axes
@@ -20,12 +19,22 @@
 class EllipsoidART(BaseART):
     """Ellipsoid ART for Clustering.
 
-    This module implements Ellipsoid ART as first published in Anagnostopoulos, G. C., &
-    Georgiopoulos, M. (2001a). Ellipsoid ART and ARTMAP for incremental clustering and
-    classification. In Proc. IEEE International Joint Conference on Neural Networks
-    (IJCNN) (pp. 1221–1226). volume 2. doi:10.1109/IJCNN.2001.939535. Ellipsoid ART
-    clusters data in Hyper-ellipsoids. It is highly sensitive to sample presentation
-    order as the second sample will determine the orientation of the principal axes.
+    This module implements Ellipsoid ART as first published in:
+    :cite:`anagnostopoulos2001a`, :cite:`anagnostopoulos2001b`.
+
+    .. # Anagnostopoulos, G. C., & Georgiopoulos, M. (2001a).
+    .. # Ellipsoid ART and ARTMAP for incremental clustering and classification.
+    .. # In Proc. IEEE International Joint Conference on Neural Networks (IJCNN)
+    .. # (pp. 1221–1226). volume 2. doi:10.1109/IJCNN.2001.939535.
+
+    .. # Anagnostopoulos, G. C., & Georgiopoulos, M. (2001b).
+    .. # Ellipsoid ART and ARTMAP for incremental unsupervised and supervised learning.
+    .. # In Aerospace/Defense Sensing, Simulation, and Controls (pp. 293– 304).
+    .. # International Society for Optics and Photonics. doi:10.1117/12.421180.
+
+    Ellipsoid ART clusters data in Hyper-ellipsoids. It is highly sensitive to sample
+    presentation order as the second sample will determine the orientation of the
+    principal axes.
 
     """
 
diff --git a/artlib/elementary/FuzzyART.py b/artlib/elementary/FuzzyART.py
index fd849d0..5410b7e 100644
--- a/artlib/elementary/FuzzyART.py
+++ b/artlib/elementary/FuzzyART.py
@@ -1,11 +1,9 @@
-"""Fuzzy ART.
+"""Fuzzy ART :cite:`carpenter1991fuzzy`."""
+# Carpenter, G. A., Grossberg, S., & Rosen, D. B. (1991c).
+# Fuzzy ART: Fast stable learning and categorization of analog patterns by an
+# adaptive resonance system.
+# Neural Networks, 4, 759 – 771. doi:10.1016/0893-6080(91)90056-B.
 
-Carpenter, G. A., Grossberg, S., & Rosen, D. B. (1991c).
-Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive
-resonance system.
-Neural Networks, 4, 759 – 771. doi:10.1016/0893-6080(91)90056-B.
-
-"""
 import numpy as np
 from typing import Optional, Iterable, List, Tuple, Union, Dict
 from matplotlib.axes import Axes
@@ -58,13 +56,16 @@ def get_bounding_box(
 class FuzzyART(BaseART):
     """Fuzzy ART for Clustering.
 
-    This module implements Fuzzy ART as first published in
-    Carpenter, G. A., Grossberg, S., & Rosen, D. B. (1991c).
-    Fuzzy ART: Fast stable learning and categorization of analog patterns by an
-    adaptive resonance system.
-    Neural Networks, 4, 759 – 771. doi:10.1016/0893-6080(91)90056-B.
+    This module implements Fuzzy ART as first published in:
+    :cite:`carpenter1991fuzzy`.
+
+    .. # Carpenter, G. A., Grossberg, S., & Rosen, D. B. (1991c).
+    .. # Fuzzy ART: Fast stable learning and categorization of analog patterns by an
+    .. # adaptive resonance system.
+    .. # Neural Networks, 4, 759 – 771. doi:10.1016/0893-6080(91)90056-B.
 
-    Fuzzy ART is a hyper-box based clustering method.
+    Fuzzy ART is a hyper-box based clustering method that is exceptionally fast and
+    explainable.
 
     """
 
diff --git a/artlib/elementary/GaussianART.py b/artlib/elementary/GaussianART.py
index 11f76c2..34a917b 100644
--- a/artlib/elementary/GaussianART.py
+++ b/artlib/elementary/GaussianART.py
@@ -1,11 +1,8 @@
-"""Gaussian ART.
-
-Williamson, J. R. (1996).
-Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of Noisy
-Multidimensional Maps.
-Neural Networks, 9, 881 – 897. doi:10.1016/0893-6080(95)00115-8.
-
-"""
+"""Gaussian ART :cite:`williamson1996gaussian`."""
+# Williamson, J. R. (1996).
+# Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of Noisy
+# Multidimensional Maps.
+# Neural Networks, 9, 881 – 897. doi:10.1016/0893-6080(95)00115-8.
 
 import numpy as np
 from typing import Optional, Iterable, List, Tuple, Union, Dict
@@ -17,15 +14,18 @@
 class GaussianART(BaseART):
     """Gaussian ART for Clustering.
 
-    This module implements Gaussian ART as first published in
-    Williamson, J. R. (1996).
-    Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of
-    Noisy Multidimensional Maps.
-    Neural Networks, 9, 881 – 897. doi:10.1016/0893-6080(95)00115-8.
+    This module implements Gaussian ART as first published in:
+    :cite:`williamson1996gaussian`.
+
+    .. # Williamson, J. R. (1996).
+    .. # Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of Noisy
+    .. # Multidimensional Maps.
+    .. # Neural Networks, 9, 881 – 897. doi:10.1016/0893-6080(95)00115-8.
 
     Guassian ART clusters data in Gaussian Distributions (Hyper-ellipsoids) and is
-    similar to Bayesian ART but differs in that the hyper-ellipsoid always have their
-    principal axes square to the coordinate frame. It is also faster than Bayesian ART.
+    similar to :class:`~artlib.elementary.BayesianART.BayesianART` but differs in that
+    the hyper-ellipsoid always have their principal axes square to the coordinate
+    frame. It is also faster than :class:`~artlib.elementary.BayesianART.BayesianART`.
 
