diff --git a/sklearn/linear_model/cd_fast.c b/sklearn/linear_model/cd_fast.c
diff --git a/sklearn/linear_model/cd_fast.pyx b/sklearn/linear_model/cd_fast.pyx
@@ -37,6 +37,48 @@ cdef extern from "cblas.h":
 ctypedef np.float64_t DOUBLE
 ctypedef np.int32_t INTEGER
 
+@cython.boundscheck(False)
+@cython.wraparound(False)
+@cython.cdivision(True)
+def sparse_std(unsigned int n_samples,
+               unsigned int n_features,
+               np.ndarray[DOUBLE, ndim=1] X_data,
+               np.ndarray[INTEGER, ndim=1] X_indices,
+               np.ndarray[INTEGER, ndim=1] X_indptr,
+               np.ndarray[DOUBLE, ndim=1] X_mean=None):
+    cdef unsigned int ii
+    cdef unsigned int jj
+    cdef unsigned int nnz_ii
+    cdef double X_sum_ii
+    cdef double X_mean_ii
+    cdef double diff
+    cdef double X_std_ii
+
+    cdef np.ndarray[DOUBLE, ndim = 1] X_std = np.zeros(n_features, np.float64)
+
+    if X_mean is None:
+        X_mean = np.zeros(n_features, np.float64)
+
+        for ii in xrange(n_features):
+            # Computes the mean
+            X_sum_ii = 0.0
+            for jj in xrange(X_indptr[ii], X_indptr[ii + 1]):
+                X_sum_ii += X_data[jj]
+            X_mean[ii] = X_sum_ii / n_samples
+
+    for ii in xrange(n_features):
+        X_mean_ii = X_mean[ii]
+        X_sum_ii = 0.0
+        nnz_ii = 0
+        for jj in xrange(X_indptr[ii], X_indptr[ii + 1]):
+            diff = X_data[jj] - X_mean_ii
+            X_sum_ii += diff * diff
+            nnz_ii += 1
+
+        X_std[ii] = (X_sum_ii + (n_samples - nnz_ii) * X_mean_ii * X_mean_ii)
+    return np.sqrt(X_std)
+
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 @cython.cdivision(True)