diff --git a/CHANGES b/CHANGES
--- a/CHANGES
+++ b/CHANGES
@@ -45,4 +45,7 @@
 
 		error in decorrelation fixed
 
-0.2.0.9:
+0.2.1.1:
+		added PDF.hs
+		modified Information to take in PDF addition
+		added Surrogate.hs
diff --git a/hstatistics.cabal b/hstatistics.cabal
--- a/hstatistics.cabal
+++ b/hstatistics.cabal
@@ -1,5 +1,5 @@
 Name:               hstatistics
-Version:            0.2.0.8
+Version:            0.2.1.1
 License:            GPL
 License-file:       LICENSE
 Copyright:          (c) A.V.H. McPhail 2010
@@ -31,8 +31,10 @@
     Exposed-modules:    Numeric.Statistics
                         Numeric.Statistics.PCA
                         Numeric.Statistics.ICA
+                        Numeric.Statistics.PDF
                         Numeric.Statistics.Information
                         Numeric.Statistics.Histogram
+                        Numeric.Statistics.Surrogate
     other-modules:      
     C-sources:          
 
diff --git a/lib/Numeric/Statistics/Histogram.hs b/lib/Numeric/Statistics/Histogram.hs
--- a/lib/Numeric/Statistics/Histogram.hs
+++ b/lib/Numeric/Statistics/Histogram.hs
@@ -2,7 +2,7 @@
 -----------------------------------------------------------------------------
 -- |
 -- Module      :  Numeric.Statistics.Histogram
--- Copyright   :  (c) Alexander Vivian Hugh McPhail 2010
+-- Copyright   :  (c) A. V. H. McPhail 2010
 -- License     :  GPL-style
 --
 -- Maintainer  :  haskell.vivian.mcphail <at> gmail <dot> com
diff --git a/lib/Numeric/Statistics/ICA.hs b/lib/Numeric/Statistics/ICA.hs
--- a/lib/Numeric/Statistics/ICA.hs
+++ b/lib/Numeric/Statistics/ICA.hs
@@ -2,7 +2,7 @@
 -----------------------------------------------------------------------------
 -- |
 -- Module      :  Numeric.Statistics.ICA
--- Copyright   :  (c) Alexander Vivian Hugh McPhail 2010
+-- Copyright   :  (c) A. V. H. McPhail 2010
 -- License     :  GPL-style
 --
 -- Maintainer  :  haskell.vivian.mcphail <at> gmail <dot> com
@@ -13,11 +13,11 @@
 --
 --  implements the FastICA algorithm found in:
 --
---   http://www.google.com/url?sa=t&source=web&cd=2&ved=0CBgQFjAB&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.79.7003%26rep%3Drep1%26type%3Dpdf&ei=RQozTJb6L4_fcbCV6cMD&usg=AFQjCNGClLIB9MAvbrEj45SyUx9cYubLyA&sig2=hg5Wnfy3dLPkoIc1hqSfjg
+-- * Aapo Hyvärinen and Erkki Oja,
+--   Independent Component Analysis: Algorithms and Applications,
+--   /Neural Networks/, 13(4-5):411-430, 2000
 --
---   Aapo Hyvärinen and Erkki Oja
---   Independent Component Analysis: Algorithms and Applications
---   Neural Networks, 13(4-5):411-430, 2000
+--   <http://www.google.com/url?sa=t&source=web&cd=2&ved=0CBgQFjAB&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.79.7003%26rep%3Drep1%26type%3Dpdf&ei=RQozTJb6L4_fcbCV6cMD&usg=AFQjCNGClLIB9MAvbrEj45SyUx9cYubLyA&sig2=hg5Wnfy3dLPkoIc1hqSfjg>
 --
 -----------------------------------------------------------------------------
 
@@ -138,12 +138,12 @@
                       cov = fromArray2D $ I.listArray ix $ map (\(m,n) -> covariance (mapVector g' (ys!!(m-1))) (ys!!(n-1))) $ I.range ix
                   in w + (diag $ fromList ais) <> ((diag $ fromList bis) + cov) <> w  
 
-decorrelate :: Matrix Double -> Matrix Double
-decorrelate w = let w' = w / (scalar $ sqrt $ pnorm PNorm2 (w <> trans w))
-                in decorrelate' w w'
-    where decorrelate' m m' 
-              | converged 0.000001 m m' = m'
-              | otherwise               = decorrelate' m' ((scale 1.5 m') - (scale 0.5 (m' <> trans m' <> m')))
+decorrelate :: NormType -> Double -> Matrix Double -> Matrix Double
+decorrelate n t w = let w' = w / (scalar $ sqrt $ pnorm n (w <> trans w))
+                    in decorrelate' t w w'
+    where decorrelate' t' m m' 
+              | converged t' m m' = m'
+              | otherwise         = decorrelate' t' m' ((scale 1.5 m') - (scale 0.5 (m' <> trans m' <> m')))
 {- don't know how to do svd of non-square matrices
 decorrelate m = let (u,d,v) = svd m
                 in u <> (diag (d ** (-0.5))) <> trans v <> m
@@ -166,11 +166,12 @@
      -> [Matrix Double]             -- ^ input data in chunks
      -> Matrix Double               -- ^ ica transform (weight matrix)
 ica' _ _  _ _ _ []     = error "no sample data"
-ica' g g' n t w (x:xs) = let w' = normalise n $ decorrelate $ update g g' w x
+ica' g g' n t w (x:xs) = let w' = normalise n $ decorrelate n t $ update g g' w x
                              in if converged t w w' 
                                 then w'
                                 else ica' g g' n t w' (xs ++ [x])
 