     """
 
diff --git a/artlib/elementary/HypersphereART.py b/artlib/elementary/HypersphereART.py
index 38b6e76..73ff933 100644
--- a/artlib/elementary/HypersphereART.py
+++ b/artlib/elementary/HypersphereART.py
@@ -1,11 +1,9 @@
-"""Hyperpshere ART.
+"""Hyperpshere ART :cite:`anagnostopoulos2000hypersphere`."""
+# Anagnostopoulos, G. C., & Georgiopulos, M. (2000).
+# Hypersphere ART and ARTMAP for unsupervised and supervised, incremental learning.
+# In Proc. IEEE International Joint Conference on Neural Networks (IJCNN)
+# (pp. 59–64). volume 6. doi:10.1109/IJCNN.2000.859373.
 
-Anagnostopoulos, G. C., & Georgiopulos, M. (2000). Hypersphere ART and ARTMAP for
-unsupervised and supervised, incremental learning. In Proc. IEEE International Joint
-Conference on Neural Networks (IJCNN) (pp. 59–64). volume 6.
-doi:10.1109/IJCNN.2000.859373.
-
-"""
 import numpy as np
 from typing import Optional, Iterable, List
 from matplotlib.axes import Axes
@@ -16,11 +14,16 @@
 class HypersphereART(BaseART):
     """Hypersphere ART for Clustering.
 
-    This module implements Ellipsoid ART as first published in Anagnostopoulos, G. C., &
-    Georgiopulos, M. (2000). Hypersphere ART and ARTMAP for unsupervised and supervised,
-    incremental learning. In Proc. IEEE International Joint Conference on Neural
-    Networks (IJCNN) (pp. 59–64). volume 6. doi:10.1109/IJCNN.2000.859373. Hyperpshere
-    ART clusters data in Hyper-spheres similar to k-means with a dynamic k.
+    This module implements Ellipsoid ART as first published in:
+    :cite:`anagnostopoulos2000hypersphere`.
+
+    .. # Anagnostopoulos, G. C., & Georgiopulos, M. (2000).
+    .. # Hypersphere ART and ARTMAP for unsupervised and supervised, incremental
+    .. # learning.
+    .. # In Proc. IEEE International Joint Conference on Neural Networks (IJCNN)
+    .. # (pp. 59–64). volume 6. doi:10.1109/IJCNN.2000.859373.
+
+    Hyperpshere ART clusters data in Hyper-spheres similar to k-means with a dynamic k.
 
     """
 
diff --git a/artlib/elementary/QuadraticNeuronART.py b/artlib/elementary/QuadraticNeuronART.py
index a492c42..1d0c6f7 100644
--- a/artlib/elementary/QuadraticNeuronART.py
+++ b/artlib/elementary/QuadraticNeuronART.py
@@ -1,12 +1,11 @@
-"""Quadratic Neuron ART.
+"""Quadratic Neuron ART :cite:`su2001application`, :cite:`su2005new`."""
+# Su, M.-C., & Liu, T.-K. (2001).
+# Application of neural networks using quadratic junctions in cluster analysis.
+# Neurocomputing, 37, 165 – 175. doi:10.1016/S0925-2312(00)00343-X.
 
-Su, M.-C., & Liu, T.-K. (2001). Application of neural networks using quadratic junctions
-in cluster analysis. Neurocomputing, 37, 165 – 175. doi:10.1016/S0925-2312(00)00343-X.
-
-Su, M.-C., & Liu, Y.-C. (2005). A new approach to clustering data with arbitrary shapes.
-Pattern Recognition, 38, 1887 – 1901. doi:10.1016/j.patcog.2005.04.010.
-
-"""
+# Su, M.-C., & Liu, Y.-C. (2005).
+# A new approach to clustering data with arbitrary shapes.
+# Pattern Recognition, 38, 1887 – 1901. doi:10.1016/j.patcog.2005.04.010.
 
 import numpy as np
 from typing import Optional, Iterable, List, Tuple, Union, Dict
@@ -19,11 +18,19 @@
 class QuadraticNeuronART(BaseART):
     """Quadratic Neuron ART for Clustering.
 
-    This module implements Quadratic Neuron ART as first published in Su, M.-C., & Liu,
-    Y.-C. (2005). A new approach to clustering data with arbitrary shapes. Pattern
-    Recognition, 38, 1887 – 1901. doi:10.1016/j.patcog.2005.04.010. Quadratic Neuron ART
-    clusters data in Hyper-ellipsoid by utilizing a quadratic neural network for
-    activation and resonance.
+    This module implements Quadratic Neuron ART as first published in:
+    :cite:`su2001application`, :cite:`su2005new`.
+
+    .. # Su, M.-C., & Liu, T.-K. (2001).
+    .. # Application of neural networks using quadratic junctions in cluster analysis.
+    .. # Neurocomputing, 37, 165 – 175. doi:10.1016/S0925-2312(00)00343-X.
+
+    .. # Su, M.-C., & Liu, Y.-C. (2005).
+    .. # A new approach to clustering data with arbitrary shapes.
+    .. # Pattern Recognition, 38, 1887 – 1901. doi:10.1016/j.patcog.2005.04.010.
+
+    Quadratic Neuron ART clusters data in Hyper-ellipsoid by utilizing a quadratic
+    neural network for activation and resonance.
 
     """
 
diff --git a/artlib/fusion/FusionART.py b/artlib/fusion/FusionART.py
index 343f3ea..58449ef 100644
--- a/artlib/fusion/FusionART.py
+++ b/artlib/fusion/FusionART.py
@@ -1,13 +1,11 @@
-"""Fusion ART.
+"""Fusion ART :cite:`tan2007intelligence`."""
+# Tan, A.-H., Carpenter, G. A., & Grossberg, S. (2007).
+# Intelligence Through Interaction: Towards a Unified Theory for Learning.
+# In D. Liu, S. Fei, Z.-G. Hou, H. Zhang, & C. Sun (Eds.),
+# Advances in Neural Networks – ISNN 2007 (pp. 1094–1103).
+# Berlin, Heidelberg: Springer Berlin Heidelberg.
+# doi:10.1007/ 978-3-540-72383-7_128.
 
