diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index a2091c51..de36617a 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -152,18 +152,18 @@ jobs:
       displayName: Install Anaconda packages
       condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
 
-    - bash: |
-        source activate myEnvironment
-        conda build . recipe --variants "{'version': ['$(GitVersion.SemVer)']}"
-      displayName: Build Noarch conda packages
-      condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
-
-    - bash: |
-        source activate myEnvironment
-        anaconda login --username $(ANACONDAUSER) --password $(ANACONDAPW)
-        anaconda upload /usr/local/miniconda/envs/myEnvironment/conda-bld/noarch/*.tar.bz2
-      displayName: Upload conda packages
-      condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
+#    - bash: |
+#        source activate myEnvironment
+#        conda build . recipe --variants "{'version': ['$(GitVersion.SemVer)']}"
+#      displayName: Build Noarch conda packages
+#      condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
+#
+#    - bash: |
+#        source activate myEnvironment
+#        anaconda login --username $(ANACONDAUSER) --password $(ANACONDAPW)
+#        anaconda upload /usr/local/miniconda/envs/myEnvironment/conda-bld/noarch/*.tar.bz2
+#      displayName: Upload conda packages
+#      condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
 
 - job: "Create_Docker_images"
   dependsOn: Build_UQpy_and_run_tests
diff --git a/docs/code/sensitivity/chatterjee/plot_chatterjee_exponential.py b/docs/code/sensitivity/chatterjee/chatterjee_exponential.py
similarity index 94%
rename from docs/code/sensitivity/chatterjee/plot_chatterjee_exponential.py
rename to docs/code/sensitivity/chatterjee/chatterjee_exponential.py
index 04cf4cf0..d824a885 100644
--- a/docs/code/sensitivity/chatterjee/plot_chatterjee_exponential.py
+++ b/docs/code/sensitivity/chatterjee/chatterjee_exponential.py
@@ -52,7 +52,7 @@
 SA = ChatterjeeSensitivity(runmodel_obj, dist_object)
 
 # Compute Chatterjee indices using the pick and freeze algorithm
-computed_indices = SA.run(n_samples=1_000_000)
+SA.run(n_samples=1_000_000)
 
 # %% [markdown]
 # **Chattererjee indices**
@@ -66,11 +66,11 @@
 # :math:`S^2_{CVM} = \frac{6}{\pi} \operatorname{arctan}(\sqrt{19}) - 2 \approx 0.5693`
 
 # %%
-computed_indices["chatterjee_i"]
+SA.first_order_chatterjee_indices
 
 # **Plot the Chatterjee indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["chatterjee_i"][:, 0],
+    SA.first_order_chatterjee_indices[:, 0],
     plot_title="Chatterjee indices",
     color="C2",
 )
diff --git a/docs/code/sensitivity/chatterjee/plot_chatterjee_ishigami.py b/docs/code/sensitivity/chatterjee/chatterjee_ishigami.py
similarity index 85%
rename from docs/code/sensitivity/chatterjee/plot_chatterjee_ishigami.py
rename to docs/code/sensitivity/chatterjee/chatterjee_ishigami.py
index 696a07fe..16807e1e 100644
--- a/docs/code/sensitivity/chatterjee/plot_chatterjee_ishigami.py
+++ b/docs/code/sensitivity/chatterjee/chatterjee_ishigami.py
@@ -4,7 +4,7 @@
 ==============================================
 
 The ishigami function is a non-linear, non-monotonic function that is commonly used to 
-benchmark uncertainty and senstivity analysis methods.
+benchmark uncertainty and sensitivity analysis methods.
 
 .. math::
     f(x_1, x_2, x_3) = sin(x_1) + a \cdot sin^2(x_2) + b \cdot x_3^4 sin(x_1)
@@ -49,7 +49,7 @@
 # %% [markdown]
 SA = ChatterjeeSensitivity(runmodel_obj, dist_object)
 
-computed_indices = SA.run(
+SA.run(
     n_samples=100_000,
     estimate_sobol_indices=True,
     n_bootstrap_samples=100,
@@ -60,18 +60,18 @@
 # **Chattererjee indices**
 
 # %%
-computed_indices["chatterjee_i"]
+SA.first_order_chatterjee_indices
 
 # %% [markdown]
 # **Confidence intervals for the Chatterjee indices**
 
 # %%
-computed_indices["confidence_interval_chatterjee_i"]
+SA.confidence_interval_chatterjee
 
 # **Plot the Chatterjee indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["chatterjee_i"][:, 0],
-    computed_indices["confidence_interval_chatterjee_i"],
+    SA.first_order_chatterjee_indices[:, 0],
+    SA.confidence_interval_chatterjee,
     plot_title="Chatterjee indices",
     color="C2",
 )
@@ -88,11 +88,11 @@
 # :math:`S_3`: 0.0
 