+-- | perform an ICA transform
 ica :: Int                         -- ^ random seed
     -> (Double -> Double)          -- ^ transfer function (tanh,u exp(u^2/2), etc...)
     -> (Double -> Double)          -- ^ derivative of transfer function
@@ -197,6 +198,6 @@
             -> (I.Array Int (Vector Double),Matrix Double) -- ^ transformed data, ica transform
 icaDefaults r a = let c = I.rangeSize $ I.bounds a
                       s = (dim $ (a I.! 1)) `div` 16
-                  in ica r sigmoid sigmoid' PNorm2 0.0000001 (c-1) s a
+                  in ica r sigmoid sigmoid' PNorm1 0.0000001 (c-1) s a
 
 -----------------------------------------------------------------------------
diff --git a/lib/Numeric/Statistics/Information.hs b/lib/Numeric/Statistics/Information.hs
--- a/lib/Numeric/Statistics/Information.hs
+++ b/lib/Numeric/Statistics/Information.hs
@@ -2,7 +2,7 @@
 -----------------------------------------------------------------------------
 -- |
 -- Module      :  Numeric.Statistics.Information
--- Copyright   :  (c) Alexander Vivian Hugh McPhail 2010
+-- Copyright   :  (c) A. V. H. McPhail 2010
 -- License     :  GPL-style
 --
 -- Maintainer  :  haskell.vivian.mcphail <at> gmail <dot> com
@@ -23,8 +23,7 @@
 
 import Data.Packed.Vector
 
-import qualified Numeric.GSL.Histogram as H
-import qualified Numeric.GSL.Histogram2D as H2
+import Numeric.Statistics.PDF
 
 import Numeric.LinearAlgebra.Algorithms
 import Numeric.LinearAlgebra.Interface()
@@ -41,21 +40,21 @@
 -----------------------------------------------------------------------------
 
 -- | the entropy \sum p_i l\ln{p_i} of a sequence
-entropy :: H.Histogram             -- ^ the underlying distribution
-        -> Vector Double           -- ^ the sequence (expected to fall within bounds of Histogram)
+entropy :: PDF a Double => a       -- ^ the underlying distribution
+        -> Vector Double           -- ^ the sequence
         -> Double                  -- ^ the entropy
-entropy p x = let ps = H.prob p x
+entropy p x = let ps = probability p x
               in negate $ dot ps (logE ps)
 
--- | the mutual information \sum_x \sum_y \ln{\frac{p(x,y)}{p(x)p(y)}}
-mutual_information :: H2.Histogram2D -- ^ the underlying distribution
-                   -> H.Histogram    -- ^ the first dimension distribution
-                   -> H.Histogram    -- ^ the second dimension distribution
-                   -> (Vector Double, Vector Double) -- ^ the sequence (expected to fall within bounds of Histogram)
+-- | the mutual information \sum_x \sum_y p(x,y) \ln{\frac{p(x,y)}{p(x)p(y)}}
+mutual_information :: (PDF a Double, PDF b (Double,Double)) => b -- ^ the underlying distribution
+                   -> a                                          -- ^ the first dimension distribution
+                   -> a                                          -- ^ the second dimension distribution
+                   -> (Vector Double, Vector Double)             -- ^ the sequence
                    -> Double         -- ^ the mutual information
-mutual_information p px py z@(x,y) = let ps = H2.prob p z
-                                         xs = H.prob px x
-                                         ys = H.prob py y
-                               in negate $ dot ps (logE ps - logE (xs*ys)) 
+mutual_information p px py (x,y) = let ps = probability p $ zipVector (,) x y
+                                       xs = probability px x
+                                       ys = probability py y
+                                   in negate $ dot ps (logE ps - logE (xs*ys)) 
 
