diff --git a/pyproject.toml b/pyproject.toml
index 51175d22..6eb96143 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,8 +8,7 @@ dependencies = [
     "plotly<6.0.0,>=5.13.1",
     "pandas>=2.2.3",
     "polarstate==0.1.8",
-    "smoothstate>=0.1.0",
-    "statsmodels>=0.14.0",
+    "smoothstate>=0.1.1",
     "polars>=1.31.0",
     "reactable>=0.1.5",
     "great-tables>=0.18.0",
diff --git a/src/rtichoke/calibration/calibration.py b/src/rtichoke/calibration/calibration.py
index e031ba3e..05954caf 100644
--- a/src/rtichoke/calibration/calibration.py
+++ b/src/rtichoke/calibration/calibration.py
@@ -11,6 +11,7 @@
 import polars as pl
 import numpy as np
 from polarstate import predict_aj_estimates, prepare_event_table
+from smoothstate import smooth_state_lowess
 from ._secondary_cox import calculate_secondary_cox_smooth
 
 # from rtichoke.helpers.send_post_request_to_r_rtichoke import send_requests_to_rtichoke_r
@@ -757,8 +758,6 @@ def _calculate_smooth_curve(
     """
     Calculate the smoothed calibration curve using lowess.
     """
-    from statsmodels.nonparametric.smoothers_lowess import lowess
-
     smooth_frames = []
 
     # Helper function to process a single probability and real array
@@ -772,12 +771,9 @@ def process_single_array(p, r, group_name):
                 }
             )
         else:
-            # lowess returns a 2D array where the first column is x and the second is y
-            smoothed = lowess(r, p, it=0)
-            xout = np.linspace(0, 1, 101)
-            yout = np.clip(np.interp(xout, smoothed[:, 0], smoothed[:, 1]), 0.0, 1.0)
-            return pl.DataFrame(
-                {"x": xout, "y": yout, "reference_group": [group_name] * len(xout)}
+            smoothed = smooth_state_lowess(p, r)
+            return smoothed.with_columns(
+                pl.lit(group_name).alias("reference_group")
             )
 
     if isinstance(reals, dict):