-Tan, A.-H., Carpenter, G. A., & Grossberg, S. (2007).
-Intelligence Through Interaction: Towards a Unified Theory for Learning.
-In D. Liu, S. Fei, Z.-G. Hou, H. Zhang, & C. Sun (Eds.),
-Advances in Neural Networks – ISNN 2007 (pp. 1094–1103).
-Berlin, Heidelberg: Springer Berlin Heidelberg.
-doi:10.1007/ 978-3-540-72383-7_128.
-
-"""
 import numpy as np
 from typing import Optional, Union, Callable, List, Literal, Tuple, Dict
 from copy import deepcopy
@@ -45,13 +43,16 @@ def get_channel_position_tuples(
 class FusionART(BaseART):
     """Fusion ART for Data Fusion and Regression.
 
-    This module implements Fusion ART as first described in
-    Tan, A.-H., Carpenter, G. A., & Grossberg, S. (2007).
-    Intelligence Through Interaction: Towards a Unified Theory for Learning.
-    In D. Liu, S. Fei, Z.-G. Hou, H. Zhang, & C. Sun (Eds.),
-    Advances in Neural Networks – ISNN 2007 (pp. 1094–1103).
-    Berlin, Heidelberg: Springer Berlin Heidelberg.
-    doi:10.1007/ 978-3-540-72383-7_128.
+    This module implements Fusion ART as first described in:
+    :cite:`tan2007intelligence`.
+
+    .. # Tan, A.-H., Carpenter, G. A., & Grossberg, S. (2007).
+    .. # Intelligence Through Interaction: Towards a Unified Theory for Learning.
+    .. # In D. Liu, S. Fei, Z.-G. Hou, H. Zhang, & C. Sun (Eds.),
+    .. # Advances in Neural Networks – ISNN 2007 (pp. 1094–1103).
+    .. # Berlin, Heidelberg: Springer Berlin Heidelberg.
+    .. # doi:10.1007/ 978-3-540-72383-7_128.
+
     Fusion ART accepts an arbitrary number of ART modules, each assigned a different
     data channel. The activation and match functions for all ART modules are then fused
     such that all modules must be simultaneously active and resonant in order for a
diff --git a/artlib/hierarchical/DeepARTMAP.py b/artlib/hierarchical/DeepARTMAP.py
index 9e771b4..b11475a 100644
--- a/artlib/hierarchical/DeepARTMAP.py
+++ b/artlib/hierarchical/DeepARTMAP.py
@@ -1,11 +1,8 @@
-"""Deep ARTMAP.
-
-Carpenter, G. A., Grossberg, S., & Reynolds, J. H. (1991a).
-ARTMAP: Supervised real-time learning and classification of nonstationary data by a
-self-organizing neural network.
-Neural Networks, 4, 565 – 588. doi:10.1016/0893-6080(91)90012-T.
-
-"""
+"""Deep ARTMAP :cite:`carpenter1991artmap`."""
+# Carpenter, G. A., Grossberg, S., & Reynolds, J. H. (1991a).
+# ARTMAP: Supervised real-time learning and classification of nonstationary data by a
+# self-organizing neural network.
+# Neural Networks, 4, 565 – 588. doi:10.1016/0893-6080(91)90012-T.
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin, ClusterMixin
 from typing import Optional, cast, Union, Literal, Tuple
@@ -19,12 +16,19 @@
 class DeepARTMAP(BaseEstimator, ClassifierMixin, ClusterMixin):
     """DeepARTMAP for Hierachical Supervised and Unsupervised Learning.
 
-    This module implements DeepARTMAP, a generalization of the ARTMAP class that allows
-    an arbitrary number of data channels to be divisively clustered. DeepARTMAP support
-    both supervised and unsupervised modes. If only two ART modules are provided,
-    DeepARTMAP reverts to standard ARTMAP where the first module is the A module and the
-    second module is the B module. DeepARTMAP does not currently have a direct citation
-    and is an original creation of this library.
+    This module implements DeepARTMAP, a generalization of the
+    :class:`~artlib.supervised.ARTMAP.ARTMAP` class :cite:`carpenter1991artmap` that
+    allows an arbitrary number of data channels to be divisively clustered. DeepARTMAP
+    support both supervised and unsupervised modes. If only two ART modules are
+    provided, DeepARTMAP reverts to standard :class:`~artlib.supervised.ARTMAP.ARTMAP`
+    where the first module is the A-module and the second module is the B-module.
+    DeepARTMAP does not currently have a direct citation and is an original creation
+    of this library.
+
+    .. # Carpenter, G. A., Grossberg, S., & Reynolds, J. H. (1991a).
+    .. # ARTMAP: Supervised real-time learning and classification of nonstationary data
+    .. # by a self-organizing neural network.
+    .. # Neural Networks, 4, 565 – 588. doi:10.1016/0893-6080(91)90012-T.
 
     """
 
diff --git a/artlib/hierarchical/SMART.py b/artlib/hierarchical/SMART.py
index 42ac2b3..8952607 100644
--- a/artlib/hierarchical/SMART.py
+++ b/artlib/hierarchical/SMART.py
@@ -1,10 +1,9 @@
-"""SMART.
-
-Bartfai, G. (1994). Hierarchical clustering with ART neural networks. In Proc. IEEE
-International Conference on Neural Networks (ICNN) (pp. 940–944). volume 2.
-doi:10.1109/ICNN.1994.374307.
-
-"""
+"""SMART :cite:`bartfai1994hierarchical`."""
+# Bartfai, G. (1994).
+# Hierarchical clustering with ART neural networks.
+# In Proc. IEEE International Conference on Neural Networks (ICNN)
+# (pp. 940–944). volume 2.
+# doi:10.1109/ICNN.1994.374307.
 
 import numpy as np
 from typing import Union, Type, Optional, Literal, Tuple
@@ -17,13 +16,19 @@
 class SMART(DeepARTMAP):
     """SMART for Hierachical Clustering.
 