 # %%
-computed_indices["sobol_i"]
+SA.first_order_sobol_indices
 
 # **Plot the first order Sobol indices**
 fig2, ax2 = plot_sensitivity_index(
-    computed_indices["sobol_i"][:, 0],
+    SA.first_order_sobol_indices[:, 0],
     plot_title="First order Sobol indices",
     color="C0",
 )
diff --git a/docs/code/sensitivity/chatterjee/plot_chatterjee_sobol_func.py b/docs/code/sensitivity/chatterjee/chatterjee_sobol_func.py
similarity index 91%
rename from docs/code/sensitivity/chatterjee/plot_chatterjee_sobol_func.py
rename to docs/code/sensitivity/chatterjee/chatterjee_sobol_func.py
index 3d54ad38..293b2b88 100644
--- a/docs/code/sensitivity/chatterjee/plot_chatterjee_sobol_func.py
+++ b/docs/code/sensitivity/chatterjee/chatterjee_sobol_func.py
@@ -68,17 +68,17 @@
 SA = ChatterjeeSensitivity(runmodel_obj, dist_object)
 
 # Compute Chatterjee indices using rank statistics
-computed_indices = SA.run(n_samples=500_000, estimate_sobol_indices=True)
+SA.run(n_samples=500_000, estimate_sobol_indices=True)
 
 # %% [markdown]
 # **Chatterjee indices**
 
 # %%
-computed_indices["chatterjee_i"]
+SA.first_order_chatterjee_indices
 
 # **Plot the Chatterjee indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["chatterjee_i"][:, 0],
+    SA.first_order_chatterjee_indices[:, 0],
     plot_title="Chatterjee indices",
     color="C2",
 )
@@ -101,11 +101,11 @@
 # :math:`S_6` = 0.03760626
 
 # %%
-computed_indices["sobol_i"]
+SA.first_order_sobol_indices
 
 # **Plot the first order Sobol indices**
 fig2, ax2 = plot_sensitivity_index(
-    computed_indices["sobol_i"][:, 0],
+    SA.first_order_sobol_indices[:, 0],
     plot_title="First order Sobol indices",
     color="C0",
 )
@@ -143,12 +143,12 @@
 for i, sample_size in enumerate(sample_sizes):
 
     # Estimate using rank statistics
-    _indices = SA_chatterjee.run(n_samples=sample_size*7, estimate_sobol_indices=True)
-    store_rank_stats[:, i] = _indices["sobol_i"].ravel()
+    SA_chatterjee.run(n_samples=sample_size*7, estimate_sobol_indices=True)
+    store_rank_stats[:, i] = SA_chatterjee.first_order_sobol_indices.ravel()
 
     # Estimate using Pick and Freeze approach
-    _indices = SA_sobol.run(n_samples=sample_size)
-    store_pick_freeze[:, i] = _indices["sobol_i"].ravel()
+    SA_sobol.run(n_samples=sample_size)
+    store_pick_freeze[:, i] = SA_sobol.first_order_indices.ravel()
 
 # %%
 
diff --git a/docs/code/sensitivity/comparison/plot_additive.py b/docs/code/sensitivity/comparison/additive.py
similarity index 88%
rename from docs/code/sensitivity/comparison/plot_additive.py
rename to docs/code/sensitivity/comparison/additive.py
index 8dd101a1..1a533edd 100644
--- a/docs/code/sensitivity/comparison/plot_additive.py
+++ b/docs/code/sensitivity/comparison/additive.py
@@ -64,7 +64,7 @@
 # %% [markdown]
 SA_sobol = SobolSensitivity(runmodel_obj, dist_object)
 
-computed_indices_sobol = SA_sobol.run(n_samples=50_000)
+SA_sobol.run(n_samples=50_000)
 
 # %% [markdown]
 # **First order Sobol indices**
@@ -76,7 +76,7 @@
 # :math:`\mathrm{S}_2 = \frac{b^2 \cdot \mathbb{V}[X_2]}{a^2 \cdot \mathbb{V}[X_1] + b^2 \cdot \mathbb{V}[X_2]} = \frac{2^2 \cdot 1}{1^2 \cdot 1 + 2^2 \cdot 1} = 0.8`
 
 # %%
-computed_indices_sobol["sobol_i"]
+SA_sobol.first_order_indices
 
 # %% [markdown]
 # **Compute Chatterjee indices**
@@ -84,19 +84,19 @@
 # %% [markdown]
 SA_chatterjee = ChatterjeeSensitivity(runmodel_obj, dist_object)
 
-computed_indices_chatterjee = SA_chatterjee.run(n_samples=50_000)
+SA_chatterjee.run(n_samples=50_000)
 
