diff --git a/Statistics/Constants.hs b/Statistics/Constants.hs
--- a/Statistics/Constants.hs
+++ b/Statistics/Constants.hs
@@ -22,7 +22,7 @@
 
 -- | A very large number.
 m_huge :: Double
-m_huge = 1.797693e308
+m_huge = 1.7976931348623157e308
 {-# INLINE m_huge #-}
 
 -- | The largest 'Int' /x/ such that 2**(/x/-1) is approximately
diff --git a/Statistics/Function.hs b/Statistics/Function.hs
--- a/Statistics/Function.hs
+++ b/Statistics/Function.hs
@@ -15,10 +15,13 @@
       minMax
     , sort
     , partialSort
+    , createU
     ) where
 
+import Control.Exception (assert)
+import Control.Monad.ST (unsafeSTToIO)
 import Data.Array.Vector.Algorithms.Combinators (apply)
-import Data.Array.Vector ((:*:)(..), UA, UArr, foldlU)
+import Data.Array.Vector
 import qualified Data.Array.Vector.Algorithms.Intro as I
 
 -- | Sort an array.
@@ -44,3 +47,14 @@
     go (MM lo hi) k = MM (min lo k) (max hi k)
     fini (MM lo hi) = lo :*: hi
 {-# INLINE minMax #-}
+
+-- | Create an array, using the given action to populate each element.
+createU :: (UA e) => Int -> (Int -> IO e) -> IO (UArr e)
+createU size itemAt = assert (size >= 0) $
+    unsafeSTToIO (newMU size) >>= loop 0
+  where
+    loop k arr | k >= size = unsafeSTToIO (unsafeFreezeAllMU arr)
+               | otherwise = do
+      r <- itemAt k
+      unsafeSTToIO (writeMU arr k r)
+      loop (k+1) arr
diff --git a/Statistics/Resampling.hs b/Statistics/Resampling.hs
--- a/Statistics/Resampling.hs
+++ b/Statistics/Resampling.hs
@@ -16,11 +16,11 @@
     , resample
     ) where
 
-import Control.Exception (assert)
 import Control.Monad (forM_)
 import Control.Monad.ST (unsafeSTToIO)
 import Data.Array.Vector
 import Data.Array.Vector.Algorithms.Intro (sort)
+import Statistics.Function (createU)
 import Statistics.Types (Estimator, Sample)
 import System.Random.Mersenne (MTGen, random)
 
@@ -50,17 +50,6 @@
         writeMU arr k . est $ re
     loop (k+1) ers
   n = lengthU samples
-
--- | Create an array, using the given action to populate each element.
-createU :: (UA e) => Int -> (Int -> IO e) -> IO (UArr e)
-createU size itemAt = assert (size >= 0) $
-    unsafeSTToIO (newMU size) >>= loop 0
-  where
-    loop k arr | k >= size = unsafeSTToIO (unsafeFreezeAllMU arr)
-               | otherwise = do
-      r <- itemAt k
-      unsafeSTToIO (writeMU arr k r)
-      loop (k+1) arr
 
 -- | Compute a statistical estimate repeatedly over a sample, each
 -- time omitting a successive element.
diff --git a/Statistics/Sample.hs b/Statistics/Sample.hs
--- a/Statistics/Sample.hs
+++ b/Statistics/Sample.hs
@@ -38,7 +38,7 @@
     -- $references
     ) where
 
-import Data.Array.Vector (foldlU)
+import Data.Array.Vector (foldlU, lengthU)
 import Statistics.Types (Sample)
 
 -- | Arithmetic mean.  This uses Welford's algorithm to provide
@@ -83,16 +83,16 @@
 -- Because of the need for two passes, these functions are /not/
 -- subject to stream fusion.
 
+data V = V {-# UNPACK #-} !Double {-# UNPACK #-} !Double
+
 robustVar :: Sample -> T
-robustVar samp = fini . foldlU go (T1 0 0 0) $ samp
+robustVar samp = fini . foldlU go (V 0 0) $ samp
   where
-    go (T1 n s c) x = T1 n' s' c'
-      where n' = n + 1
-            s' = s + d * d
-            c' = c + d
-            d  = x - m
-    fini (T1 n s c) = T (s - c ** (2 / fromIntegral n)) n
-    m = mean samp
+    go (V s c) x = V (s + d * d) (c + d)
+        where d  = x - m
+    fini (V s c) = T (s - (c * c) / fromIntegral n) n
+    n            = lengthU samp
+    m            = mean samp
 
 -- | Maximum likelihood estimate of a sample's variance.
 variance :: Sample -> Double
diff --git a/Statistics/Types.hs b/Statistics/Types.hs
--- a/Statistics/Types.hs
+++ b/Statistics/Types.hs
@@ -11,8 +11,8 @@
 
 module Statistics.Types
     (
-      Sample
-    , Estimator
+      Estimator
+    , Sample
     , Weights
     ) where
 
diff --git a/statistics.cabal b/statistics.cabal
--- a/statistics.cabal
+++ b/statistics.cabal
@@ -1,5 +1,5 @@
 name:           statistics
-version:        0.2
+version:        0.2.1
 synopsis:       A library of statistical types, data, and functions
 description:
   This library provides a number of common functions and types useful