diff --git a/pysr/__init__.py b/pysr/__init__.py
index 5f220035..99c6a974 100644
--- a/pysr/__init__.py
+++ b/pysr/__init__.py
@@ -1,9 +1,10 @@
 from . import sklearn_monkeypatch
+from .deprecated import best, best_callable, best_row, best_tex, pysr
 from .export_jax import sympy2jax
 from .export_torch import sympy2torch
 from .feynman_problems import FeynmanProblem, Problem
 from .julia_helpers import install
-from .sr import PySRRegressor, best, best_callable, best_row, best_tex, pysr
+from .sr import PySRRegressor
 from .version import __version__
 
 __all__ = [
diff --git a/pysr/deprecated.py b/pysr/deprecated.py
index ea592272..ecfeb45e 100644
--- a/pysr/deprecated.py
+++ b/pysr/deprecated.py
@@ -1,4 +1,58 @@
 """Various functions to deprecate features."""
+import warnings
+
+
+def pysr(X, y, weights=None, **kwargs):  # pragma: no cover
+    from .sr import PySRRegressor
+
+    warnings.warn(
+        "Calling `pysr` is deprecated. "
+        "Please use `model = PySRRegressor(**params); "
+        "model.fit(X, y)` going forward.",
+        FutureWarning,
+    )
+    model = PySRRegressor(**kwargs)
+    model.fit(X, y, weights=weights)
+    return model.equations_
+
+
+def best(*args, **kwargs):  # pragma: no cover
+    raise NotImplementedError(
+        "`best` has been deprecated. "
+        "Please use the `PySRRegressor` interface. "
+        "After fitting, you can return `.sympy()` "
+        "to get the sympy representation "
+        "of the best equation."
+    )
+
+
+def best_row(*args, **kwargs):  # pragma: no cover
+    raise NotImplementedError(
+        "`best_row` has been deprecated. "
+        "Please use the `PySRRegressor` interface. "
+        "After fitting, you can run `print(model)` to view the best equation, "
+        "or "
+        "`model.get_best()` to return the best equation's "
+        "row in `model.equations_`."
+    )
+
+
+def best_tex(*args, **kwargs):  # pragma: no cover
+    raise NotImplementedError(
+        "`best_tex` has been deprecated. "
+        "Please use the `PySRRegressor` interface. "
+        "After fitting, you can return `.latex()` to "
+        "get the sympy representation "
+        "of the best equation."
+    )
+
+
+def best_callable(*args, **kwargs):  # pragma: no cover
+    raise NotImplementedError(
+        "`best_callable` has been deprecated. Please use the `PySRRegressor` "
+        "interface. After fitting, you can use "
+        "`.predict(X)` to use the best callable."
+    )
 
 
 def make_deprecated_kwargs_for_pysr_regressor():
diff --git a/pysr/feynman_problems.py b/pysr/feynman_problems.py
index a264a901..b64b4139 100644
--- a/pysr/feynman_problems.py
+++ b/pysr/feynman_problems.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from .sr import best, pysr
+from .deprecated import best, pysr
 
 PKG_DIR = Path(__file__).parents[1]
 FEYNMAN_DATASET = PKG_DIR / "datasets" / "FeynmanEquations.csv"
diff --git a/pysr/sr.py b/pysr/sr.py
index 71df6cac..d824b52c 100644
--- a/pysr/sr.py
+++ b/pysr/sr.py
@@ -48,17 +48,6 @@
 already_ran = False
 
 
-def pysr(X, y, weights=None, **kwargs):  # pragma: no cover
-    warnings.warn(
-        "Calling `pysr` is deprecated. "
-        "Please use `model = PySRRegressor(**params); model.fit(X, y)` going forward.",
-        FutureWarning,
-    )
-    model = PySRRegressor(**kwargs)
-    model.fit(X, y, weights=weights)
-    return model.equations_
-
-
 def _process_constraints(binary_operators, unary_operators, constraints):
     constraints = constraints.copy()
     for op in unary_operators:
@@ -181,37 +170,6 @@ def _check_assertions(
             )
 
 
-def best(*args, **kwargs):  # pragma: no cover
-    raise NotImplementedError(
-        "`best` has been deprecated. Please use the `PySRRegressor` interface. "
-        "After fitting, you can return `.sympy()` to get the sympy representation "
-        "of the best equation."
-    )
-
-
-def best_row(*args, **kwargs):  # pragma: no cover
-    raise NotImplementedError(
-        "`best_row` has been deprecated. Please use the `PySRRegressor` interface. "
-        "After fitting, you can run `print(model)` to view the best equation, or "
-        "`model.get_best()` to return the best equation's row in `model.equations_`."
-    )
-
-
-def best_tex(*args, **kwargs):  # pragma: no cover
-    raise NotImplementedError(
-        "`best_tex` has been deprecated. Please use the `PySRRegressor` interface. "
-        "After fitting, you can return `.latex()` to get the sympy representation "
-        "of the best equation."
-    )
-
-
-def best_callable(*args, **kwargs):  # pragma: no cover
-    raise NotImplementedError(
-        "`best_callable` has been deprecated. Please use the `PySRRegressor` "
-        "interface. After fitting, you can use `.predict(X)` to use the best callable."
-    )
-
-
 # Class validation constants
 VALID_OPTIMIZER_ALGORITHMS = ["NelderMead", "BFGS"]