 # %%
-computed_indices_chatterjee["chatterjee_i"]
+SA_chatterjee.first_order_chatterjee_indices
 
 # %%
 SA_cvm = cvm(runmodel_obj, dist_object)
 
 # Compute CVM indices using the pick and freeze algorithm
-computed_indices_cvm = SA_cvm.run(n_samples=20_000, estimate_sobol_indices=True)
+SA_cvm.run(n_samples=20_000, estimate_sobol_indices=True)
 
 # %%
-computed_indices_cvm["CVM_i"]
+SA_cvm.first_order_CramerVonMises_indices
 
 # %%
 # **Plot all indices**
@@ -106,9 +106,9 @@
 variable_names = [r"$X_{}$".format(i + 1) for i in range(num_vars)]
 
 # round to 2 decimal places
-indices_1 = np.around(computed_indices_sobol["sobol_i"][:, 0], decimals=2)
-indices_2 = np.around(computed_indices_chatterjee["chatterjee_i"][:, 0], decimals=2)
-indices_3 = np.around(computed_indices_cvm["CVM_i"][:, 0], decimals=2)
+indices_1 = np.around(SA_sobol.first_order_indices[:, 0], decimals=2)
+indices_2 = np.around(SA_chatterjee.first_order_chatterjee_indices[:, 0], decimals=2)
+indices_3 = np.around(SA_cvm.first_order_CramerVonMises_indices[:, 0], decimals=2)
 
 fig, ax = plt.subplots()
 width = 0.3
diff --git a/docs/code/sensitivity/comparison/plot_ishigami.py b/docs/code/sensitivity/comparison/ishigami.py
similarity index 86%
rename from docs/code/sensitivity/comparison/plot_ishigami.py
rename to docs/code/sensitivity/comparison/ishigami.py
index 958cd582..9d8d95a8 100644
--- a/docs/code/sensitivity/comparison/plot_ishigami.py
+++ b/docs/code/sensitivity/comparison/ishigami.py
@@ -52,7 +52,7 @@
 # %%
 SA_sobol = SobolSensitivity(runmodel_obj, dist_object)
 
-computed_indices_sobol = SA_sobol.run(n_samples=100_000)
+SA_sobol.run(n_samples=100_000)
 
 # %% [markdown]
 # **First order Sobol indices**
@@ -66,7 +66,7 @@
 # :math:`S_3` = 0.0
 
 # %%
-computed_indices_sobol["sobol_i"]
+SA_sobol.first_order_indices
 
 # %% [markdown]
 # **Total order Sobol indices**
@@ -80,7 +80,7 @@
 # :math:`S_{T_3}` =  0.24368366
 
 # %%
-computed_indices_sobol["sobol_total_i"]
+SA_sobol.total_order_indices
 
 # %% [markdown]
 # **Compute Chatterjee indices**
@@ -88,20 +88,20 @@
 # %% [markdown]
 SA_chatterjee = ChatterjeeSensitivity(runmodel_obj, dist_object)
 
-computed_indices_chatterjee = SA_chatterjee.run(n_samples=50_000)
+SA_chatterjee.run(n_samples=50_000)
 
 # %%
-computed_indices_chatterjee["chatterjee_i"]
+SA_chatterjee.first_order_chatterjee_indices
 
 # %% [markdown]
 # **Compute Cramér-von Mises indices**
 SA_cvm = cvm(runmodel_obj, dist_object)
 
 # Compute CVM indices using the pick and freeze algorithm
-computed_indices_cvm = SA_cvm.run(n_samples=20_000, estimate_sobol_indices=True)
+SA_cvm.run(n_samples=20_000, estimate_sobol_indices=True)
 
 # %%
-computed_indices_cvm["CVM_i"]
+SA_cvm.first_order_CramerVonMises_indices
 
 # %%
 # **Plot all indices**
@@ -111,9 +111,9 @@
 variable_names = [r"$X_{}$".format(i + 1) for i in range(num_vars)]
 
 # round to 2 decimal places
-indices_1 = np.around(computed_indices_sobol["sobol_i"][:, 0], decimals=2)
-indices_2 = np.around(computed_indices_chatterjee["chatterjee_i"][:, 0], decimals=2)
-indices_3 = np.around(computed_indices_cvm["CVM_i"][:, 0], decimals=2)
+indices_1 = np.around(SA_sobol.first_order_indices[:, 0], decimals=2)
+indices_2 = np.around(SA_chatterjee.first_order_chatterjee_indices[:, 0], decimals=2)
+indices_3 = np.around(SA_cvm.first_order_CramerVonMises_indices[:, 0], decimals=2)
 
 fig, ax = plt.subplots()
 width = 0.3
diff --git a/docs/code/sensitivity/cramer_von_mises/plot_cvm_exponential.py b/docs/code/sensitivity/cramer_von_mises/cvm_exponential.py
similarity index 88%
rename from docs/code/sensitivity/cramer_von_mises/plot_cvm_exponential.py
rename to docs/code/sensitivity/cramer_von_mises/cvm_exponential.py
index 81b258bf..8b1e2377 100644
--- a/docs/code/sensitivity/cramer_von_mises/plot_cvm_exponential.py
+++ b/docs/code/sensitivity/cramer_von_mises/cvm_exponential.py
@@ -48,7 +48,7 @@
 SA = cvm(runmodel_obj, dist_object)
 
