diff --git a/README b/README
deleted file mode 100644
--- a/README
+++ /dev/null
@@ -1,47 +0,0 @@
-Statistics: efficient, general purpose statistics
--------------------------------------------------
-
-This package provides the Statistics module, a Haskell library for
-working with statistical data in a space- and time-efficient way.
-
-Where possible, we give citations and computational complexity
-estimates for the algorithms used.
-
-
-Performance
------------
-
-This library has been carefully optimised for high performance.  To
-obtain the best runtime efficiency, it is imperative to compile
-libraries and applications that use this library using a high level of
-optimisation.
-
-Suggested GHC options:
-
-  -O -fvia-C -funbox-strict-fields
-
-To illustrate, here are the times (in seconds) to generate and sum 250
-million random Word32 values, on a laptop with a 2.4GHz Core2 Duo
-P8600 processor, running Fedora 11 and GHC 6.10.3:
-
-  no flags   200+
-  -O           1.249
-  -O -fvia-C   0.991
-
-As the numbers above suggest, compiling without optimisation will
-yield unacceptable performance.
-
-
-Get involved!
--------------
-
-Please feel welcome to contribute new code or bug fixes.  You can
-fetch the source repository from here:
-
-http://bitbucket.org/bos/statistics
-
-
-Authors
--------
-
-Bryan O'Sullivan <bos@serpentine.com>
diff --git a/README.markdown b/README.markdown
new file mode 100644
--- /dev/null
+++ b/README.markdown
@@ -0,0 +1,52 @@
+# Statistics: efficient, general purpose statistics
+
+This package provides the Statistics module, a Haskell library for
+working with statistical data in a space- and time-efficient way.
+
+Where possible, we give citations and computational complexity
+estimates for the algorithms used.
+
+
+# Performance
+
+This library has been carefully optimised for high performance.  To
+obtain the best runtime efficiency, it is imperative to compile
+libraries and applications that use this library using a high level of
+optimisation.
+
+Suggested GHC options:
+
+    -O -funbox-strict-fields
+
+To illustrate, here are the times (in seconds) to generate and sum 250
+million random Word32 values, on a laptop with a 2.4GHz Core2 Duo
+P8600 processor, running Fedora 11 and GHC 6.10.3:
+
+    no flags   200+
+    -O           1.249
+    -O -fvia-C   0.991
+
+As the numbers above suggest, compiling without optimisation will
+yield unacceptable performance.
+
+
+# Get involved!
+
+Please report bugs via the
+[bitbucket issue tracker](http://bitbucket.org/bos/attoparsec/statistics).
+
+Master [Mercurial repository](http://bitbucket.org/bos/statistics):
+
+* `hg clone http://bitbucket.org/bos/statistics`
+
+There's also a [git mirror](http://github.com/bos/statistics):
+
+* `git clone git://github.com/bos/statistics.git`
+
+(You can create and contribute changes using either Mercurial or git.)
+
+
+# Authors
+
+This library is written and maintained by Bryan O'Sullivan,
+<bos@serpentine.com>.
diff --git a/Statistics/Distribution/Beta.hs b/Statistics/Distribution/Beta.hs
deleted file mode 100644
--- a/Statistics/Distribution/Beta.hs
+++ /dev/null
@@ -1,51 +0,0 @@
-{-# LANGUAGE DeriveDataTypeable #-}
--- |
--- Module    : Statistics.Distribution.Beta
--- Copyright : (c) 2010 Karamaan Group
---
--- The beta distribution.