-    This module implements SMART as first published in Bartfai, G. (1994). Hierarchical
-    clustering with ART neural networks. In Proc. IEEE International Conference on
-    Neural Networks (ICNN) (pp. 940–944). volume 2. doi:10.1109/ICNN.1994.374307. SMART
-    accepts an uninstatiated ART class and hierarchically clusters data in a divisive
-    fashion by using a set of vigilance values that monotonically increase in their
-    restrictiveness. SMART is a special case of DeepARTMAP, which forms the backbone of
-    this class, where all channels receive the same data.
+    This module implements SMART as first published in: :cite:`bartfai1994hierarchical`
+
+    .. # Bartfai, G. (1994).
+    .. # Hierarchical clustering with ART neural networks.
+    .. # In Proc. IEEE International Conference on Neural Networks (ICNN)
+    .. # (pp. 940–944). volume 2.
+    .. # doi:10.1109/ICNN.1994.374307.
+
+    SMART accepts an uninstantiated :class:`~artlib.common.BaseART.BaseART` class and
+    hierarchically clusters data in a divisive fashion by using a set of vigilance
+    values that monotonically increase in their restrictiveness. SMART is a special
+    case of :class:`~artlib.hierarchical.DeepARTMAP.DeepARTMAP`, which forms the
+    backbone of this class, where all channels receive the same data.
 
     """
 
diff --git a/artlib/reinforcement/FALCON.py b/artlib/reinforcement/FALCON.py
index d65dd2f..8946ffb 100644
--- a/artlib/reinforcement/FALCON.py
+++ b/artlib/reinforcement/FALCON.py
@@ -1,14 +1,13 @@
-"""FALCON.
+"""FALCON :cite:`tan2004falcon`, :cite:`tan2008integrating`."""
+# Tan, A.-H. (2004).
+# FALCON: a fusion architecture for learning, cognition, and navigation.
+# In Proc. IEEE International Joint Conference on Neural Networks (IJCNN)
+# (pp. 3297–3302). volume 4. doi:10.1109/ IJCNN.2004.1381208.
 
-Tan, A.-H. (2004). FALCON: a fusion architecture for learning, cognition, and
-navigation. In Proc. IEEE International Joint Conference on Neural Networks (IJCNN) (pp.
-3297–3302). volume 4. doi:10.1109/ IJCNN.2004.1381208.
-
-Tan, A.-H., Lu, N., & Xiao, D. (2008). Integrating Temporal Difference Methods and Self-
-Organizing Neural Networks for Reinforcement Learning With Delayed Evaluative Feedback.
-IEEE Transactions on Neural Networks, 19 , 230–244. doi:10.1109/TNN.2007.905839
-
-"""
+# Tan, A.-H., Lu, N., & Xiao, D. (2008).
+# Integrating Temporal Difference Methods and Self-Organizing Neural Networks for
+# Reinforcement Learning With Delayed Evaluative Feedback.
+# IEEE Transactions on Neural Networks, 19 , 230–244. doi:10.1109/TNN.2007.905839
 
 import numpy as np
 from typing import Optional, Literal, Tuple, Union, List
@@ -20,15 +19,17 @@
 class FALCON:
     """FALCON for Reinforcement Learning.
 
-    This module implements the reactive FALCON as first described in
-    Tan, A.-H. (2004).
-    FALCON: a fusion architecture for learning, cognition, and navigation.
-    In Proc. IEEE International Joint Conference on Neural Networks (IJCNN)
-    (pp. 3297–3302). volume 4. doi:10.1109/IJCNN.2004.1381208.
+    This module implements the reactive FALCON as first described in:
+    :cite:`tan2004falcon`.
+
+    .. # Tan, A.-H. (2004).
+    .. # FALCON: a fusion architecture for learning, cognition, and navigation.
+    .. # In Proc. IEEE International Joint Conference on Neural Networks (IJCNN)
+    .. # (pp. 3297–3302). volume 4. doi:10.1109/ IJCNN.2004.1381208.
 
-    FALCON is based on a Fusion-ART backbone but only accepts 3 channels: State, Action,
-    and Reward. Specific functions are implemented for getting optimal reward and action
-    predictions.
+    FALCON is based on a :class:`~artlib.fusion.FusionART.FusionART` backbone but only
+    accepts 3 channels: State, Action, and Reward. Specific functions are implemented
+    for getting optimal reward and action predictions.
 
     """
 
@@ -291,13 +292,17 @@ def get_rewards(self, states: np.ndarray, actions: np.ndarray) -> np.ndarray:
 class TD_FALCON(FALCON):
     """TD-FALCON for Reinforcement Learning.
 
-    This module implements TD-FALCON as first described in Tan, A.-H., Lu, N., & Xiao,
-    D. (2008). Integrating Temporal Difference Methods and Self-Organizing Neural
-    Networks for Reinforcement Learning With Delayed Evaluative Feedback. IEEE
-    Transactions on Neural Networks, 19 , 230–244. doi:10.1109/TNN.2007.905839. TD-
-    FALCON is based on a FALCON backbone but includes specific function for temporal-
-    difference learning. Currently, only SARSA is implemented and only Fuzzy ART base
-    modules are supported.
+    This module implements TD-FALCON as first described in:
+    :cite:`tan2008integrating`.
+
+    .. # Tan, A.-H., Lu, N., & Xiao, D. (2008).
+    .. # Integrating Temporal Difference Methods and Self-Organizing Neural Networks for
+    .. # Reinforcement Learning With Delayed Evaluative Feedback.
+    .. # IEEE Transactions on Neural Networks, 19 , 230–244. doi:10.1109/TNN.2007.905839
+
+    TD-FALCON is based on a :class:`FALCON` backbone but includes specific function for
+    temporal-difference learning. Currently, only SARSA is implemented and only
+    :class:`~artlib.elementary.FuzzyART.FuzzyART` base modules are supported.
 
     """
 
diff --git a/artlib/reinforcement/__init__.py b/artlib/reinforcement/__init__.py
index 93dc41b..fb10e97 100644
--- a/artlib/reinforcement/__init__.py
+++ b/artlib/reinforcement/__init__.py
@@ -13,6 +13,7 @@
 The modules herein only provide for reactive and SARSA style learning.
 
