diff --git a/MANIFEST.in b/MANIFEST.in
index 49210f4..cd84699 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -2,6 +2,7 @@ graft clairvoyance
 include setup.py
 include LICENSE.txt
 include README.md
+include requirements.txt
 global-exclude *.py[cod]
 global-exclude Icon*
 global-exclude .DS_Store
diff --git a/clairvoyance/__init__.py b/clairvoyance/__init__.py
index 2b1c88f..316b837 100644
--- a/clairvoyance/__init__.py
+++ b/clairvoyance/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "2024.7.6"
+__version__ = "2024.7.8"
 
 from . import utils
 from . import visuals
diff --git "a/clairvoyance/__pycache__/Icon\r" "b/clairvoyance/__pycache__/Icon\r"
new file mode 100644
index 0000000..e69de29
diff --git a/clairvoyance/bayesian.py b/clairvoyance/bayesian.py
index fa0f29d..5cf37cc 100644
--- a/clairvoyance/bayesian.py
+++ b/clairvoyance/bayesian.py
@@ -33,6 +33,9 @@
     ClairvoyanceRecursiveFeatureElimination,
 )
 
+# Memory profiling
+from memory_profiler import profile
+
 _bayesianclairvoyancebase_docstring = """
         # Modeling parameters:
         # ====================
@@ -455,7 +458,6 @@ def _feature_selection(self, estimator, X, y, X_testing, y_testing, study_name):
             # Show the feature weights be scaled? Before or after
             return (selected_features, model_fs.initial_feature_importances_, model_fs.feature_selected_importances_, model_fs.performance_drifts_, feature_selected_training_cv, feature_selected_testing_score)
                 
-
     def _fit(self, X, y, cv, X_testing=None, y_testing=None, optimize_with_training_and_testing="auto", **study_kws): # How to use the test set here?
         if self.copy_X:
             self.X_ = X.copy()
@@ -624,6 +626,7 @@ def _get_results(self):
         df.index.name = "study_name"
         return df
         
+    @profile
     def fit(self, X, y, cv=3, X_testing=None, y_testing=None, optimize_with_training_and_testing="auto", **study_kws):
         self._fit(
             X=X,
@@ -637,6 +640,7 @@ def fit(self, X, y, cv=3, X_testing=None, y_testing=None, optimize_with_training
         self.results_ = self._get_results()
         return self
 
+    @profile
     def fit_transform(self, X, y, cv=3, X_testing=None, y_testing=None, optimize_with_training_and_testing="auto", **study_kws):
         self._fit(
             X=X,
diff --git a/requirements.txt b/requirements.txt
index eaacff9..27218f8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,4 +8,5 @@ xarray>=0.10.3
 seaborn>=0.10.1
 optuna>=3.6.1
 feature_engine>=1.8.0
+memory-profiler
 #shap>=0.45.1
diff --git a/setup.py b/setup.py
index 9da64f0..3db3527 100644
--- a/setup.py
+++ b/setup.py
@@ -30,7 +30,7 @@
       description='Feature selection via recursive feature inclusion',
       url='https://github.com/jolespin/clairvoyance',
       author='Josh L. Espinoza',
-      author_email='jespinoz@jcvi.org',
+      author_email='jol.espinoz@gmail.com',
       license='BSD-3',
       packages=["clairvoyance"],
       install_requires=requirements[::-1],