diff --git a/hidimstat/adaptive_permutation_threshold.py b/hidimstat/adaptive_permutation_threshold.py
index 66e525d..d6dabe5 100644
--- a/hidimstat/adaptive_permutation_threshold.py
+++ b/hidimstat/adaptive_permutation_threshold.py
@@ -49,23 +49,24 @@ def ada_svr(X, y, rcond=1e-3):
     return beta_hat, scale
 
 
-def _manual_inversion(X, rcond=1e-3, full_rank=False):
-    "Inverting taking care of low eigenvalues to increase numerical stability"
+def _manual_inversion(X, rcond=1e-3):
+    """
+    Inverting taking care of low eigenvalues to increase numerical stability
+    
+    Parameters
+    -----------
+    X : ndarray, shape (n_features, n_features) 
+        Data.
+        Base on usage of X in the main function, X is squared matrix and not full_ranked (for details see PR#58)
+        
+    """
 
     X = np.asarray(X)
-    n_samples, n_features = X.shape
-
-    if n_samples != n_features:
-        raise ValueError("The matrix is not a square matrix")
-
     U, s, V = np.linalg.svd(X, full_matrices=False)
     rank = np.sum(s > rcond * s.max())
     s_inv = np.zeros(np.size(s))
     s_inv[:rank] = 1 / s[:rank]
 
-    if full_rank:
-        s_inv[rank:] = 1 / (rcond * s.max())
-
     X_inv = np.linalg.multi_dot([U, np.diag(s_inv), V])
 
     return X_inv