diff --git a/ChangeLog.md b/ChangeLog.md
--- a/ChangeLog.md
+++ b/ChangeLog.md
@@ -1,3 +1,8 @@
-# Changelog for splitmix-distributions
+0.3 :
 
-## Unreleased changes
+- type signatures of all generators are now parametrized over some Monad m rather than Identity. This allows for more flexibility on the use site.
+
+0.2 :
+
+- add Pareto, Dirichlet, multinomial distributions
+
diff --git a/splitmix-distributions.cabal b/splitmix-distributions.cabal
--- a/splitmix-distributions.cabal
+++ b/splitmix-distributions.cabal
@@ -1,5 +1,5 @@
 name:           splitmix-distributions
-version:        0.1.0.0
+version:        0.3.0.0
 description:    Random samplers for some common distributions, as well as a convenient interface for composing them, based on splitmix. Please see the README on GitHub at <https://github.com/ocramz/splitmix-distributions#readme>
 homepage:       https://github.com/ocramz/splitmix-distributions#readme
 bug-reports:    https://github.com/ocramz/splitmix-distributions/issues
@@ -34,7 +34,7 @@
     , transformers
   default-language: Haskell2010
 
-test-suite splitmix-distributions-test
+test-suite test
   type: exitcode-stdio-1.0
   main-is: Spec.hs
   hs-source-dirs:
diff --git a/src/System/Random/SplitMix/Distributions.hs b/src/System/Random/SplitMix/Distributions.hs
--- a/src/System/Random/SplitMix/Distributions.hs
+++ b/src/System/Random/SplitMix/Distributions.hs
@@ -40,8 +40,11 @@
   stdNormal, normal,
   beta,
   gamma,
+  pareto,
+  dirichlet,
   -- ** Discrete
-  bernoulli,
+  bernoulli, fairCoin,
+  multinomial,
   -- * PRNG
   -- ** Pure
   Gen, sample,
@@ -52,7 +55,9 @@
 
 import Control.Monad (replicateM)
 import Control.Monad.IO.Class (MonadIO(..))
+import Data.Foldable (toList)
 import Data.Functor.Identity (Identity(..))
+import Data.List (findIndex)
 import GHC.Word (Word64)
 
 -- erf
@@ -89,30 +94,58 @@
 
 
 -- | Bernoulli trial
-bernoulli :: Double -- ^ bias parameter \( 0 \lt p \lt 1 \)
-          -> Gen Bool
+bernoulli :: Monad m =>
+             Double -- ^ bias parameter \( 0 \lt p \lt 1 \)
+          -> GenT m Bool
 bernoulli p = withGen (bernoulliF p)
 
+-- | A fair coin toss returns either value with probability 0.5
+fairCoin :: Monad m => GenT m Bool
+fairCoin = bernoulli 0.5
+
+-- | Multinomial distribution
+--
+-- NB : returns @Nothing@ if any of the input probabilities is negative
+multinomial :: (Monad m, Foldable t) =>
+               Int -- ^ number of Bernoulli trials \( n \gt 0 \)
+            -> t Double -- ^ probability vector \( p_i \gt 0 , \forall i \) (does not need to be normalized)
+            -> GenT m (Maybe [Int])
+multinomial n ps = do
+    let (cumulative, total) = runningTotals (toList ps)
+    ms <- replicateM n $ do
+      z <- uniformR 0 total
+      pure $ findIndex (> z) cumulative
+        -- Just g  -> return g
+        -- Nothing -> error "splitmix-distributions: invalid probability vector"
+    pure $ sequence ms
+  where
+    runningTotals :: Num a => [a] -> ([a], a)
+    runningTotals xs = let adds = scanl1 (+) xs in (adds, sum xs)
+{-# INLINABLE multinomial #-}
+
+
 -- | Uniform between two values
-uniformR :: Double -- ^ low
+uniformR :: Monad m =>
+            Double -- ^ low
          -> Double -- ^ high
-         -> Gen Double
+         -> GenT m Double
 uniformR lo hi = scale <$> stdUniform
   where
     scale x = x * (hi - lo) + lo
 
--- | Standard normal
-stdNormal :: Gen Double
+-- | Standard normal distribution
+stdNormal :: Monad m => GenT m Double
 stdNormal = normal 0 1
 
 -- | Uniform in [0, 1)
-stdUniform :: Gen Double
+stdUniform :: Monad m => GenT m Double
 stdUniform = withGen nextDouble
 
 -- | Beta distribution, from two standard uniform samples
-beta :: Double -- ^ shape parameter \( \alpha \gt 0 \) 
+beta :: Monad m =>
+        Double -- ^ shape parameter \( \alpha \gt 0 \) 
      -> Double -- ^ shape parameter \( \beta \gt 0 \)
-     -> Gen Double
+     -> GenT m Double
 beta a b = go
   where
     go = do
@@ -128,9 +161,10 @@
 -- | Gamma distribution, using Ahrens-Dieter accept-reject (algorithm GD):
 --
 -- Ahrens, J. H.; Dieter, U (January 1982). "Generating gamma variates by a modified rejection technique". Communications of the ACM. 25 (1): 47–54
-gamma :: Double -- ^ shape parameter \( k \gt 0 \)
+gamma :: Monad m =>
+         Double -- ^ shape parameter \( k \gt 0 \)
       -> Double -- ^ scale parameter \( \theta \gt 0 \)
-      -> Gen Double
+      -> GenT m Double
 gamma k th = do
   xi <- sampleXi
   us <- replicateM n (log <$> stdUniform)
@@ -153,16 +187,42 @@
             let xi = 1 - log v
             in (xi, w * exp (- xi))
 
+-- | Pareto distribution
+pareto :: Monad m =>
+          Double -- ^ shape parameter \( \alpha \gt 0 \)
+       -> Double -- ^ scale parameter \( x_{min} \gt 0 \)
+       -> GenT m Double
+pareto a xmin = do
+  y <- exponential a
+  return $ xmin * exp y
+{-# INLINABLE pareto #-}
 
+-- | The Dirichlet distribution with the provided concentration parameters.
+--   The dimension of the distribution is determined by the number of
+--   concentration parameters supplied.
+--
+--   >>> sample 1234 (dirichlet [0.1, 1, 10])
+--   [2.3781130220132788e-11,6.646079701567026e-2,0.9335392029605486]
+dirichlet :: (Monad m, Traversable f) =>
+             f Double -- ^ concentration parameters \( \gamma_i \gt 0 , \forall i \)
+          -> GenT m (f Double)
+dirichlet as = do
+  zs <- traverse (`gamma` 1) as
+  return $ fmap (/ sum zs) zs
+{-# INLINABLE dirichlet #-}
+
+
 -- | Normal distribution
-normal :: Double -- ^ mean
+normal :: Monad m =>
+          Double -- ^ mean
        -> Double -- ^ standard deviation \( \sigma \gt 0 \)
-       -> Gen Double
+       -> GenT m Double
 normal mu sig = withGen (normalF mu sig)
 
 -- | Exponential distribution
-exponential :: Double -- ^ rate parameter \( \lambda > 0 \)
-            -> Gen Double
+exponential :: Monad m =>
+               Double -- ^ rate parameter \( \lambda > 0 \)
+            -> GenT m Double
 exponential l = withGen (exponentialF l)
 
 -- | Wrap a 'splitmix' PRNG function