 `SARSA <https://en.wikipedia.org/wiki/Reinforcement_learning#SARSA>`_
+
 `Reactive agents <https://en.wikipedia.org/wiki/Reactive_planning>`_
 
 """
diff --git a/artlib/supervised/ARTMAP.py b/artlib/supervised/ARTMAP.py
index 4529146..a24a389 100644
--- a/artlib/supervised/ARTMAP.py
+++ b/artlib/supervised/ARTMAP.py
@@ -1,11 +1,8 @@
-"""ARTMAP.
-
-Carpenter, G. A., Grossberg, S., & Reynolds, J. H. (1991a).
-ARTMAP: Supervised real-time learning and classification of nonstationary data by a
-self-organizing neural network.
-Neural Networks, 4, 565 – 588. doi:10.1016/0893-6080(91)90012-T.
-
-"""
+"""ARTMAP :cite:`carpenter1991artmap`."""
+# Carpenter, G. A., Grossberg, S., & Reynolds, J. H. (1991a).
+# ARTMAP: Supervised real-time learning and classification of nonstationary data by a
+# self-organizing neural network.
+# Neural Networks, 4, 565 – 588. doi:10.1016/0893-6080(91)90012-T.
 import numpy as np
 from typing import Literal, Tuple, Dict, Union, Optional
 from artlib.common.BaseART import BaseART
@@ -16,19 +13,22 @@
 class ARTMAP(SimpleARTMAP):
     """ARTMAP for Classification and Regression.
 
-    This module implements ARTMAP as first published in
-    Carpenter, G. A., Grossberg, S., & Reynolds, J. H. (1991a).
-    ARTMAP: Supervised real-time learning and classification of nonstationary data by a
-    self-organizing neural network.
-    Neural Networks, 4, 565 – 588. doi:10.1016/0893-6080(91)90012-T.
+    This module implements ARTMAP as first published in: :cite:`carpenter1991artmap`.
 
-    ARTMAP joins accepts two ART modules A and B which cluster the dependent channel
-    (samples) and the independent channel (labels) respectively while linking them with
-    a many-to-one mapping. If your labels are integers, use SimpleARTMAP for a faster
-    and more direct implementation. ARTMAP also provides the ability to fit a regression
-    model to data and specific functions have been implemented to allow this. However,
-    FusionART provides substantially better fit for regression problems which are not
-    monotonic.
+    .. # Carpenter, G. A., Grossberg, S., & Reynolds, J. H. (1991a).
+    .. # ARTMAP: Supervised real-time learning and classification of nonstationary data
+    .. # by a self-organizing neural network.
+    .. # Neural Networks, 4, 565 – 588. doi:10.1016/0893-6080(91)90012-T.
+
+    ARTMAP accepts two :class:`~artlib.common.BaseART.BaseART` modules A and B which
+    cluster the dependent
+    channel (samples) and the independent channel (labels) respectively while linking
+    them with a many-to-one mapping. If your labels are integers,
+    use :class:`~artlib.supervised.SimpleARTMAP.SimpleARTMAP` for a faster and more
+    direct implementation. ARTMAP also provides the ability to fit a regression model to
+    data and specific functions have been implemented to allow this. However,
+    :class:`~artlib.fusion.FusionART.FusionART` provides substantially better fit for
+    regression problems which are not monotonic.
 
     """
 
@@ -248,9 +248,7 @@ def partial_fit(
 
         """
         self.validate_data(X, y)
-        self.module_b.partial_fit(
-            y, match_tracking=match_tracking, epsilon=epsilon
-        )
+        self.module_b.partial_fit(y, match_tracking=match_tracking, epsilon=epsilon)
         super(ARTMAP, self).partial_fit(
             X,
             self.labels_b,
diff --git a/artlib/supervised/SimpleARTMAP.py b/artlib/supervised/SimpleARTMAP.py
index 1674bd5..f77eff0 100644
--- a/artlib/supervised/SimpleARTMAP.py
+++ b/artlib/supervised/SimpleARTMAP.py
@@ -1,10 +1,7 @@
-"""Simple ARTMAP.
-
-Serrano-Gotarredona, T., Linares-Barranco, B., & Andreou, A. G. (1998).
-Adaptive Resonance Theory Microchips: Circuit Design Techniques.
-Norwell, MA, USA: Kluwer Academic Publishers.
-
-"""
+"""Simple ARTMAP :cite:`gotarredona1998adaptive`."""
+# Serrano-Gotarredona, T., Linares-Barranco, B., & Andreou, A. G. (1998).
+# Adaptive Resonance Theory Microchips: Circuit Design Techniques.
+# Norwell, MA, USA: Kluwer Academic Publishers.
 import numpy as np
 from typing import Optional, Literal, Dict, Union, Tuple
 from matplotlib.axes import Axes
@@ -18,16 +15,20 @@
 class SimpleARTMAP(BaseARTMAP):
     """SimpleARTMAP for Classification.
 
-    This module implements SimpleARTMAP as first published in
-    Serrano-Gotarredona, T., Linares-Barranco, B., & Andreou, A. G. (1998).
-    Adaptive Resonance Theory Microchips: Circuit Design Techniques.
-    Norwell, MA, USA: Kluwer Academic Publishers.
+    This module implements SimpleARTMAP as first published in:
+    :cite:`gotarredona1998adaptive`.
 
-    SimpleARTMAP allows the clustering of data samples while enforcing a many-to-one
-    mapping from sample clusters to labels. It accepts an instantiated ART module and
-    dynamically adapts the vigilance function to prevent resonance when the many-to-one
-    mapping is violated. This enables SimpleARTMAP to identify discrete clusters
-    belonging to each category label.
+    .. # Serrano-Gotarredona, T., Linares-Barranco, B., & Andreou, A. G. (1998).
+    .. # Adaptive Resonance Theory Microchips: Circuit Design Techniques.
+    .. # Norwell, MA, USA: Kluwer Academic Publishers.
+
+    SimpleARTMAP is a special case of :class:`~artlib.supervised.ARTMAP.ARTMAP`
+    specifically for classification. It allows the clustering of data samples while
+    enforcing a many-to-one mapping from sample clusters to labels. It accepts an
+    instantiated :class:`~artlib.common.BaseART.BaseART` module and dynamically adapts
+    the vigilance function to prevent resonance when the many-to-one mapping is
+    violated. This enables SimpleARTMAP to identify discrete clusters belonging to
+    each category label.
 
