diff --git a/CHANGELOG b/CHANGELOG
new file mode 100644
--- /dev/null
+++ b/CHANGELOG
@@ -0,0 +1,11 @@
+# Changelog
+
+	- 2.0.0 (2018-01-29)
+	* Add Laplace and Zipf-Mandelbrot distribution
+	* Rename `isoGauss` to `isoNormal` and `standard` to `standardNormal` to uniform naming scheme	
+	* Divide Haddock in sections
+	
+	- 1.3.0 (2016-12-04)
+	* Generalize a couple of samplers to use Traversable rather than lists.
+
+
diff --git a/README.md b/README.md
new file mode 100644
--- /dev/null
+++ b/README.md
@@ -0,0 +1,40 @@
+# mwc-probability
+
+[![Build Status](https://secure.travis-ci.org/jtobin/mwc-probability.png)](http://travis-ci.org/jtobin/mwc-probability)
+[![Hackage Version](https://img.shields.io/hackage/v/mwc-probability.svg)](http://hackage.haskell.org/package/mwc-probability)
+[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/jtobin/mwc-probability/blob/master/LICENSE)
+
+Sampling function-based probability distributions.
+
+A simple probability distribution type, where distributions are characterized
+by sampling functions.
+
+This implementation is a thin layer over `mwc-random`, which handles RNG
+state-passing automatically by using a `PrimMonad` like `IO` or `ST s` under
+the hood.
+
+Examples
+--------
+
+Transform a distribution's support while leaving its density structure
+invariant:
+
+    -- uniform over [0, 1] to uniform over [1, 2]
+    succ <$> uniform
+
+Sequence distributions together using bind:
+
+    -- a beta-binomial composite distribution
+    beta 1 10 >>= binomial 10
+
+Use do-notation to build complex joint distributions from composable,
+local conditionals:
+
+    hierarchicalModel = do
+      [c, d, e, f] <- replicateM 4 $ uniformR (1, 10)
+      a <- gamma c d
+      b <- gamma e f
+      p <- beta a b
+      n <- uniformR (5, 10)
+      binomial n p
+
diff --git a/mwc-probability.cabal b/mwc-probability.cabal
--- a/mwc-probability.cabal
+++ b/mwc-probability.cabal
@@ -1,13 +1,14 @@
 name:                mwc-probability
-version:             1.3.0
+version:             2.0.0
 homepage:            http://github.com/jtobin/mwc-probability
 license:             MIT
 license-file:        LICENSE
-author:              Jared Tobin
-maintainer:          jared@jtobin.ca
+author:              Jared Tobin, Marco Zocca
+maintainer:          jared@jtobin.ca, zocca.marco gmail
 category:            Math
 build-type:          Simple
 cabal-version:       >= 1.10
+tested-with:         GHC == 8.0.2, GHC == 8.2.2                     
 synopsis:            Sampling function-based probability distributions.
 description:
 
@@ -41,6 +42,8 @@
   >   p <- beta a b
   >   n <- uniformR (5, 10)
   >   binomial n p
+extra-source-files:  README.md
+                     CHANGELOG
 
 Source-repository head
   Type:     git
@@ -51,8 +54,8 @@
   default-language:    Haskell2010
   hs-source-dirs:      src
   build-depends:
-      base          >  4 && < 6
-    , mwc-random    >  0.13 && < 0.14
-    , primitive     >= 0.6 && < 1.0
-    , transformers  >= 0.5 && < 1.0
+      base          >=  4.8 && < 6
+    , mwc-random    >   0.13 && < 0.14
+    , primitive     >=  0.6 && < 1.0
+    , transformers  >=  0.5 && < 1.0
 
diff --git a/src/System/Random/MWC/Probability.hs b/src/System/Random/MWC/Probability.hs
--- a/src/System/Random/MWC/Probability.hs
+++ b/src/System/Random/MWC/Probability.hs
@@ -4,10 +4,10 @@
 