 # Compute CVM indices using the pick and freeze algorithm
-computed_indices = SA.run(n_samples=20_000, estimate_sobol_indices=True)
+SA.run(n_samples=20_000, estimate_sobol_indices=True)
 
 # %% [markdown]
 # **Cramér-von Mises indices**
@@ -60,11 +60,11 @@
 # :math:`S^2_{CVM} = \frac{6}{\pi} \operatorname{arctan}(\sqrt{19}) - 2 \approx 0.5693`
 
 # %%
-computed_indices["CVM_i"]
+SA.first_order_CramerVonMises_indices
 
 # **Plot the CVM indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["CVM_i"][:, 0],
+    SA.first_order_CramerVonMises_indices[:, 0],
     plot_title="Cramér-von Mises indices",
     color="C4",
 )
@@ -79,11 +79,11 @@
 # :math:`S_2` = 0.3738
 
 # %%
-computed_indices["sobol_i"]
+SA.first_order_sobol_indices
 
 # **Plot the first order Sobol indices**
 fig2, ax2 = plot_sensitivity_index(
-    computed_indices["sobol_i"][:, 0],
+    SA.first_order_sobol_indices[:, 0],
     plot_title="First order Sobol indices",
     color="C0",
 )
@@ -92,12 +92,12 @@
 # **Estimated total order Sobol indices**
 
 # %%
-computed_indices["sobol_total_i"]
+SA.total_order_sobol_indices
 
 # **Plot the first and total order sensitivity indices**
 fig3, ax3 = plot_index_comparison(
-    computed_indices["sobol_i"][:, 0],
-    computed_indices["sobol_total_i"][:, 0],
+    SA.first_order_sobol_indices[:, 0],
+    SA.total_order_sobol_indices[:, 0],
     label_1="First order Sobol indices",
     label_2="Total order Sobol indices",
     plot_title="First and Total order Sobol indices",
diff --git a/docs/code/sensitivity/cramer_von_mises/plot_cvm_sobol_func.py b/docs/code/sensitivity/cramer_von_mises/cvm_sobol_func.py
similarity index 91%
rename from docs/code/sensitivity/cramer_von_mises/plot_cvm_sobol_func.py
rename to docs/code/sensitivity/cramer_von_mises/cvm_sobol_func.py
index a69ab0e9..cf99040c 100644
--- a/docs/code/sensitivity/cramer_von_mises/plot_cvm_sobol_func.py
+++ b/docs/code/sensitivity/cramer_von_mises/cvm_sobol_func.py
@@ -62,17 +62,17 @@
 SA = cvm(runmodel_obj, dist_object)
 
 # Compute Sobol indices using rank statistics
-computed_indices = SA.run(n_samples=50_000, estimate_sobol_indices=True)
+SA.run(n_samples=50_000, estimate_sobol_indices=True)
 
 # %% [markdown]
 # **Cramér-von Mises indices**
 
 # %%
-computed_indices["CVM_i"]
+SA.first_order_CramerVonMises_indices
 
 # **Plot the CVM indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["CVM_i"][:, 0],
+    SA.first_order_CramerVonMises_indices[:, 0],
     plot_title="Cramér-von Mises indices",
     color="C4",
 )
@@ -95,11 +95,11 @@
 # :math:`S_6` = 0.03760626
 
 # %%
-computed_indices["sobol_i"]
+SA.total_order_sobol_indices
 
 # **Plot the first order Sobol indices**
 fig2, ax2 = plot_sensitivity_index(
-    computed_indices["sobol_i"][:, 0],
+    SA.total_order_sobol_indices[:, 0],
     plot_title="First order Sobol indices",
     color="C0",
 )
diff --git a/docs/code/sensitivity/generalised_sobol/plot_generalised_sobol_mechanical_oscillator_ODE.py b/docs/code/sensitivity/generalised_sobol/generalised_sobol_mechanical_oscillator_ODE.py
similarity index 91%
rename from docs/code/sensitivity/generalised_sobol/plot_generalised_sobol_mechanical_oscillator_ODE.py
rename to docs/code/sensitivity/generalised_sobol/generalised_sobol_mechanical_oscillator_ODE.py
index e9900578..8ce86328 100644
--- a/docs/code/sensitivity/generalised_sobol/plot_generalised_sobol_mechanical_oscillator_ODE.py
+++ b/docs/code/sensitivity/generalised_sobol/generalised_sobol_mechanical_oscillator_ODE.py
@@ -63,7 +63,7 @@
 # %% [markdown]
 SA = GeneralisedSobolSensitivity(runmodel_obj, dist_object)
 
