diff --git a/src/arviz_stats/__init__.py b/src/arviz_stats/__init__.py
index dfb743f..4a9503e 100644
--- a/src/arviz_stats/__init__.py
+++ b/src/arviz_stats/__init__.py
@@ -5,6 +5,7 @@
     from arviz_stats.utils import *
     from arviz_stats.accessors import *
     from arviz_stats.psense import psense, psense_summary
+    from sampling_diagnostics import ess
 
 except ModuleNotFoundError:
     pass
diff --git a/src/sampling_diagnostics.py b/src/sampling_diagnostics.py
new file mode 100644
index 0000000..5e2047a
--- /dev/null
+++ b/src/sampling_diagnostics.py
@@ -0,0 +1,85 @@
+"""Functions for sampling diagnostics."""
+
+from arviz_base import convert_to_dataset
+from numpy import atleast_2d, ndarray
+
+from arviz_stats.base.array import BaseArray
+
+base_array = BaseArray()
+
+
+def ess(data, var_names=None, method="bulk", relative=False, prob=None):
+    r"""Estimate the effective sample size (ess).
+
+    Parameters
+    ----------
+    data : obj
+        Array-like or any object that can be converted to a :class:`xarray.DataSet` object.
+        Refer to documentation of :func:`arviz.convert_to_dataset` for details.
+        For ndarray: shape = (chain, draw).
+    var_names : str or list of str
+        Names of the variables for which the ess should be computed.
+    method : str, optional
+        Defaults to "bulk". Valid methods are:
+
+        - "bulk"
+        - "tail"     # prob, optional
+        - "quantile" # prob
+        - "mean" (old ess)
+        - "sd"
+        - "median"
+        - "mad" (mean absolute deviance)
+        - "z_scale"
+        - "folded"
+        - "identity"
+        - "local"  # prob
+    relative : bool
+        Return relative ess
+        ``ress = ess / n``
+    prob : float, or tuple of two floats, optional
+        probability value for "tail", "quantile" or "local" ess functions.
+
+    Returns
+    -------
+    xarray.Dataset or numpy array with the effective sample size values
+
+    See Also
+    --------
+    arviz.rhat : Compute estimate of rank normalized split R-hat for a set of traces.
+    arviz.mcse : Calculate Markov Chain Standard Error statistic.
+    plot_ess : Plot quantile, local or evolution of effective sample sizes (ESS).
+    arviz.summary : Create a data frame with summary statistics.
+
+    Examples
+    --------
+    Calculate the effective_sample_size using the default arguments:
+
+    .. ipython::
+
+        In [1]: from arviz_base import load_arviz_data
+           ...: import arviz_stats as azs
+           ...: data = load_arviz_data('non_centered_eight')
+           ...: azs.ess(data)
+
+    Calculate ess for a subset of the variables
+
+    .. ipython::
+
+        In [1]: azs.ess(data, relative=True, var_names=["mu", "theta_t"])
+
+    Calculate ess using the "tail" method, leaving the `prob` at its default value.
+
+    .. ipython::
+
+        In [1]: azs.ess(data, method="tail")
+    """
+    if isinstance(data, (list, tuple, ndarray)):
+        data = atleast_2d(data)
+        return base_array.ess(data, method=method, relative=relative, prob=prob)
+
+    data = convert_to_dataset(data)
+
+    if var_names is not None:
+        return data.azstats.filter_vars(var_names=var_names).ess(method=method, relative=relative, prob=prob)
+
+    return data.azstats.ess(method=method, relative=relative, prob=prob)
\ No newline at end of file