diff --git a/README.md b/README.md
index 4429a0a..98efdb5 100644
--- a/README.md
+++ b/README.md
@@ -126,6 +126,30 @@ np.allclose(a, b)
 >>> 306 ms ± 5.09 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
 ```
 
+## NaN handling possibility
+
+While the previous `Batch*` classes exclude every sample containing at least one NaN from the computations, the `BatchNan*` classes adopt a more flexible approach to handling NaN values, similar to `np.nansum`, `np.nanmean`, etc. Consequently, the outputted statistics can be computed from various numbers of samples for each feature:
+
+```python
+import numpy as np
+from batchstats import BatchNanSum
+
+m, n = 1_000_000, 50
+nan_ratio = 0.05
+n_batches = 17
+
+data = np.random.randn(m, n)
+num_nans = int(m * n * nan_ratio)
+nan_indices = np.random.choice(range(m * n), num_nans, replace=False)
+data.ravel()[nan_indices] = np.nan
+
+batchsum = BatchNanSum()
+for batch_data in np.array_split(data, n_batches):
+    batchsum.update_batch(batch=batch_data)
+np.allclose(np.nansum(data, axis=0), batchsum())
+>>> True
+```
+
 ## Documentation
 
 The documentation is available [here](https://batchstats.readthedocs.io/en/latest/).
diff --git a/batchstats/__init__.py b/batchstats/__init__.py
index f4f2591..ca89757 100644
--- a/batchstats/__init__.py
+++ b/batchstats/__init__.py
@@ -4,4 +4,4 @@
 __all__ = ['BatchCov', 'BatchMax', 'BatchMean', 'BatchMin', 'BatchStat', 'BatchSum', 'BatchVar',
            'BatchNanMean', 'BatchNanStat', 'BatchNanSum']
 
-__version__ = '0.2'
+__version__ = '0.3'
diff --git a/batchstats/nanstats.py b/batchstats/nanstats.py
index 6e0ca6c..7ea864a 100644
--- a/batchstats/nanstats.py
+++ b/batchstats/nanstats.py
@@ -4,10 +4,31 @@
 
 
 class BatchNanStat:
+    """
+    Base class for calculating statistics over batches of data that can contain NaN values.
+
+    Attributes:
+        n_samples (numpy.ndarray): Total number of samples processed, accounting for NaN values.
+
+    """
+
     def __init__(self):
+        """
+        Initialize the BatchNanStat object.
+        """
         self.n_samples = None
 
     def _process_batch(self, batch):
+        """
+        Process the input batch, counting NaN values.
+
+        Args:
+            batch (numpy.ndarray): Input batch.
+
+        Returns:
+            numpy.ndarray: Processed batch.
+
+        """
         batch = np.atleast_2d(np.asarray(batch))
         axis = tuple(range(1, batch.ndim))
         if self.n_samples is None:
@@ -18,11 +39,29 @@ def _process_batch(self, batch):
 
 
 class BatchNanSum(BatchNanStat):
+    """
+    Class for calculating the sum of batches of data that can contain NaN values.
+
+    """
+
     def __init__(self):
+        """
+        Initialize the BatchNanSum object.
+        """
         super().__init__()
         self.sum = None
 
     def update_batch(self, batch):
+        """
+        Update the sum with a new batch of data that can contain NaN values.
+
+        Args:
+            batch (numpy.ndarray): Input batch.
+
+        Returns:
+            BatchNanSum: Updated BatchNanSum object.
+
+        """
         batch = self._process_batch(batch)
         axis = tuple(range(1, batch.ndim))
         if self.sum is None:
@@ -32,6 +71,16 @@ def update_batch(self, batch):
         return self
 
     def __call__(self):
+        """
+        Calculate the sum of the batches that can contain NaN values.
+
+        Returns:
+            numpy.ndarray: Sum of the batches.
+
+        Raises:
+            NoValidSamplesError: If no valid samples are available.