-computed_indices = SA.run(n_samples=500)
+SA.run(n_samples=500)
 
 # %% [markdown]
 # **First order Generalised Sobol indices**
@@ -79,11 +79,11 @@
 # :math:`GS_{\ell}` = 0.0561
 
 # %%
-computed_indices["gen_sobol_i"]
+SA.generalized_first_order_indices
 
 # **Plot the first order sensitivity indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["gen_sobol_i"][:, 0],
+    SA.generalized_first_order_indices[:, 0],
     plot_title="First order Generalised Sobol indices",
     variable_names=[r"$m$", "$c$", "$k$", "$\ell$"],
     color="C0",
@@ -93,12 +93,12 @@
 # **Total order Generalised Sobol indices**
 
 # %%
-computed_indices["gen_sobol_total_i"]
+SA.generalized_total_order_indices
 
 # **Plot the first and total order sensitivity indices**
 fig2, ax2 = plot_index_comparison(
-    computed_indices["gen_sobol_i"][:, 0],
-    computed_indices["gen_sobol_total_i"][:, 0],
+    SA.generalized_first_order_indices[:, 0],
+    SA.generalized_total_order_indices[:, 0],
     label_1="First order",
     label_2="Total order",
     plot_title="First and Total order Generalised Sobol indices",
diff --git a/docs/code/sensitivity/generalised_sobol/plot_generalised_sobol_multioutput.py b/docs/code/sensitivity/generalised_sobol/generalised_sobol_multioutput.py
similarity index 76%
rename from docs/code/sensitivity/generalised_sobol/plot_generalised_sobol_multioutput.py
rename to docs/code/sensitivity/generalised_sobol/generalised_sobol_multioutput.py
index f4347d84..f17f6265 100644
--- a/docs/code/sensitivity/generalised_sobol/plot_generalised_sobol_multioutput.py
+++ b/docs/code/sensitivity/generalised_sobol/generalised_sobol_multioutput.py
@@ -54,7 +54,7 @@
 # %% [markdown]
 SA = GeneralisedSobolSensitivity(runmodel_obj, dist_object_1)
 
-computed_indices = SA.run(
+SA.run(
     n_samples=20_000, confidence_level=0.95, n_bootstrap_samples=5_00
 )
 
@@ -70,25 +70,25 @@
 # :math:`GS_2` = 0.1179
 
 # %%
-computed_indices["gen_sobol_i"]
+SA.generalized_first_order_indices
 
 # **Plot the first order sensitivity indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["gen_sobol_i"][:, 0],
-    confidence_interval=computed_indices["confidence_interval_gen_sobol_i"],
+    SA.generalized_first_order_indices[:, 0],
+    confidence_interval=SA.first_order_confidence_interval,
     plot_title="First order Generalised Sobol indices",
     color="C0",
 )
 
 # %%
-computed_indices["gen_sobol_total_i"]
+SA.generalized_total_order_indices
 
 # **Plot the first and total order sensitivity indices**
 fig2, ax2 = plot_index_comparison(
-    computed_indices["gen_sobol_i"][:, 0],
-    computed_indices["gen_sobol_total_i"][:, 0],
-    confidence_interval_1=computed_indices["confidence_interval_gen_sobol_i"],
-    confidence_interval_2=computed_indices["confidence_interval_gen_sobol_total_i"],
+    SA.generalized_first_order_indices[:, 0],
+    SA.generalized_total_order_indices[:, 0],
+    confidence_interval_1=SA.first_order_confidence_interval,
+    confidence_interval_2=SA.total_order_confidence_interval,
     label_1="First order",
     label_2="Total order",
     plot_title="First and Total order Generalised Sobol indices",
@@ -102,7 +102,7 @@
 
 SA = GeneralisedSobolSensitivity(runmodel_obj, dist_object_2)
 
-computed_indices = SA.run(
+SA.run(
     n_samples=20_000, confidence_level=0.95, n_bootstrap_samples=5_00
 )
 
@@ -118,25 +118,25 @@
 # :math:`GS_2` = 0.3566
 
 # %%
-computed_indices["gen_sobol_i"]
+SA.generalized_first_order_indices
 
 # **Plot the first order sensitivity indices**
 fig3, ax3 = plot_sensitivity_index(
-    computed_indices["gen_sobol_i"][:, 0],
-    confidence_interval=computed_indices["confidence_interval_gen_sobol_i"],
+    SA.generalized_first_order_indices[:, 0],
+    confidence_interval=SA.first_order_confidence_interval,
     plot_title="First order Generalised Sobol indices",
     color="C0",
 )
 