 -----------------------------------------------------------------------------
diff --git a/lib/Numeric/Statistics/PCA.hs b/lib/Numeric/Statistics/PCA.hs
--- a/lib/Numeric/Statistics/PCA.hs
+++ b/lib/Numeric/Statistics/PCA.hs
@@ -2,7 +2,7 @@
 -----------------------------------------------------------------------------
 -- |
 -- Module      :  Numeric.Statistics.PCA
--- Copyright   :  (c) Alexander Vivian Hugh McPhail 2010
+-- Copyright   :  (c) A. V. H. McPhail 2010
 -- License     :  GPL-style
 --
 -- Maintainer  :  haskell.vivian.mcphail <at> gmail <dot> com
diff --git a/lib/Numeric/Statistics/PDF.hs b/lib/Numeric/Statistics/PDF.hs
new file mode 100644
--- /dev/null
+++ b/lib/Numeric/Statistics/PDF.hs
@@ -0,0 +1,61 @@
+{-# OPTIONS_GHC -fglasgow-exts #-}
+-----------------------------------------------------------------------------
+-- |
+-- Module      :  Numeric.Statistics.PDF
+-- Copyright   :  (c) A. V. H. McPhail 2010
+-- License     :  GPL-style
+--
+-- Maintainer  :  haskell.vivian.mcphail <at> gmail <dot> com
+-- Stability   :  provisional
+-- Portability :  portable
+--
+-- Probability Density Function interface
+--
+-----------------------------------------------------------------------------
+
+module Numeric.Statistics.PDF (
+                               -- * PDF
+                               PDFFunction,
+                               PDF(..),
+                               -- * Creation
+                               pdfFromFunction
+                          ) where
+
+
+-----------------------------------------------------------------------------
+
+import qualified Data.Packed.Vector as V
+
+import qualified Numeric.GSL.Histogram as H
+import qualified Numeric.GSL.Histogram2D as H2
+
+import Foreign.Storable
+--import Numeric.Statistics
+
+-----------------------------------------------------------------------------
+
+-- ^ a probability density function
+data PDFFunction a = P_Func (a -> Double)     -- p(x)
+
+-----------------------------------------------------------------------------
+
+-- ^ a PDF interface
+class PDF b a where
+    -- ^ calculate a probability
+    probability :: b -> V.Vector a -> V.Vector Double
+
+instance Storable b => PDF (PDFFunction b) b where
+    probability (P_Func f) = V.mapVector f
+
+instance PDF H.Histogram Double where
+    probability = H.prob
+
+instance PDF H2.Histogram2D (Double,Double) where
+    probability = H2.probPaired
+
+-----------------------------------------------------------------------------
+
+-- | create a PDF from an arbtrary function f :-> [0,1]
+pdfFromFunction :: (a -> Double) -> PDFFunction a
+pdfFromFunction = P_Func
+
diff --git a/lib/Numeric/Statistics/Surrogate.hs b/lib/Numeric/Statistics/Surrogate.hs
new file mode 100644
--- /dev/null
+++ b/lib/Numeric/Statistics/Surrogate.hs
@@ -0,0 +1,60 @@
+{-# OPTIONS_GHC -fglasgow-exts #-}
+-----------------------------------------------------------------------------
+-- |
+-- Module      :  Numeric.Statistics.Surrogate
+-- Copyright   :  (c) Alexander Vivian Hugh McPhail 2010
+-- License     :  GPL-style
+--
+-- Maintainer  :  haskell.vivian.mcphail <at> gmail <dot> com
+-- Stability   :  provisional
+-- Portability :  portable
+--
+-- Methods for tests using surrogate data
+--
+-----------------------------------------------------------------------------
+
+module Numeric.Statistics.Surrogate (
+                                     surrogate
+                                    ) where
+
+
+-----------------------------------------------------------------------------
+
+import Data.Packed.Vector
+--import Data.Packed.Matrix
+
+import qualified Data.Array.IArray as I 
+
+import Numeric.GSL.Permutation
+
+import System.Random
+
+-----------------------------------------------------------------------------
+
+-- | perform an analysis using surrogate data
+surrogate :: Int                          -- ^ random seed
+          -> Int                          -- ^ number of repetitions
+          -> (I.Array Int (Vector Double) -> a) -- ^ the evaluation function
+          -> I.Array Int (Vector Double)  -- ^ the data
+          -> I.Array Int a                -- ^ the results, with the evaluated real data in position 1 and the rest of the array containing the evaluated surrogate data
+                                                                                                              
+surrogate r n f d = I.listArray (1,n+1) $ (f d) : (surrogate' (mkStdGen r) n f d)
+
+surrogate' :: StdGen -> Int-> (I.Array Int (Vector Double) -> a) -> I.Array Int (Vector Double) -> [a]
+surrogate' _ 0     _ _ = []
+surrogate' g (n+1) f d = let (g',g'') = split g
+                             d' = permute_data g' d
+                         in (f d) : (surrogate' g'' n f d)
+
+randomList :: StdGen -> Int -> [Int]
+randomList _ 0     = []
+randomList g (n+1) = let (r,g') = random g
+                     in r : (randomList g' n)
+
+permute_data :: StdGen -> I.Array Int (Vector Double) -> I.Array Int (Vector Double)
+permute_data g d = let s = I.rangeSize $ I.bounds d
+                       rs = randomList g s
+                       ds = zip rs $ I.elems d
+                   in I.listArray (I.bounds d) $ map (\(r,v) -> permute (random_permute r (dim v)) v) ds
+ 
+-----------------------------------------------------------------------------