 -- |
 -- Module: System.Random.MWC.Probability
--- Copyright: (c) 2015 Jared Tobin
+-- Copyright: (c) 2015-2017 Jared Tobin, Marco Zocca
 -- License: MIT
 --
--- Maintainer: Jared Tobin <jared@jtobin.ca>
+-- Maintainer: Jared Tobin <jared@jtobin.ca>, Marco Zocca <zocca.marco gmail>
 -- Stability: unstable
 -- Portability: ghc
 --
@@ -42,34 +42,54 @@
 -- For example, @'uniform'@ is a distribution over the 0-1 interval, but @fmap
 -- (+ 1) uniform@ is the translated distribution over the 1-2 interval.
 --
--- >>> sample (fmap (+ 1) uniform) gen
+-- >>> create >>= sample (fmap (+ 1) uniform)
 -- 1.5480073474340754
+--
+-- == Running the examples
+--
+-- In the following we will assume an interactive GHCi session; the user should first declare a random number generator: 
+--
+-- >>> gen <- create
+--
+-- which will be reused throughout all examples.
+-- Note: creating a random generator is an expensive operation, so it should be only performed once in the code (usually in the top-level IO action, e.g `main`).
 
+
 module System.Random.MWC.Probability (
     module MWC
   , Prob(..)
   , samples
 
+  -- * Distributions
+  -- ** Continuous-valued
   , uniform
   , uniformR
-  , discreteUniform
-  , categorical
-  , standard
   , normal
+  , standardNormal
+  , isoNormal    
   , logNormal
   , exponential
+  , laplace
   , gamma
   , inverseGamma
+  , normalGamma
+  , weibull
   , chiSquare
   , beta
+  , student
+  -- *** Dirichlet process
   , dirichlet
-  , symmetricDirichlet
+  , symmetricDirichlet  
+  -- ** Discrete-valued
+  , discreteUniform
+  , zipf
+  , categorical
   , bernoulli
   , binomial
   , multinomial
-  , student
-  , isoGauss
-  , poisson
+  , poisson  
+
+
   ) where
 
 import Control.Applicative
@@ -89,7 +109,6 @@
 
 -- | A probability distribution characterized by a sampling function.
 --
--- >>> gen <- create
 -- >>> sample uniform gen
 -- 0.4208881170464097
 newtype Prob m a = Prob { sample :: Gen (PrimState m) -> m a }
@@ -102,13 +121,20 @@
 samples n model gen = replicateM n (sample model gen)
 {-# INLINABLE samples #-}
 
-instance Monad m => Functor (Prob m) where
-  fmap h (Prob f) = Prob $ fmap h . f
+instance Functor m => Functor (Prob m) where
+  fmap h (Prob f) = Prob (fmap h . f)
 
 instance Monad m => Applicative (Prob m) where
-  pure  = return
+  pure  = Prob . const . pure
   (<*>) = ap
 
+instance Monad m => Monad (Prob m) where
+  return = pure
+  m >>= h = Prob $ \g -> do
+    z <- sample m g
+    sample (h z) g
+  {-# INLINABLE (>>=) #-}
+
 instance (Monad m, Num a) => Num (Prob m a) where
   (+)         = liftA2 (+)
   (-)         = liftA2 (-)
@@ -117,13 +143,8 @@
   signum      = fmap signum
   fromInteger = pure . fromInteger
 
-instance Monad m => Monad (Prob m) where
-  return  = Prob . const . return
-  m >>= h = Prob $ \g -> do
-    z <- sample m g
-    sample (h z) g
-  {-# INLINABLE (>>=) #-}
 
+
 instance MonadTrans Prob where
   lift m = Prob $ const m
 
@@ -137,7 +158,6 @@
 
 -- | The uniform distribution over a type.
 --
---   >>> gen <- create
 --   >>> sample uniform gen :: IO Double
 --   0.29308497534914946
 --   >>> sample uniform gen :: IO Bool
@@ -168,9 +188,9 @@
 