+
+        """
         if self.sum is None:
             raise NoValidSamplesError()
         else:
@@ -39,13 +88,38 @@ def __call__(self):
 
 
 class BatchNanMean(BatchNanStat):
+    """
+    Class for calculating the mean of batches of data that can contain NaN values.
+
+    """
+
     def __init__(self):
+        """
+        Initialize the BatchNanMean object.
+        """
         super().__init__()
         self.sum = BatchNanSum()
 
     def update_batch(self, batch):
+        """
+        Update the mean with a new batch of data that can contain NaN values.
+
+        Args:
+            batch (numpy.ndarray): Input batch.
+
+        Returns:
+            BatchNanMean: Updated BatchNanMean object.
+
+        """
         self.sum.update_batch(batch)
         return self
 
     def __call__(self):
-        return self.sum()/self.sum.n_samples
+        """
+        Calculate the mean of the batches that can contain NaN values.
+
+        Returns:
+            numpy.ndarray: Mean of the batches.
+
+        """
+        return self.sum() / self.sum.n_samples
diff --git a/batchstats/stats.py b/batchstats/stats.py
index db1b131..601b43e 100644
--- a/batchstats/stats.py
+++ b/batchstats/stats.py
@@ -243,6 +243,8 @@ class BatchVar(BatchMean):
     """
     Class for calculating the variance of batches of data.
 
+    Args:
+        ddof (int, optional): Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. By default ddof is zero.
     """
 
     def __init__(self, ddof=0):
@@ -272,9 +274,9 @@ def init_var(cls, v, vm):
     def compute_incremental_variance(v, p, u):
         """
         Compute incremental variance.
-        For v 2D and p/u 1D, equivalent to ((v-p).T@(v-u)).sum(axis=0) or
-        np.einsum('ji,ji->i', v - p, v - u). faster and less memory consumer because
-        no intermediate 2d array are created.
+        For v 2D and p/u 1D, equivalent to ``((v-p).T@(v-u)).sum(axis=0)`` or
+        ``np.einsum('ji,ji->i', v - p, v - u)``. Faster and less memory consumer because
+        no intermediate 2D array are created.
 
         Args:
             v (numpy.ndarray): Input data.
@@ -341,6 +343,8 @@ class BatchCov(BatchStat):
     """
     Class for calculating the covariance of batches of data.
 
+    Args:
+        ddof (int, optional): Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. By default ddof is zero.
     """
 
     def __init__(self, ddof=0):
diff --git a/docs/source/api_reference.rst b/docs/source/api_reference.rst
index 45f0310..fc4f34a 100644
--- a/docs/source/api_reference.rst
+++ b/docs/source/api_reference.rst
@@ -10,7 +10,19 @@ The class ``BatchStat`` is the parent class from which other classes inherit. It
 The following classes inherit from ``BatchStat``, and enable the user to compute various statistics over batch-accessed data:
 
 .. automodule:: batchstats
-    :members:
+    :members: BatchCov, BatchMax, BatchMean, BatchMin, BatchSum, BatchVar
     :undoc-members:
     :show-inheritance:
-    :exclude-members: BatchStat
\ No newline at end of file
+
+
+The class ``BatchNanStat`` is the parent class from which other classes that can treat NaNs inherit. It allows for the factorization of the ``_process_batch`` method, which keeps track of the number of NaNs per feature.
+
+.. autoclass:: batchstats.BatchNanStat
+
+The following classes inherit from ``BatchNanStat``:
+
+.. automodule:: batchstats
+    :members: BatchNanMean, BatchNanSum
+    :undoc-members:
+    :show-inheritance:
+    :no-index:
\ No newline at end of file
diff --git a/docs/source/conf.py b/docs/source/conf.py
index af3757e..c58bffe 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -27,7 +27,6 @@
 templates_path = ['_templates']
 exclude_patterns = []
 
-
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output