diff --git a/pyrocs/biosciences/hill_diversity.py b/pyrocs/biosciences/hill_diversity.py
index ff8a3d5..f3fc4b7 100644
--- a/pyrocs/biosciences/hill_diversity.py
+++ b/pyrocs/biosciences/hill_diversity.py
@@ -10,7 +10,7 @@ def hill_shannon(p: np.array) -> float:
     The use of the geometric mean captures 
     the proportional difference from the mean of extreme values (rather than the absolute values). 
     The equation for the Hill-Simpson based on the formulation in 
-    [Roswell et al., 2021](https://doi.org/10.1111/oik.07202)
+    :cite:p:`roswell_conceptual_2021`.
     
     Hill Shannon (Perplexity): 
     
diff --git a/pyrocs/complex_systems/causal_complexity.py b/pyrocs/complex_systems/causal_complexity.py
index 77aad86..fa5cff8 100644
--- a/pyrocs/complex_systems/causal_complexity.py
+++ b/pyrocs/complex_systems/causal_complexity.py
@@ -108,7 +108,7 @@ def causal_complexity(A: np.ndarray):
     (:math:`E`), nodes (:math:`N`), and connected components (:math:`P`) 
     :cite:p:`ebert_cyclomatic_2016`. 
     In contrast, feedback density captures the fraction of edges 
-    (:math:`E_{loop}`) and nodes (:math:`N_loop`) that are involved in at least one 
+    (:math:`E_{loop}`) and nodes (:math:`N_{loop}`) that are involved in at least one 
     feedback loop. As such, it reflects the potential for cyclic behaviors. 
     Jointly, the measure of causal complexity reflects the number of paths 
     through a system weighted to reflect those with feedback loops. 
diff --git a/pyrocs/complex_systems/grc.py b/pyrocs/complex_systems/grc.py
index cbe1e35..37d10ff 100644
--- a/pyrocs/complex_systems/grc.py
+++ b/pyrocs/complex_systems/grc.py
@@ -4,7 +4,11 @@
 
 def grc(A : np.ndarray, directed : bool):
     """
-    Global reaching centrality (GRC) measures the level of hierarchy within a network based on flow. The equation within the package follows the formulations from [Mones et al., 2012] (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0033799), who quantify GRC as the difference between the maximum and the average value of the local reach centralities of nodes within the network:
+    Global reaching centrality (GRC) measures the level of hierarchy within a network based on flow. 
+    The equation within the package follows the formulations from 
+    :cite:p:`mones_hierarchy_2012`, 
+    who quantify GRC as the difference between the maximum and the average value of the
+    local reach centralities of nodes within the network:
     
     .. math::