statistics 0.5.1.0 → 0.5.1.1

raw patch · 4 files changed

+18/−12 lines, 4 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

Files

• Statistics/Distribution/Poisson.hs +8/−2
• Statistics/Math.hs +6/−7
• Statistics/Sample.hs +3/−2
• statistics.cabal +1/−1

@@ -25,7 +25,7 @@ import qualified Data.Vector.Unboxed as U import qualified Statistics.Distribution as D import Statistics.Constants (m_huge)-import Statistics.Math (logGamma)+import Statistics.Math (factorial, logGamma)  newtype PoissonDistribution = PD {       pdLambda :: Double@@ -49,7 +49,13 @@ {-# INLINE fromLambda #-}  density :: PoissonDistribution -> Double -> Double-density (PD l) x = exp (x * log l - l - logGamma x)+density (PD l) x+    | x < 0                   = 0+    | l >= 100 && x >= l * 10 = 0+    | l >= 3 && x >= l * 100  = 0+    | x >= max 1 l * 200      = 0+    | l < 20 && x <= 100      = exp (-l) * l ** x / factorial (floor x)+    | otherwise               = x * log l - logGamma (x + 1) - l {-# INLINE density #-}  cumulative :: PoissonDistribution -> Double -> Double

@@ -33,19 +33,18 @@ import Statistics.Distribution.Normal (standard) import qualified Data.Vector.Unboxed as U -data C = C {-# UNPACK #-} !Double {-# UNPACK #-} !Double {-# UNPACK #-} !Double+data C = C {-# UNPACK #-} !Double {-# UNPACK #-} !Double  -- | Evaluate a series of Chebyshev polynomials. Uses Clenshaw's -- algorithm. chebyshev :: Double             -- ^ Parameter of each function.           -> U.Vector Double    -- ^ Coefficients of each polynomial-          -- term, in increasing order.+                                --   term, in increasing order.           -> Double-chebyshev x a = fini . U.foldl step (C 0 0 0) .-                U.enumFromThenTo (U.length a - 1) (-1) $ 0-    where step (C u v w) k = C (x2 * v - w + (a ! k)) u v-          fini (C u _ w)   = (u - w) / 2-          x2               = x * 2+chebyshev x a = fini . U.foldl step (C 0 0) $ U.enumFromStepN (U.length a - 1) (-1) (U.length a - 1)+    where step (C b1 b2) k = C ((a ! k) + x2 * b1 - b2) b1+          fini (C b1 b2)   = (a ! 0) + x * b1 - b2+          x2                 = x * 2  -- | The binomial coefficient. --

@@ -78,7 +78,8 @@     where       fini (V a _) = a       go (V m w) (x,xw) = V m' w'-          where m' = m + xw * (x - m) / w'+          where m' | w' == 0   = 0+                   | otherwise = m + xw * (x - m) / w'                 w' = w + xw {-# INLINE meanWeighted #-} @@ -226,7 +227,7 @@ {-# INLINE varianceUnbiased #-}  -- | Standard deviation.  This is simply the square root of the--- maximum likelihood estimate of the variance.+-- unbiased estimate of the variance. stdDev :: Sample -> Double stdDev = sqrt . varianceUnbiased 

@@ -1,5 +1,5 @@ name:           statistics-version:        0.5.1.0+version:        0.5.1.1 synopsis:       A library of statistical types, data, and functions description:   This library provides a number of common functions and types useful