     """
 
@@ -308,9 +309,7 @@ def partial_fit(
             )
         for i, (x, c_b) in enumerate(zip(X, y)):
             self.module_a.pre_step_fit(X)
-            c_a = self.step_fit(
-                x, c_b, match_tracking=match_tracking, epsilon=epsilon
-            )
+            c_a = self.step_fit(x, c_b, match_tracking=match_tracking, epsilon=epsilon)
             self.module_a.labels_[i + j] = c_a
             self.module_a.post_step_fit(X)
         return self
diff --git a/artlib/topological/DualVigilanceART.py b/artlib/topological/DualVigilanceART.py
index 45b8d9c..29ea422 100644
--- a/artlib/topological/DualVigilanceART.py
+++ b/artlib/topological/DualVigilanceART.py
@@ -1,9 +1,7 @@
-"""Dual Vigilance ART.
-
-Brito da Silva, L. E., Elnabarawy, I., & Wunsch II, D. C. (2019). Dual vigilance fuzzy
-adaptive resonance theory. Neural Networks, 109, 1–5. doi:10.1016/j.neunet.2018.09.015.
-
-"""
+"""Dual Vigilance ART :cite:`da2019dual`."""
+# Brito da Silva, L. E., Elnabarawy, I., & Wunsch II, D. C. (2019).
+# Dual vigilance fuzzy adaptive resonance theory.
+# Neural Networks, 109, 1–5. doi:10.1016/j.neunet.2018.09.015.
 import numpy as np
 from typing import Optional, Callable, List, Literal, Union, Dict
 from warnings import warn
@@ -16,16 +14,22 @@
 class DualVigilanceART(BaseART):
     """Dual Vigilance ART for Clustering.
 
-    This module implements Dual Vigilance ART as first published in Brito da Silva, L.
-    E., Elnabarawy, I., & Wunsch II, D. C. (2019). Dual vigilance fuzzy adaptive
-    resonance theory. Neural Networks, 109, 1–5. doi:10.1016/j.neunet.2018.09.015. Dual
-    Vigilance ART allows a base ART module to cluster with both an upper and lower
-    vigilance value. The upper-vigilance value allows the base ART module to cluster
-    normally, however, data is simultaneously clustered using the lower vigilance level
-    to combine multiple base ART categories into a single abstracted category. This
-    permits clusters to be combined to form arbitrary shapes. For example if the base
-    ART module is fuzzy ART, a Dual Vigilance Fuzzy ART clustering result would look
-    like a series of hyper-boxes forming an arbitrary geometry.
+    This module implements Dual Vigilance ART as first published in: :cite:`da2019dual`.
+
+    .. # Brito da Silva, L. E., Elnabarawy, I., & Wunsch II, D. C. (2019).
+    .. # Dual vigilance fuzzy adaptive resonance theory.
+    .. # Neural Networks, 109, 1–5. doi:10.1016/j.neunet.2018.09.015.
+
+    Dual Vigilance ART allows a :class:`~artlib.common.BaseART.BaseART` module to
+    cluster with both an upper and lower vigilance value. The upper-vigilance value
+    allows the :class:`~artlib.common.BaseART.BaseART` module to cluster normally,
+    however, data is simultaneously clustered using the lower vigilance level to
+    combine multiple base ART categories into a single abstracted category. This
+    permits clusters to be combined to form arbitrary shapes. For example if the
+    :class:`~artlib.common.BaseART.BaseART` module is
+    :class:`~artlib.elementary.FuzzyART.FuzzyART`, a Dual Vigilance Fuzzy ART
+    clustering result would look  like a series of hyper-boxes forming an arbitrary
+    geometry.
 
     """
 
diff --git a/artlib/topological/TopoART.py b/artlib/topological/TopoART.py
index 8f5fae6..35efe23 100644
--- a/artlib/topological/TopoART.py
+++ b/artlib/topological/TopoART.py
@@ -1,13 +1,10 @@
-"""Topo ART.
-
-Tscherepanow, M. (2010).
-TopoART: A Topology Learning Hierarchical ART Network.
-In K. Diamantaras, W. Duch, & L. S. Iliadis (Eds.),
-Artificial Neural Networks – ICANN 2010 (pp. 157–167).
-Berlin, Heidelberg: Springer Berlin Heidelberg.
-doi:10.1007/978-3-642-15825-4_21.
-
-"""
+"""Topo ART :cite:`tscherepanow2010topoart`."""
+# Tscherepanow, M. (2010).
+# TopoART: A Topology Learning Hierarchical ART Network.
+# In K. Diamantaras, W. Duch, & L. S. Iliadis (Eds.),
+# Artificial Neural Networks – ICANN 2010 (pp. 157–167).
+# Berlin, Heidelberg: Springer Berlin Heidelberg.
+# doi:10.1007/978-3-642-15825-4_21.
 
 import numpy as np
 from typing import Optional, Callable, List, Literal, Tuple, Union, Dict
@@ -22,19 +19,21 @@
 class TopoART(BaseART):
     """Topo ART for Topological Clustering.
 
