diff --git a/docs/source/api/docstrings/pydeseq2.dds.DeseqDataSet.rst b/docs/source/api/docstrings/pydeseq2.dds.DeseqDataSet.rst
index f167f7f4..c2df993c 100644
--- a/docs/source/api/docstrings/pydeseq2.dds.DeseqDataSet.rst
+++ b/docs/source/api/docstrings/pydeseq2.dds.DeseqDataSet.rst
@@ -10,6 +10,7 @@
     .. autosummary::
    
         ~DeseqDataSet.calculate_cooks
+        ~DeseqDataSet.cond
         ~DeseqDataSet.deseq2
         ~DeseqDataSet.fit_LFC
         ~DeseqDataSet.fit_MAP_dispersions
diff --git a/pydeseq2/dds.py b/pydeseq2/dds.py
index 4c16ef89..525b6b00 100644
--- a/pydeseq2/dds.py
+++ b/pydeseq2/dds.py
@@ -531,6 +531,35 @@ def deseq2(self, fit_type: Optional[Literal["parametric", "mean"]] = None) -> No
         # Compute gene mask for cooks outliers
         self.cooks_outlier()
 
+    def cond(self, **kwargs):
+        """
+        Get a contrast vector representing a specific condition.
+
+        Parameters
+        ----------
+        **kwargs
+            Column/value pairs.
+
+        Returns
+        -------
+        ndarray
+            A contrast vector that aligns to the columns of the design matrix.
+        """
+        cond_dict = kwargs
+        if not set(cond_dict.keys()).issubset(self.variables):
+            raise ValueError(
+                """You specified a variable that is not part of the model. Available
+                variables: """
+                + ",".join(self.variables)
+            )
+        for var in self.variables:
+            if var in cond_dict:
+                self._check_category(var, cond_dict[var])
+            else:
+                cond_dict[var] = self._get_default_value(var)
+        df = pd.DataFrame([kwargs])
+        return self.obsm["design_matrix"].model_spec.get_model_matrix(df).iloc[0]
+
     def fit_size_factors(
         self,
         fit_type: Literal["ratio", "poscounts", "iterative"] = "ratio",
diff --git a/pydeseq2/ds.py b/pydeseq2/ds.py
index fb527ebf..5a379b43 100644
--- a/pydeseq2/ds.py
+++ b/pydeseq2/ds.py
@@ -614,33 +614,6 @@ def _contrast(self, column: str, baseline: str, group_to_compare: str) -> np.nda
         np.ndarray
             The contrast vector.
         """
-        return self.cond(**{column: baseline}) - self.cond(**{column: group_to_compare})
-
-    def cond(self, **kwargs):
-        """
-        Get a contrast vector representing a specific condition.
-
-        Parameters
-        ----------
-        **kwargs
-            Column/value pairs.
-
-        Returns
-        -------
-        ndarray
-            A contrast vector that aligns to the columns of the design matrix.
-        """
-        cond_dict = kwargs
-        if not set(cond_dict.keys()).issubset(self.dds.variables):
-            raise ValueError(
-                """You specified a variable that is not part of the model. Available
-                variables: """
-                + ",".join(self.dds.variables)
-            )
-        for var in self.dds.variables:
-            if var in cond_dict:
-                self.dds._check_category(var, cond_dict[var])
-            else:
-                cond_dict[var] = self.dds._get_default_value(var)
-        df = pd.DataFrame([kwargs])
-        return self.dds.obsm["design_matrix"].model_spec.get_model_matrix(df).iloc[0]
+        return self.dds.cond(**{column: baseline}) - self.dds.cond(
+            **{column: group_to_compare}
+        )