diff --git a/modularity.cabal b/modularity.cabal
--- a/modularity.cabal
+++ b/modularity.cabal
@@ -1,6 +1,6 @@
 cabal-version: >=1.10
 name: modularity
-version: 0.2.1.0
+version: 0.2.1.1
 license: GPL-3
 license-file: LICENSE
 copyright: 2019 Gregory W. Schwartz
@@ -22,14 +22,12 @@
         Math.Modularity.Dense
         Math.Modularity.Sparse
         Math.Modularity.Types
-        Math.Modularity.Eigen.Sparse
     hs-source-dirs: src
     default-language: Haskell2010
     ghc-options: -O2
     build-depends:
         base >=4.7 && <5,
-        eigen ==3.3.4.1,
         hmatrix >=0.19.0.0,
         sparse-linear-algebra >=0.3.1,
-        spectral-clustering >=0.2.2.0,
+        spectral-clustering >=0.3.1.1,
         vector >=0.12.0.1
diff --git a/src/Math/Modularity/Eigen/Sparse.hs b/src/Math/Modularity/Eigen/Sparse.hs
deleted file mode 100644
--- a/src/Math/Modularity/Eigen/Sparse.hs
+++ /dev/null
@@ -1,80 +0,0 @@
-{- Math.Sparse.Modularity.Eigen.FeatureMatrix
-Gregory W. Schwartz
-
-Collects the functions pertaining to finding the Newman-Girvan modularity of a
-sparse adjacency matrix.
--}
-
-{-# LANGUAGE BangPatterns #-}
-{-# LANGUAGE TupleSections #-}
-
-module Math.Modularity.Eigen.Sparse
-    ( getModularity
-    , getBModularity
-    , Q (..)
-    , testModularity
-    ) where
-
--- Remote
-import Data.Bool (bool)
-import Math.Clustering.Spectral.Eigen.FeatureMatrix (B (..), getB)
-import qualified Data.Eigen.SparseMatrix as S
-import qualified Data.Vector.Storable as VS
-
--- Local
-import Math.Modularity.Types
-
-type LabelVector     = S.SparseMatrixXd
-type AdjacencyMatrix = S.SparseMatrixXd
-
--- | Find modularity from a vector of community labels (0 or 1) corresponding to
--- rows in the adjacency matrix. Needs 0s on the diagonal for the adjacency
--- matrix.
-getModularity :: LabelVector -> AdjacencyMatrix -> Q
-getModularity moduleVec mat = Q $ (1 / (2 * m)) * sumQ mat
-  where
-    sumQ :: S.SparseMatrixXd -> Double
-    sumQ = S.getSum
-         . S._imap (\ i j v -> inner i j v * delta i j)
-    inner v w x = x - ((k v * k w) / (2 * m))
-    delta v w = ((s v * s w) + 1) / 2
-    m = (/ 2) . S.getSum $ mat -- Symmetric matrix so divide by 2.
-    d = S.getColSums mat
-    s = bool (-1) 1 . (== 0) . (S.!) moduleVec . (,0)
-    k = (S.!) d . (0,)
-
--- | Find modularity from a vector of community labels (0 or 1) corresponding to
--- rows in the normalized matrix B. See Shu et al., "Efficient Spectral
--- Neighborhood Blocking for Entity Resolution", 2011.
--- L = sum_i^n sum_j^n A(i,j) - n = 1^TA1 - n = (B^T1)^T(B^T1) - n.
-getBModularity :: LabelVector -> B -> Q
-getBModularity moduleVec (B b) = Q . sum . fmap inner $ [first, second]
-  where
-    inner v = (a v v / l) - ((a v (S.ones n) / l) ** 2)
-    first  = moduleVec
-    second = S.fromDenseList
-           . (fmap . fmap) (bool 1 0 . (== 1))
-           . S.toDenseList
-           $ moduleVec
-    l    = a (S.ones n) (S.ones n)
-    a :: S.SparseMatrixXd -> S.SparseMatrixXd -> Double
-    a oneL oneR = ( flip (S.!) (0, 0)
-                  $ (S.transpose (partA oneL)) * (partA oneR)
-                  )
-                - (S.getSum oneL)
-    partA one = (S.transpose b) * one
-    n    = S.rows b
-
--- | Set the diagonal of a sparse matrix to 0.
-setDiag0 :: S.SparseMatrixXd -> S.SparseMatrixXd
-setDiag0 = S._imap (\x y z -> if x == y then 0 else z)
-
--- | Test whether getModularity BB^T is the same as getBModularity B.
-testModularity :: (Bool, Q, Q)
-testModularity = (modA == modB, modA, modB)
-  where
-    items = S.fromDenseList (fmap (:[]) [1,1,0,0] :: [[Double]])
-    b     = getB True $ S.fromDenseList ([[1,1],[0,0],[0,0],[1,1]] :: [[Double]])
-    a     = setDiag0 $ (unB b) * S.transpose (unB b)
-    modA  = getModularity items a
-    modB  = getBModularity items b