 # %%
-computed_indices["gen_sobol_total_i"]
+SA.total_order_confidence_interval
 
 # **Plot the first and total order sensitivity indices**
 fig4, ax4 = plot_index_comparison(
-    computed_indices["gen_sobol_i"][:, 0],
-    computed_indices["gen_sobol_total_i"][:, 0],
-    confidence_interval_1=computed_indices["confidence_interval_gen_sobol_i"],
-    confidence_interval_2=computed_indices["confidence_interval_gen_sobol_total_i"],
+    SA.generalized_first_order_indices[:, 0],
+    SA.total_order_confidence_interval[:, 0],
+    confidence_interval_1=SA.first_order_confidence_interval,
+    confidence_interval_2=SA.total_order_confidence_interval,
     label_1="First order",
     label_2="Total order",
     plot_title="First and Total order Generalised Sobol indices",
diff --git a/docs/code/sensitivity/sobol/plot_mechanical_oscillator_ODE.py b/docs/code/sensitivity/sobol/mechanical_oscillator_ODE.py
similarity index 78%
rename from docs/code/sensitivity/sobol/plot_mechanical_oscillator_ODE.py
rename to docs/code/sensitivity/sobol/mechanical_oscillator_ODE.py
index 0403e8a5..5dcbb0ba 100644
--- a/docs/code/sensitivity/sobol/plot_mechanical_oscillator_ODE.py
+++ b/docs/code/sensitivity/sobol/mechanical_oscillator_ODE.py
@@ -61,7 +61,7 @@
 # %% [markdown]
 SA = SobolSensitivity(runmodel_obj, dist_object)
 
-computed_indices = SA.run(n_samples=500)
+SA.run(n_samples=500)
 
 # %%
 # **Plot the Sobol indices**
@@ -74,12 +74,10 @@
 
 fig, ax = plt.subplots(1, 2, figsize=(16, 8))
 
-ax[0].plot(T, computed_indices["sobol_total_i"][0, :], "r", label=r"$m$")
-ax[0].plot(T, computed_indices["sobol_total_i"][1, :], "g", label=r"$c$")
-ax[0].plot(T, computed_indices["sobol_total_i"][2, :], label=r"$k$", color="royalblue")
-ax[0].plot(
-    T, computed_indices["sobol_total_i"][3, :], label=r"$\ell$", color="aquamarine"
-)
+ax[0].plot(T, SA.total_order_indices[0, :], "r", label=r"$m$")
+ax[0].plot(T, SA.total_order_indices[1, :], "g", label=r"$c$")
+ax[0].plot(T, SA.total_order_indices[2, :], label=r"$k$", color="royalblue")
+ax[0].plot(T, SA.total_order_indices[3, :], label=r"$\ell$", color="aquamarine")
 
 ax[0].set_title("Total order Sobol indices", fontsize=16)
 ax[0].set_xlabel("time (s)", fontsize=16)
@@ -88,10 +86,10 @@
 ax[0].set_ybound(-0.2, 1.2)
 ax[0].legend()
 
-ax[1].plot(T, computed_indices["sobol_i"][0, :], "r", label=r"$m$")
-ax[1].plot(T, computed_indices["sobol_i"][1, :], "g", label=r"$c$")
-ax[1].plot(T, computed_indices["sobol_i"][2, :], label=r"$k$", color="royalblue")
-ax[1].plot(T, computed_indices["sobol_i"][3, :], label=r"$\ell$", color="aquamarine")
+ax[1].plot(T, SA.first_order_indices[0, :], "r", label=r"$m$")
+ax[1].plot(T, SA.first_order_indices[1, :], "g", label=r"$c$")
+ax[1].plot(T, SA.first_order_indices[2, :], label=r"$k$", color="royalblue")
+ax[1].plot(T, SA.first_order_indices[3, :], label=r"$\ell$", color="aquamarine")
 
 ax[1].set_title("First order Sobol indices", fontsize=16)
 ax[1].set_xlabel("time (s)", fontsize=16)
diff --git a/docs/code/sensitivity/sobol/plot_sobol_additive.py b/docs/code/sensitivity/sobol/sobol_additive.py
similarity index 92%
rename from docs/code/sensitivity/sobol/plot_sobol_additive.py
rename to docs/code/sensitivity/sobol/sobol_additive.py
index dca212fe..d3749cad 100644
--- a/docs/code/sensitivity/sobol/plot_sobol_additive.py
+++ b/docs/code/sensitivity/sobol/sobol_additive.py
@@ -53,7 +53,7 @@
 # %% [markdown]
 SA = SobolSensitivity(runmodel_obj, dist_object)
 
-computed_indices = SA.run(n_samples=50_000)
+SA.run(n_samples=50_000)
 