 -- | The standard normal or Gaussian distribution (with mean 0 and standard
 --   deviation 1).
-standard :: PrimMonad m => Prob m Double
-standard = Prob MWC.Dist.standard
-{-# INLINABLE standard #-}
+standardNormal :: PrimMonad m => Prob m Double
+standardNormal = Prob MWC.Dist.standard
+{-# INLINABLE standardNormal #-}
 
 -- | The normal or Gaussian distribution with a specified mean and standard
 --   deviation.
@@ -188,6 +208,23 @@
 exponential r = Prob $ MWC.Dist.exponential r
 {-# INLINABLE exponential #-}
 
+-- | The Laplace distribution with provided location and scale parameters.
+laplace :: (Floating a, Variate a, PrimMonad m) => a -> a -> Prob m a
+laplace mu sigma = do
+  u <- uniformR (-0.5, 0.5)
+  let b = sigma / sqrt 2
+  return $ mu - b * signum u * log (1 - 2 * abs u)
+{-# INLINABLE laplace #-}  
+
+
+-- | The Weibull distribution with provided shape and scale parameters.
+weibull :: (Floating a, Variate a, PrimMonad m) => a -> a -> Prob m a
+weibull a b = do
+  x <- uniform
+  return $ (- 1/a * log (1 - x)) ** 1/b
+{-# INLINABLE weibull #-}
+
+
 -- | The gamma distribution with shape parameter a and scale parameter b.
 --
 --   This is the parameterization used more traditionally in frequentist
@@ -204,6 +241,14 @@
 inverseGamma a b = recip <$> gamma a b
 {-# INLINABLE inverseGamma #-}
 
+-- | The Normal-Gamma distribution of parameters mu, lambda, a, b
+normalGamma :: PrimMonad m => Double -> Double -> Double -> Double -> Prob m Double
+normalGamma mu lambda a b = do
+  tau <- gamma a b
+  let xsd = sqrt $ 1 / (lambda * tau)
+  normal mu xsd
+{-# INLINABLE normalGamma #-}
+
 -- | The chi-square distribution.
 chiSquare :: PrimMonad m => Int -> Prob m Double
 chiSquare k = Prob $ MWC.Dist.chiSquare k
@@ -257,11 +302,12 @@
   normal m sd
 {-# INLINABLE student #-}
 
--- | An isotropic or spherical Gaussian distribution.
-isoGauss
+-- | An isotropic or spherical Gaussian distribution with specified mean
+-- vector and scalar standard deviation parameter.
+isoNormal
   :: (Traversable f, PrimMonad m) => f Double -> Double -> Prob m (f Double)
-isoGauss ms sd = traverse (`normal` sd) ms
-{-# INLINABLE isoGauss #-}
+isoNormal ms sd = traverse (`normal` sd) ms
+{-# INLINABLE isoNormal #-}
 
 -- | The Poisson distribution.
 poisson :: PrimMonad m => Double -> Prob m Int
@@ -278,3 +324,32 @@
     _   -> error "categorical: invalid return value"
 {-# INLINABLE categorical #-}
 
+
+-- | The Zipf-Mandelbrot distribution, generated with the rejection
+-- sampling algorithm X.6.1 shown in
+-- L.Devroye, Non-Uniform Random Variate Generation.
+--
+-- The parameter should be positive, but values close to 1 should be
+-- avoided as they are very computationally intensive. The following
+-- code illustrates this behaviour.
+-- 
+-- >>> samples 10 (zipf 1.1) gen
+-- [11315371987423520,2746946,653,609,2,13,85,4,256184577853,50]
+-- 
+-- >>> samples 10 (zipf 1.5) gen
+-- [19,3,3,1,1,2,1,191,2,1]
+zipf :: (PrimMonad m, Integral b) => Double -> Prob m b
+zipf a = do
+  let
+    b = 2 ** (a - 1)
+    go = do
+        u <- uniform
+        v <- uniform
+        let xInt = floor (u ** (- 1 / (a - 1))) 
+            x = fromIntegral xInt
+            t = (1 + 1 / x) ** (a - 1)
+        if v * x * (t - 1) / (b - 1) <= t / b
+          then return xInt
+          else go
+  go
+{-# INLINABLE zipf #-}