-
-module Statistics.Distribution.Beta
-    (
-      BetaDistribution
-    -- * Constructors
-    , fromParams
-    -- * Accessors
-    , tridistA
-    , tridistB
-    , tridistC
-    ) where
-
-import Control.Exception
-import Data.Generics
-import Foreign.C.Math.Double (gamma)
-import qualified Statistics.Distribution as D
-
-data BetaDistribution = BetaDist {
-  alpha :: Double,
-  beta :: Double
-} deriving (Eq, Read, Show, Typeable, Data)
-
-instance D.Distribution BetaDistribution where
-  density (BetaDist a b) x = (gamma (a+b) / (gamma a * gamma b)) *
-    (x**(a-1)) * ((1-x)**(b-1))
-  {-# INLINE density #-}
-  cumulative (BetaDist a b) x = undefined
-  {-# INLINE cumulative #-}
-  quantile (BetaDist a b) p = undefined
-  {-# INLINE quantile #-}
-
-instance D.Variance BetaDistribution where
-    variance (BetaDist a b) = (a * b) /
-      ((a+b)^2 * (a + b + 1))
-    {-# INLINE variance #-}
-
-instance D.Mean BetaDistribution where
-    mean (BetaDist a b) = a / (a + b)
-    {-# INLINE mean #-}
-
-fromParams :: Double -> Double -> BetaDistribution
-fromParams a b = assert (a > 0 && b > 0) (BetaDist a b)
-{-# INLINE fromParams #-}
-
-
diff --git a/Statistics/Distribution/LogNormal.hs b/Statistics/Distribution/LogNormal.hs
deleted file mode 100644
--- a/Statistics/Distribution/LogNormal.hs
+++ /dev/null
@@ -1,81 +0,0 @@
-{-# LANGUAGE DeriveDataTypeable #-}
--- |
--- Module    : Statistics.Distribution.LogNormal
--- Copyright : (c) 2009 Karamaan Group
---
--- The lognormal distribution. This is the distribution of a random 
--- variable whose logarithm is normally distributed.
-
-module Statistics.Distribution.LogNormal
-    (
-      LogNormalDistribution
-    -- * Constructors
-    , fromParams
-    , standard
-    ) where
-
-import Control.Exception (assert)
-import Data.Number.Erf (erf)
-import Data.Generics
-import Statistics.Constants (m_sqrt_2, m_sqrt_2_pi)
-import qualified Statistics.Distribution as D
-
--- | The lognormal distribution.
-data LogNormalDistribution = ND {
-      mean     :: {-# UNPACK #-} !Double
-    , variance :: {-# UNPACK #-} !Double
-    , ndPdfDenom :: {-# UNPACK #-} !Double
-    , ndCdfDenom :: {-# UNPACK #-} !Double
-    } deriving (Eq, Read, Show, Typeable, Data)
-
-instance D.Distribution LogNormalDistribution where
-    density    = density
-    cumulative = cumulative
-    quantile   = quantile
-
-instance D.Variance LogNormalDistribution where
-    variance = variance
-
-instance D.Mean LogNormalDistribution where
-    mean = mean
-
-standard :: LogNormalDistribution
-standard = ND {
-             mean = 0.0
-           , variance = 1.0
-           , ndPdfDenom = m_sqrt_2_pi
-           , ndCdfDenom = m_sqrt_2
-           }
-
-fromParams :: Double -> Double -> LogNormalDistribution
-fromParams m v = assert (v > 0)
-                 ND {
-                   mean = m
-                 , variance = v
-                 , ndPdfDenom = m_sqrt_2_pi * sv
-                 , ndCdfDenom = m_sqrt_2 * sv
-                 }
-    where sv = sqrt v
-
-density :: LogNormalDistribution -> Double -> Double
-density d x = exp (-xm * xm / (2 * variance d)) / (x * ndPdfDenom d)
-    where xm = log x - mean d
-
-cumulative :: LogNormalDistribution -> Double -> Double
-cumulative d x = (1 + erf ((log x-mean d) / ndCdfDenom d)) / 2
-
--- | This is the quantile function for the LogNormalDistribution.
-quantile :: LogNormalDistribution -> Double -> Double
-quantile d p = exp $ quantile' d p
-
--- | This is the quantile function for NormalDistribution.
-quantile' :: LogNormalDistribution -> Double -> Double
-quantile' d p
-  | p < 0 || p > 1 = inf/inf
-  | p == 0         = -inf
-  | p == 1         = inf
-  | p == 0.5       = mean d
-  | otherwise      = x * sqrt (variance d) + mean d
-  where x          = D.findRoot standard p 0 (-100) 100
-        inf        = 1/0
-
diff --git a/Statistics/Distribution/Triangular.hs b/Statistics/Distribution/Triangular.hs
deleted file mode 100644
--- a/Statistics/Distribution/Triangular.hs
+++ /dev/null
@@ -1,68 +0,0 @@
-{-# LANGUAGE DeriveDataTypeable #-}
--- |
--- Module    : Statistics.Distribution.Triangular
--- Copyright : (c) 2010 Karamaan Group
---
--- The triangular distribution. This is the distribution of a random 
--- variable with lower limit a, mode c and upper limit b.