-    This module implements Topo ART as first published in
-
-    Tscherepanow, M. (2010).
-    TopoART: A Topology Learning Hierarchical ART Network.
-    In K. Diamantaras, W. Duch, & L. S. Iliadis (Eds.),
-    Artificial Neural Networks – ICANN 2010 (pp. 157–167).
-    Berlin, Heidelberg: Springer Berlin Heidelberg.
-    doi:10.1007/978-3-642-15825-4_21.
-
-    Topo ART clusters accepts an instatiated base ART module and generates a topological
-    clustering by recording the first and second resonant cluster relationships in an
-    adjacency matrix. Further, it updates the second resonant cluster with a lower
-    learning rate than the first, providing for a distributed learning model.
+    This module implements Topo ART as first published in:
+    :cite:`tscherepanow2010topoart`.
+
+    .. # Tscherepanow, M. (2010).
+    .. # TopoART: A Topology Learning Hierarchical ART Network.
+    .. # In K. Diamantaras, W. Duch, & L. S. Iliadis (Eds.),
+    .. # Artificial Neural Networks – ICANN 2010 (pp. 157–167).
+    .. # Berlin, Heidelberg: Springer Berlin Heidelberg.
+    .. # doi:10.1007/978-3-642-15825-4_21.
+
+    Topo ART clusters accepts an instantiated :class:`~artlib.common.BaseART.BaseART`
+    module and generates a topological clustering by recording the first and second
+    resonant cluster relationships in an adjacency matrix. Further, it updates the
+    second resonant cluster with a lower learning rate than the first, providing for
+    a distributed learning model.
 
     """
 
diff --git a/artlib/topological/__init__.py b/artlib/topological/__init__.py
index 72f9273..e8e583a 100644
--- a/artlib/topological/__init__.py
+++ b/artlib/topological/__init__.py
@@ -5,11 +5,12 @@
 geometry of the data. Topological clustering techniques, such as hierarchical clustering
 and Mapper, are often used in fields like data analysis and computational topology.
 
-The two modules herein provide contrasting advantages. TopoART allows for the creation
-of an adjacency matrix which can be useful when clusters overlap or are in close
-proximity. Dual Vigilance ART allows for the abstract merging of many smaller clusters
-and is well suited to problems where the clusters take-on complex geometries where other
-clustering approaches would fail.
+The two modules herein provide contrasting advantages.
+:class:`~artlib.topological.TopoART.TopoART` allows for the creation of an adjacency
+matrix which can be useful when clusters overlap or are in close proximity.
+:class:`~artlib.topological.DualVigilanceART.DualVigilanceART` allows for the abstract
+merging of many smaller clusters and is well suited to problems where the clusters
+take-on complex geometries where other clustering approaches would fail.
 
 `Topological clustering <https://en.wikipedia.org/wiki/Topological_data_analysis>`_
 
diff --git a/docs/source/citation.rst b/docs/source/citation.rst
index 1b5e4dd..035e8bf 100644
--- a/docs/source/citation.rst
+++ b/docs/source/citation.rst
@@ -2,11 +2,11 @@ Citation
 ==================================
 If you use this project in your research, please cite it as:
 
-.. bibliography:: references.bib
+.. bibliography:: artlib_citation.bib
    :style: unsrt
    :all:
 
 Alternatively, you can cite the project using the following BibTeX entry:
 
-.. literalinclude:: references.bib
+.. literalinclude:: artlib_citation.bib
    :language: bibtex
diff --git a/docs/source/conf.py b/docs/source/conf.py
index d69d5b3..68f3418 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -45,7 +45,7 @@ def setup(app):
 # autoapi_python_class_content = 'both'
 # autoclass_content = 'both'
 
-bibtex_bibfiles = ['references.bib']
+bibtex_bibfiles = ['artlib_citation.bib', '../../references.bib']
 
 intersphinx_mapping = {
     'python': ('https://docs.python.org/3', None),
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 0fabef5..1573794 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -22,6 +22,7 @@ AdaptiveResonanceLib Home
    contributing
    contact
    license
+   references
    citation
 
