diff --git a/pyciemss/interfaces.py b/pyciemss/interfaces.py
index 120c44370..e68be4410 100644
--- a/pyciemss/interfaces.py
+++ b/pyciemss/interfaces.py
@@ -1,5 +1,5 @@
 import contextlib
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Union
 
 import pyro
 import torch
@@ -331,7 +331,7 @@ def calibrate(
     num_particles: int = 1,
     deterministic_learnable_parameters: List[str] = [],
     progress_hook: Callable = lambda i, loss: None,
-) -> Tuple[pyro.nn.PyroModule, float]:
+) -> Dict[str, Any]:
     """
     Infer parameters for a DynamicalSystem model conditional on data.
     This uses variational inference with a mean-field variational family to infer the parameters of the model.
@@ -412,11 +412,13 @@ def calibrate(
             - This can be used to implement custom progress bars.
 
     Returns:
-        - inferred_parameters: pyro.nn.PyroModule
-            - A Pyro module that contains the inferred parameters of the model.
-            - This can be passed to `sample` to sample from the model conditional on the data.
-        - loss: float
-            - The final loss value of the approximate ELBO loss.
+        result: Dict[str, Any]
+            - Dictionary with the following key-value pairs.
+                - inferred_parameters: pyro.nn.PyroModule
+                    - A Pyro module that contains the inferred parameters of the model.
+                    - This can be passed to `sample` to sample from the model conditional on the data.
+                - loss: float
+                    - The final loss value of the approximate ELBO loss.
     """
 
     pyro.clear_param_store()
@@ -511,7 +513,7 @@ def wrapped_model():
             if i % 25 == 0:
                 print(f"iteration {i}: loss = {loss}")
 
-    return inferred_parameters, loss
+    return {"inferred_parameters": inferred_parameters, "loss": loss}
 
 
 # # TODO
diff --git a/setup.cfg b/setup.cfg
index cb6432b71..4ad546b3e 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = pyciemss
-version = 0.1.0
+version = 0.1.1
 
 license = BSD-3-Clause
 license_files = LICENSE
@@ -24,6 +24,7 @@ packages =
     pyciemss
     pyciemss.integration_utils
     pyciemss.mira_integration
+    pyciemss.ensemble
 
 [options.package_data]
 * = *.json
diff --git a/tests/test_interfaces.py b/tests/test_interfaces.py
index 58a0913d3..8af70d3e4 100644
--- a/tests/test_interfaces.py
+++ b/tests/test_interfaces.py
@@ -210,7 +210,9 @@ def test_calibrate_no_kwargs(model_fixture, start_time, end_time, logging_step_s
     }
 
     with pyro.poutine.seed(rng_seed=0):
-        inferred_parameters, _ = calibrate(*calibrate_args, **calibrate_kwargs)
+        inferred_parameters = calibrate(*calibrate_args, **calibrate_kwargs)[
+            "inferred_parameters"
+        ]
 
     assert isinstance(inferred_parameters, pyro.nn.PyroModule)
 
@@ -255,7 +257,8 @@ def test_calibrate_deterministic(
     }
 
     with pyro.poutine.seed(rng_seed=0):
-        inferred_parameters, _ = calibrate(*calibrate_args, **calibrate_kwargs)
+        output = calibrate(*calibrate_args, **calibrate_kwargs)
+        inferred_parameters = output["inferred_parameters"]
 
     assert isinstance(inferred_parameters, pyro.nn.PyroModule)
 
@@ -307,7 +310,7 @@ def test_calibrate_interventions(
     }
 
     with pyro.poutine.seed(rng_seed=0):
-        _, loss = calibrate(*calibrate_args, **calibrate_kwargs)
+        loss = calibrate(*calibrate_args, **calibrate_kwargs)["loss"]
 
     # SETUP INTERVENTION
 
@@ -334,8 +337,11 @@ def time_key(time, _):
     }
 
     with pyro.poutine.seed(rng_seed=0):
-        intervened_parameters, intervened_loss = calibrate(
-            *calibrate_args, **calibrate_kwargs
+        output = calibrate(*calibrate_args, **calibrate_kwargs)
+
+        intervened_parameters, intervened_loss = (
+            output["inferred_parameters"],
+            output["loss"],
         )
 
     assert intervened_loss != loss