-
-module Statistics.Distribution.Triangular
-    (
-      TriangularDistribution
-    -- * Constructors
-    , fromParams
-    -- * Accessors
-    , tridistA
-    , tridistB
-    , tridistC
-    ) where
-
-import Data.Generics
-import qualified Statistics.Distribution as D
-
-data TriangularDistribution = TriDist {
-  tridistA :: Double, -- min
-  tridistB :: Double, -- max
-  tridistC :: Double  -- mode
-} deriving (Eq, Read, Show, Typeable, Data)
-
-instance D.Distribution TriangularDistribution where
-  density (TriDist a b c) x
-    | (a <= x) && (x <= c) = (2 * (x - a)) / ((b - a) * (c - a))
-    | (c <= x) && (x <= b) = (2 * (b - x)) / ((b - a) * (b - c))
-    | otherwise         = 0
-  {-# INLINE density #-}
-
-  cumulative (TriDist a b c) x
-    | a > x             = 0
-    | (a <= x) && (x <= c) = ((x - a) ^ 2) / ((b - a) * (c - a))
-    | (c <= x) && (x <= b) = 1 - ((b - x) ^ 2) / ((b - a) * (b - c))
-    | otherwise         = 1
-  {-# INLINE cumulative #-}
-
-  quantile (TriDist a b c) p   = calc ((c - a) / (b - a))
-    where calc p0
-            | p < p0    = sqrt ((b-a) * (c-a) * p) + a
-            | p == p0   = c
-            | otherwise = b - sqrt ((b-a) * (b-c) * (1-p))
-  {-# INLINE quantile #-}
-
-instance D.Variance TriangularDistribution where
-    variance (TriDist a b c) =
-      (a^2 + b^2 + c^2 - (a*b) - (a*c) - (b*c)) / 18
-    {-# INLINE variance #-}
-
-instance D.Mean TriangularDistribution where
-    mean (TriDist a b c) = (a + b + c) / 3
-    {-# INLINE mean #-}
-
-fromParams :: Double -> Double -> Double -> TriangularDistribution
-fromParams a b c 
-    | (c > b) || (c < a) = error $ "Triangular Distribution: Parameter " ++ (show c)
-                          ++ " is expected to be between the parameters " 
-                          ++ (show a) ++ " and " ++ (show b) ++ "."
-    | b < a             = error $ "Triangular Distribution: Parameter " ++ (show b)
-                          ++ " is expected to be greater than parameter " ++ (show a) ++ "."
-    | otherwise         = TriDist a b c
-{-# INLINE fromParams #-}
-
diff --git a/statistics.cabal b/statistics.cabal
--- a/statistics.cabal
+++ b/statistics.cabal
@@ -1,5 +1,5 @@
 name:           statistics
-version:        0.8.0.2
+version:        0.8.0.3
 synopsis:       A library of statistical types, data, and functions
 description:
   This library provides a number of common functions and types useful
@@ -30,24 +30,21 @@
 category:       Math, Statistics
 build-type:     Simple
 cabal-version:  >= 1.6
-extra-source-files: README
+extra-source-files: README.markdown
 
 library
   exposed-modules:
     Statistics.Autocorrelation
     Statistics.Constants
     Statistics.Distribution
-    Statistics.Distribution.Beta
     Statistics.Distribution.Binomial
     Statistics.Distribution.ChiSquared
-    Statistics.Distribution.Exponential
     Statistics.Distribution.Gamma
     Statistics.Distribution.Geometric
+    Statistics.Distribution.Exponential
     Statistics.Distribution.Hypergeometric
-    Statistics.Distribution.LogNormal
     Statistics.Distribution.Normal
     Statistics.Distribution.Poisson
-    Statistics.Distribution.Triangular
     Statistics.Function
     Statistics.KernelDensity
     Statistics.Math