 .. toctree::
diff --git a/docs/source/references.rst b/docs/source/references.rst
new file mode 100644
index 0000000..45f1346
--- /dev/null
+++ b/docs/source/references.rst
@@ -0,0 +1,6 @@
+References
+==================================
+
+.. bibliography:: ../../references.bib
+   :style: unsrt
+   :all:
\ No newline at end of file
diff --git a/references.bib b/references.bib
new file mode 100644
index 0000000..3188944
--- /dev/null
+++ b/references.bib
@@ -0,0 +1,196 @@
+@inproceedings{bezdek2002vat,
+  title={VAT: A tool for visual assessment of (cluster) tendency},
+  author={Bezdek, James C and Hathaway, Richard J},
+  booktitle={Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No. 02CH37290)},
+  volume={3},
+  pages={2225--2230},
+  year={2002},
+  organization={IEEE}
+}
+@article{da2022icvi,
+  title={iCVI-ARTMAP: using incremental cluster validity indices and adaptive resonance theory reset mechanism to accelerate validation and achieve multiprototype unsupervised representations},
+  author={da Silva, Leonardo Enzo Brito and Rayapati, Nagasharath and Wunsch, Donald C},
+  journal={IEEE Transactions on Neural Networks and Learning Systems},
+  volume={34},
+  number={12},
+  pages={9757--9770},
+  year={2022},
+  publisher={IEEE}
+}
+@article{carpenter1987massively,
+  title={A massively parallel architecture for a self-organizing neural pattern recognition machine},
+  author={Carpenter, Gail A and Grossberg, Stephen},
+  journal={Computer vision, graphics, and image processing},
+  volume={37},
+  number={1},
+  pages={54--115},
+  year={1987},
+  publisher={Elsevier}
+}
+@article{carpenter1987art,
+  title={ART 2: Self-organization of stable category recognition codes for analog input patterns},
+  author={Carpenter, Gail A and Grossberg, Stephen},
+  journal={Applied optics},
+  volume={26},
+  number={23},
+  pages={4919--4930},
+  year={1987},
+  publisher={Optica Publishing Group}
+}
+@article{carpenter1991art,
+  title={ART 2-A: An adaptive resonance algorithm for rapid category learning and recognition},
+  author={Carpenter, Gail A and Grossberg, Stephen and Rosen, David B},
+  journal={Neural networks},
+  volume={4},
+  number={4},
+  pages={493--504},
+  year={1991},
+  publisher={Elsevier}
+}
+@article{vigdor2007bayesian,
+  title={The bayesian artmap},
+  author={Vigdor, Boaz and Lerner, Boaz},
+  journal={IEEE Transactions on Neural Networks},
+  volume={18},
+  number={6},
+  pages={1628--1644},
+  year={2007},
+  publisher={IEEE}
+}
+@inproceedings{anagnostopoulos2001a,
+  title={Ellipsoid ART and ARTMAP for incremental clustering and classification},
+  author={Anagnostopoulos, Georgios C and Georgiopoulos, Michael},
+  booktitle={IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No. 01CH37222)},
+  volume={2},
+  pages={1221--1226},
+  year={2001},
+  organization={IEEE}
+}
+@inproceedings{anagnostopoulos2001b,
+  title={Ellipsoid ART and ARTMAP for incremental unsupervised and supervised learning},
+  author={Anagnostopoulos, Georgios C and Georgiopoulos, Michael},
+  booktitle={Applications and Science of Computational Intelligence IV},
+  volume={4390},
+  pages={293--304},
+  year={2001},
+  organization={SPIE}
+}
+@article{carpenter1991fuzzy,
+  title={Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system},
+  author={Carpenter, Gail A and Grossberg, Stephen and Rosen, David B},
+  journal={Neural networks},
+  volume={4},
+  number={6},
+  pages={759--771},
+  year={1991},
+  publisher={Elsevier}
+}
+@article{williamson1996gaussian,
+  title={Gaussian ARTMAP: A neural network for fast incremental learning of noisy multidimensional maps},
+  author={Williamson, James R},
+  journal={Neural networks},
+  volume={9},
+  number={5},
+  pages={881--897},
+  year={1996},
+  publisher={Elsevier}
+}
+@inproceedings{anagnostopoulos2000hypersphere,
+  title={Hypersphere ART and ARTMAP for unsupervised and supervised, incremental learning},
+  author={Anagnostopoulos, Georgios C and Georgiopulos, M},
+  booktitle={Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium},
+  volume={6},
+  pages={59--64},
+  year={2000},
+  organization={IEEE}
+}
+@article{su2001application,
+  title={Application of neural networks using quadratic junctions in cluster analysis},
+  author={Su, Mu-Chun and Liu, Ta-Kang},
+  journal={Neurocomputing},
+  volume={37},
+  number={1-4},
+  pages={165--175},
+  year={2001},
+  publisher={Elsevier}
+}
+@article{su2005new,
+  title={A new approach to clustering data with arbitrary shapes},
+  author={Su, Mu-chun and Liu, Yi-chun},
+  journal={pattern recognition},
+  volume={38},
+  number={11},
+  pages={1887--1901},
+  year={2005},
+  publisher={Elsevier}
+}
+@inproceedings{tan2007intelligence,
+  title={Intelligence through interaction: Towards a unified theory for learning},
+  author={Tan, Ah-Hwee and Carpenter, Gail A and Grossberg, Stephen},
+  booktitle={International Symposium on Neural Networks},
+  pages={1094--1103},
+  year={2007},
+  organization={Springer}
+}
+@inproceedings{bartfai1994hierarchical,
+  title={Hierarchical clustering with ART neural networks},
+  author={Bartfai, Guszti},
+  booktitle={Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)},
+  volume={2},
+  pages={940--944},
+  year={1994},
+  organization={IEEE}
+}
+@inproceedings{tan2004falcon,
+  title={FALCON: A fusion architecture for learning, cognition, and navigation},
+  author={Tan, Ah-Hwee},
+  booktitle={2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541)},
+  volume={4},
+  pages={3297--3302},
+  year={2004},
+  organization={IEEE}
+}
+@article{tan2008integrating,
+  title={Integrating temporal difference methods and self-organizing neural networks for reinforcement learning with delayed evaluative feedback},
+  author={Tan, Ah-Hwee and Lu, Ning and Xiao, Dan},
+  journal={IEEE transactions on neural networks},
+  volume={19},
+  number={2},
+  pages={230--244},
+  year={2008},
+  publisher={IEEE}
+}
+@inproceedings{carpenter1991artmap,
+  author={Carpenter, G.A. and Grossberg, S. and Reynolds, J.H.},
+  booktitle={[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering},
+  title={ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network},
+  year={1991},
+  volume={},
+  number={},
+  pages={341-342},
+  keywords={Subspace constraints;Databases;Neural networks;Supervised learning;Resonance;Pattern recognition;System testing;Benchmark testing;Machine learning;Machine learning algorithms},
+  doi={10.1109/ICNN.1991.163370}
+}
+@misc{gotarredona1998adaptive,
+  title={Adaptive Resonance Theory Microchips Circuit Design Techniques},
+  author={Gotarredona, Teresa Serrano and Barranco, B Linares and Andreou, AG},
+  year={1998},
+  publisher={Norwell, MA: Kluwer}
+}
+@article{da2019dual,
+  title={Dual vigilance fuzzy adaptive resonance theory},
+  author={da Silva, Leonardo Enzo Brito and Elnabarawy, Islam and Wunsch II, Donald C},
+  journal={Neural Networks},
+  volume={109},
+  pages={1--5},
+  year={2019},
+  publisher={Elsevier}
+}
+@inproceedings{tscherepanow2010topoart,
+  title={TopoART: A topology learning hierarchical ART network},
+  author={Tscherepanow, Marko},
+  booktitle={International Conference on Artificial Neural Networks},
+  pages={157--167},
+  year={2010},
+  organization={Springer}
+}
diff --git a/scripts/generate_references.py b/scripts/generate_references.py
index 6a7a24f..51f0404 100644
--- a/scripts/generate_references.py
+++ b/scripts/generate_references.py
@@ -23,7 +23,7 @@ def generate_references():
             modified_bibtex = "\n".join(bibtex_lines)
 
             # Write the modified content to references.bib
-            output_path = os.path.join("docs", "source", "references.bib")
+            output_path = os.path.join("docs", "source", "artlib_citation.bib")
             with open(output_path, "w", encoding="utf-8") as f:
                 f.write(modified_bibtex)
         else: