Add example for Python/R interoperability using mass univariate t-test

doc/changes/dev/13729.newfeature.rst

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+Add example showing how to run a mass-univariate t-test in both Python and R using :mod:`rpy2`, demonstrating Python–R interoperability with :class:`~mne.Epochs` data (:gh:`13729` by `Aman Srivastava`).

examples/preprocessing/r_interop.py

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+"""
+.. _r-interop:
+
+==============================================
+Mass-univariate t-test: Python and R compared
+==============================================
+
+This example shows how to run a mass-univariate 2-sample t-test on
+:class:`~mne.Epochs` data in Python using :func:`scipy.stats.ttest_ind`,
+then run the equivalent test in R via :mod:`rpy2`, and confirm that both
+approaches give identical results.
+
+This is useful when you want to leverage R's statistical ecosystem (e.g.,
+``lme4``, ``EMMeans``) while keeping your data loading and visualization
+in Python.
+
+.. note::
+    This example requires ``rpy2`` to be installed (``pip install rpy2``)
+    and a working R installation with the ``stats`` package (included by
+    default in R).
+"""
+# Authors: Aman Srivastava <amansri345@gmail.com>
+#
+# License: BSD-3-Clause
+# Copyright the MNE-Python contributors.
+
+# %%
+# Load sample data and create Epochs
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# We use the MNE sample dataset and create epochs for two conditions:
+# auditory/left and auditory/right.
+
+import rpy2.robjects as ro
+from rpy2.robjects import default_converter, numpy2ri
+from rpy2.robjects.conversion import localconverter
+from scipy import stats
+
+import mne
+
+data_path = mne.datasets.sample.data_path()
+raw_fname = data_path / "MEG" / "sample" / "sample_audvis_filt-0-40_raw.fif"
+event_fname = data_path / "MEG" / "sample" / "sample_audvis_filt-0-40_raw-eve.fif"
+
+raw = mne.io.read_raw_fif(raw_fname, preload=True)
+events = mne.read_events(event_fname)
+
+event_id = {"auditory/left": 1, "auditory/right": 2}
+tmin, tmax = -0.2, 0.5
+
+epochs = mne.Epochs(
+    raw,
+    events,
+    event_id=event_id,
+    tmin=tmin,
+    tmax=tmax,
+    baseline=(None, 0),
+    preload=True,
+)
+
+# %%
+# Extract data and run t-test in Python
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# We average over channels and time to get one value per epoch, then run
+# a 2-sample t-test comparing the two conditions.
+
+epochs_left = epochs["auditory/left"].get_data(picks="eeg").mean(axis=(1, 2))
+epochs_right = epochs["auditory/right"].get_data(picks="eeg").mean(axis=(1, 2))
+
+t_python, p_python = stats.ttest_ind(epochs_left, epochs_right)
+print(f"Python  →  t = {t_python:.4f},  p = {p_python:.4f}")
+
+# %%
+# Run the same t-test in R via rpy2
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# We pass the same NumPy arrays to R using :mod:`rpy2` and call R's
+# built-in ``t.test()``.
+
+with localconverter(default_converter + numpy2ri.converter):
+    r_left = ro.FloatVector(epochs_left)
+    r_right = ro.FloatVector(epochs_right)
+
+r_ttest = ro.r["t.test"]
+result = r_ttest(r_left, r_right, **{"var.equal": True})
+
+t_r = float(result.rx2("statistic")[0])
+p_r = float(result.rx2("p.value")[0])
+print(f"R       →  t = {t_r:.4f},  p = {p_r:.4f}")
+
+# %%
+# Compare results
+# ^^^^^^^^^^^^^^^^
+#
+# Both approaches give identical t and p values (up to floating point
+# precision), confirming that R and Python produce equivalent results.
+
+print(f"\nt difference: {abs(t_python - t_r):.2e}")
+print(f"p difference: {abs(p_python - p_r):.2e}")