 # %% [markdown]
 # **First order Sobol indices**
@@ -65,13 +65,13 @@
 # :math:`\mathrm{S}_2 = \frac{b^2 \cdot \mathbb{V}[X_2]}{a^2 \cdot \mathbb{V}[X_1] + b^2 \cdot \mathbb{V}[X_2]} = \frac{2^2 \cdot 1}{1^2 \cdot 1 + 2^2 \cdot 1} = 0.8`
 
 # %%
-computed_indices["sobol_i"]
+SA.first_order_indices
 
 # %%
 # **Plot the first and total order sensitivity indices**
 fig1, ax1 = plot_index_comparison(
-    computed_indices["sobol_i"][:, 0],
-    computed_indices["sobol_total_i"][:, 0],
+    SA.first_order_indices[:, 0],
+    SA.total_order_indices[:, 0],
     label_1="First order Sobol indices",
     label_2="Total order Sobol indices",
     plot_title="First and Total order Sobol indices",
diff --git a/docs/code/sensitivity/sobol/plot_sobol_func.py b/docs/code/sensitivity/sobol/sobol_func.py
similarity index 91%
rename from docs/code/sensitivity/sobol/plot_sobol_func.py
rename to docs/code/sensitivity/sobol/sobol_func.py
index ba28d3e7..5dfdf1dc 100644
--- a/docs/code/sensitivity/sobol/plot_sobol_func.py
+++ b/docs/code/sensitivity/sobol/sobol_func.py
@@ -62,7 +62,7 @@
 SA = SobolSensitivity(runmodel_obj, dist_object)
 
 # Compute Sobol indices using the pick and freeze algorithm
-computed_indices = SA.run(n_samples=50_000, estimate_second_order=True)
+SA.run(n_samples=50_000, estimate_second_order=True)
 
 # %% [markdown]
 # **First order Sobol indices**
@@ -82,12 +82,12 @@
 # :math:`S_6` = 5.86781190e-05
 
 # %%
-computed_indices["sobol_i"]
+SA.first_order_indices
 
 # %%
 # **Plot the first order sensitivity indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["sobol_i"][:, 0],
+    SA.first_order_indices[:, 0],
     plot_title="First order Sobol indices",
     color="C0",
 )
@@ -111,13 +111,13 @@
 #
 
 # %%
-computed_indices["sobol_total_i"]
+SA.total_order_indices
 
 # %%
 # **Plot the first and total order sensitivity indices**
 fig2, ax2 = plot_index_comparison(
-    computed_indices["sobol_i"][:, 0],
-    computed_indices["sobol_total_i"][:, 0],
+    SA.first_order_indices[:, 0],
+    SA.total_order_indices[:, 0],
     label_1="First order Sobol indices",
     label_2="Total order Sobol indices",
     plot_title="First and Total order Sobol indices",
@@ -159,11 +159,11 @@
 # :math:`S_{T_{56}}` = 2.0e-9
 
 # %%
-computed_indices["sobol_ij"]
+SA.second_order_indices
 
 # %%
 # **Plot the second order sensitivity indices**
 fig3, ax3 = plot_second_order_indices(
-    computed_indices["sobol_ij"][:, 0],
+    SA.second_order_indices[:, 0],
     num_vars=num_vars,
 )
diff --git a/docs/code/sensitivity/sobol/plot_sobol_ishigami.py b/docs/code/sensitivity/sobol/sobol_ishigami.py
similarity index 84%
rename from docs/code/sensitivity/sobol/plot_sobol_ishigami.py
rename to docs/code/sensitivity/sobol/sobol_ishigami.py
index 664bbc0b..5875b8af 100644
--- a/docs/code/sensitivity/sobol/plot_sobol_ishigami.py
+++ b/docs/code/sensitivity/sobol/sobol_ishigami.py
@@ -74,7 +74,7 @@
 # %%
 SA = SobolSensitivity(runmodel_obj, dist_object)
 
-computed_indices = SA.run(n_samples=100_000, n_bootstrap_samples=100)
+SA.run(n_samples=100_000, n_bootstrap_samples=100)
 
