diff --git a/pyproject.toml b/pyproject.toml
index a40a727..aed2303 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ name = "growthcurves"
 # See the section below: [tools.setuptools.dynamic]
 dynamic = [
   "version", # version is loaded from the package
-  "dependencies", # add if using requirements.txt
+  # "dependencies", # add if using requirements.txt
 ]
 readme = "README.md"
 requires-python = ">=3.9" # test all higher Python versions
@@ -19,12 +19,14 @@ classifiers = [
 # Also update LICENSE file if you pick another one
 license = "GPL-3.0-or-later" # https://choosealicense.com/licenses/gpl-3.0/
 # # add dependencies here: (use one of the two)
-# dependencies = ["numpy", "pandas", "scipy", "matplotlib", "seaborn"]
+dependencies = [
+  "numpy", "scipy", "plotly", "scikit-learn",
+]
 # use requirements.txt instead of pyproject.toml for dependencies
 # https://stackoverflow.com/a/73600610/9684872
 # ! uncomment also dependencies in the dynamic section above
-[tool.setuptools.dynamic]
-dependencies = {file = ["requirements.txt"]}
+# [tool.setuptools.dynamic]
+# dependencies = {file = ["requirements.txt"]}
 
 
 [project.urls]
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 8296a3f..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-numpy>=1.20.0
-scipy>=1.7.0
-plotly>=5.0.0
diff --git a/src/growthcurves/non_parametric.py b/src/growthcurves/non_parametric.py
index f7bae0e..79cf24e 100644
--- a/src/growthcurves/non_parametric.py
+++ b/src/growthcurves/non_parametric.py
@@ -9,11 +9,14 @@
 from logging import getLogger
 
 import numpy as np
+import sklearn.linear_model
 from scipy.interpolate import make_smoothing_spline
-from scipy.stats import theilslopes
 
 from .inference import bad_fit_stats
 
+# from scipy.stats import theilslopes
+
+
 logger = getLogger(__name__)
 
 # Default settings for the auto-spline (sigma-based smoothing + OD weights).
@@ -79,7 +82,7 @@ def fit_sliding_window(t, N, window_points=15, step=None, n_fits=None, **kwargs)
             step = 1
         else:
             step = max(1, int(len(t) / n_fits))
-
+    huber_regressor = sklearn.linear_model.HuberRegressor()
     # limit number of fits to avoid excessive computation using step parameter
     for i in range(0, len(t) - w + 1, step):
         t_win = t[i : i + w]
@@ -88,9 +91,13 @@ def fit_sliding_window(t, N, window_points=15, step=None, n_fits=None, **kwargs)
         if np.ptp(t_win) <= 0:
             continue
 
-        # Use Theil-Sen estimator for robust line fitting
-        result = theilslopes(y_log_win, t_win)
-        slope, intercept = result.slope, result.intercept
+        # # Use Theil-Sen estimator for robust line fitting
+        # result = theilslopes(y_log_win, t_win)
+        # slope, intercept = result.slope, result.intercept
+        # # Use HuberRegressor which uses L2 regularization and is twice as fast as
+        # # Theil-Sen.
+        result = huber_regressor.fit(t_win.reshape(-1, 1), y_log_win)
+        slope, intercept = result.coef_[0], result.intercept_
 
         if slope > best_slope:
             best_slope = slope