diff --git a/docs/src/guide/doublemachinelearning.md b/docs/src/guide/doublemachinelearning.md
index ca7382a..caaa92e 100644
--- a/docs/src/guide/doublemachinelearning.md
+++ b/docs/src/guide/doublemachinelearning.md
@@ -91,9 +91,8 @@ an estimated zero probability of treatment, which implies the positivity assumpt
 satisfied.
 
 !!! tip
-    One can also specify the maxium number of possible treatments to consider for the causal 
-    consistency assumption and the minimum and maximum probabilities of treatment for the 
-    positivity assumption with the num\_treatments, min, and max keyword arguments.
+    One can also specify the minimum and maximum probabilities of treatment for the 
+    positivity assumption with the `num_treatments`, `min`, and `max` keyword arguments.
 
 !!! danger
     Obtaining correct estimates is dependent on meeting the assumptions for double machine 
diff --git a/docs/src/guide/gcomputation.md b/docs/src/guide/gcomputation.md
index 27f10f8..aaf41f0 100644
--- a/docs/src/guide/gcomputation.md
+++ b/docs/src/guide/gcomputation.md
@@ -31,7 +31,6 @@ and binary, continuous, time to event, and count outcome variables.
     neurons to use during estimation. These options are specified with the following keyword 
     arguments: quantity\_of\_interest, activation, temporal, num\_machines, num\_feats, 
     sample\_size, and num\_neurons.
-
 ```julia
 # Create some data with a binary treatment
 X, T, Y =  rand(1000, 5), [rand()<0.4 for i in 1:1000], rand(1000)
@@ -91,7 +90,7 @@ satisfied.
 
 !!! tip
     One can also specify the minimum and maximum probabilities of treatment for the 
-    positivity assumption with the num\_treatments, min, and max keyword arguments.
+    positivity assumption with the `num_treatments`, `min`, and `max` keyword arguments.
 
 !!! danger
     Obtaining correct estimates is dependent on meeting the assumptions for G-computation. 
diff --git a/docs/src/guide/its.md b/docs/src/guide/its.md
index 29fb852..8b48420 100644
--- a/docs/src/guide/its.md
+++ b/docs/src/guide/its.md
@@ -97,7 +97,7 @@ tests in one line of code.
 !!! tip
     One can also specify the number of simulated confounders to generate to test the sensitivity 
     of the model to confounding and the minimum and maximum proportion of data to use in the 
-    Wald supremum test by including the n, low, and high keyword arguments.
+    Wald supremum test by including the `n`, `low`, and `high` keyword arguments.
 
 !!! danger
     Obtaining correct estimates is dependent on meeting the assumptions for interrupted time 
diff --git a/docs/src/guide/metalearners.md b/docs/src/guide/metalearners.md
index 573a164..ce47aa3 100644
--- a/docs/src/guide/metalearners.md
+++ b/docs/src/guide/metalearners.md
@@ -119,9 +119,8 @@ an estimated zero probability of treatment, which implies the positivity assumpt
 satisfied.
 
 !!! tip
-    One can also specify the maxium number of possible treatments to consider for the causal 
-    consistency assumption and the minimum and maximum probabilities of treatment for the 
-    positivity assumption with the num\_treatments, min, and max keyword arguments.
+    One can also specify the minimum and maximum probabilities of treatment for the 
+    positivity assumption with the `num_treatments`, `min`, and `max` keyword arguments.
 
 !!! danger
     Obtaining correct estimates is dependent on meeting the assumptions for interrupted time 
diff --git a/docs/src/release_notes.md b/docs/src/release_notes.md
index a0ece26..61cdeab 100644
--- a/docs/src/release_notes.md
+++ b/docs/src/release_notes.md
@@ -1,7 +1,7 @@
 # Release Notes
 These release notes adhere to the [keep a changelog](https://keepachangelog.com/en/1.0.0/) format. Below is a list of changes since CausalELM was first released.
 
-## Version [0.8.0](https://github.com/dscolby/CausalELM.jl/releases/tag/v0.8.0) - 2024-10-31
+## Version [0.8.0](https://github.com/dscolby/CausalELM.jl/releases/tag/v0.8.0) - 2024-12-16
 ### Added
 *   Implemented randomization inference-based confidence intervals [#78](https://github.com/dscolby/CausalELM.jl/issues/78)
 *   Added marginal effects to model summaries [#78](https://github.com/dscolby/CausalELM.jl/issues/78)
@@ -12,6 +12,7 @@ These release notes adhere to the [keep a changelog](https://keepachangelog.com/
 *   Fixed the randomization inference index selection procedure for interrupted time series estimators
 *   Inlined certain methods to slightly improve performance [#76](https://github.com/dscolby/CausalELM.jl/issues/76)
 *   CausalELM models now support any data structure that implements the Tables.jl API, not just DataFrames
+*   Removed unnecessary include and using statements
 
 ## Version [v0.7.0](https://github.com/dscolby/CausalELM.jl/releases/tag/v0.7.0) - 2024-06-22
 ### Added