 # %% [markdown]
 # **First order Sobol indices**
@@ -88,7 +88,7 @@
 # :math:`S_3` = 0.0
 
 # %%
-computed_indices["sobol_i"]
+SA.first_order_indices
 
 # %% [markdown]
 # **Total order Sobol indices**
@@ -102,25 +102,25 @@
 # :math:`S_{T_3}` =  0.24368366
 
 # %%
-computed_indices["sobol_total_i"]
+SA.total_order_indices
 
 # %% [markdown]
 # **Confidence intervals for first order Sobol indices**
 
 # %%
-computed_indices["confidence_interval_sobol_i"]
+SA.first_order_confidence_interval
 
 # %% [markdown]
 # **Confidence intervals for total order Sobol indices**
 
 # %%
-computed_indices["confidence_interval_sobol_total_i"]
+SA.total_order_confidence_interval
 
 # %%
 # **Plot the first order sensitivity indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["sobol_i"][:, 0],
-    confidence_interval=computed_indices["confidence_interval_sobol_i"],
+    SA.first_order_indices[:, 0],
+    confidence_interval=SA.first_order_confidence_interval,
     plot_title="First order Sobol indices",
     variable_names=["$X_1$", "$X_2$", "$X_3$"],
     color="C0",
@@ -129,10 +129,10 @@
 # %%
 # **Plot the first and total order sensitivity indices**
 fig2, ax2 = plot_index_comparison(
-    computed_indices["sobol_i"][:, 0],
-    computed_indices["sobol_total_i"][:, 0],
-    confidence_interval_1=computed_indices["confidence_interval_sobol_i"],
-    confidence_interval_2=computed_indices["confidence_interval_sobol_total_i"],
+    SA.first_order_indices[:, 0],
+    SA.total_order_indices[:, 0],
+    confidence_interval_1=SA.first_order_confidence_interval,
+    confidence_interval_2=SA.total_order_confidence_interval,
     label_1="First order Sobol indices",
     label_2="Total order Sobol indices",
     plot_title="First and Total order Sobol indices",
diff --git a/src/UQpy/sensitivity/SobolSensitivity.py b/src/UQpy/sensitivity/SobolSensitivity.py
index 967f8f96..294bc3c5 100644
--- a/src/UQpy/sensitivity/SobolSensitivity.py
+++ b/src/UQpy/sensitivity/SobolSensitivity.py
@@ -249,10 +249,6 @@ def run(
 
         self.logger.info("UQpy: All model evaluations computed successfully.")
 
-        ######################### STORAGE ########################
-
-        # Create dictionary to store the sensitivity indices
-        computed_indices = {}
 
         ################## COMPUTE SOBOL INDICES ##################
 
@@ -267,8 +263,6 @@ def run(
 
         self.logger.info("UQpy: First order Sobol indices computed successfully.")
 
-        computed_indices["sobol_i"] = self.first_order_indices
-
         # Total order Sobol indices
         self.total_order_indices = compute_total_order(
             A_model_evals,
@@ -280,8 +274,6 @@ def run(
 
         self.logger.info("UQpy: Total order Sobol indices computed successfully.")
 
-        computed_indices["sobol_total_i"] = self.total_order_indices
-
         if estimate_second_order:
 
             # Second order Sobol indices
@@ -290,13 +282,12 @@ def run(
                 B_model_evals,
                 C_i_model_evals,
                 D_i_model_evals,
-                computed_indices["sobol_i"],
+                self.first_order_indices,
                 scheme=second_order_scheme,
             )
 
             self.logger.info("UQpy: Second order Sobol indices computed successfully.")
 
-            computed_indices["sobol_ij"] = self.second_order_indices
 
         ################## CONFIDENCE INTERVALS ####################
 
@@ -315,7 +306,7 @@ def run(
             self.first_order_confidence_interval = self.bootstrapping(
                 compute_first_order,
                 estimator_inputs,
-                computed_indices["sobol_i"],
+                self.first_order_indices,
                 n_bootstrap_samples,
                 confidence_level,
                 scheme=first_order_scheme,
@@ -325,15 +316,11 @@ def run(
                 "UQpy: Confidence intervals for First order Sobol indices computed successfully."
             )
 
-            computed_indices[
-                "confidence_interval_sobol_i"
-            ] = self.first_order_confidence_interval
-
             # Total order Sobol indices
             self.total_order_confidence_interval = self.bootstrapping(
                 compute_total_order,
                 estimator_inputs,
-                computed_indices["sobol_total_i"],
+                self.total_order_indices,
                 n_bootstrap_samples,
                 confidence_level,
                 scheme=total_order_scheme,
@@ -343,19 +330,16 @@ def run(
                 "UQpy: Confidence intervals for Total order Sobol indices computed successfully."
             )
 
-            computed_indices[
-                "confidence_interval_sobol_total_i"
-            ] = self.total_order_confidence_interval
 
             # Second order Sobol indices
             if estimate_second_order:
                 self.second_order_confidence_interval = self.bootstrapping(
                     compute_second_order,
                     estimator_inputs,
-                    computed_indices["sobol_ij"],
+                    self.second_order_indices,
                     n_bootstrap_samples,
                     confidence_level,
-                    first_order_sobol=computed_indices["sobol_i"],
+                    first_order_sobol=self.first_order_indices,
                     scheme=second_order_scheme,
                 )
 
@@ -363,12 +347,6 @@ def run(
                     "UQpy: Confidence intervals for Second order Sobol indices computed successfully."
                 )
 
-                computed_indices[
-                    "confidence_interval_sobol_ij"
-                ] = self.second_order_confidence_interval
-
-        return computed_indices
-
 
 ###################### Pick and Freeze Methods #####################