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1. wilcoxonRankSums :: (Ord a, Unbox a) => Vector a -> Vector a -> (Double, Double)

   statistics	Statistics.Test.MannWhitneyU

   The Wilcoxon Rank Sums Test. This test calculates the sum of ranks for the given two samples. The samples are ordered, and assigned ranks (ties are given their average rank), then these ranks are summed for each sample. The return value is (W₁, W₂) where W₁ is the sum of ranks of the first sample and W₂ is the sum of ranks of the second sample. This test is trivially transformed into the Mann-Whitney U test. You will probably want to use mannWhitneyU and the related functions for testing significance, but this function is exposed for completeness.

2. ChecksumIncorrect :: FormatError

   tar	Codec.Archive.Tar

   No documentation available.

3. timeZoneSummerOnly :: TimeZone -> Bool

   time-compat	Data.Time.LocalTime.Compat

   Is this time zone just persisting for the summer?

4. asumOf :: Alternative f => Getting (Endo (f a)) s (f a) -> s -> f a

   diagrams-lib	Diagrams.Prelude

   The sum of a collection of actions, generalizing concatOf.

   >>> asumOf both ("hello","world")
   "helloworld"
   

   >>> asumOf each (Nothing, Just "hello", Nothing)
   Just "hello"
   

   asum ≡ asumOf folded
   

   asumOf :: Alternative f => Getter s (f a)     -> s -> f a
   asumOf :: Alternative f => Fold s (f a)       -> s -> f a
   asumOf :: Alternative f => Lens' s (f a)      -> s -> f a
   asumOf :: Alternative f => Iso' s (f a)       -> s -> f a
   asumOf :: Alternative f => Traversal' s (f a) -> s -> f a
   asumOf :: Alternative f => Prism' s (f a)     -> s -> f a
   

5. msumOf :: MonadPlus m => Getting (Endo (m a)) s (m a) -> s -> m a

   diagrams-lib	Diagrams.Prelude

   The sum of a collection of actions, generalizing concatOf.

   >>> msumOf both ("hello","world")
   "helloworld"
   

   >>> msumOf each (Nothing, Just "hello", Nothing)
   Just "hello"
   

   msum ≡ msumOf folded
   

   msumOf :: MonadPlus m => Getter s (m a)     -> s -> m a
   msumOf :: MonadPlus m => Fold s (m a)       -> s -> m a
   msumOf :: MonadPlus m => Lens' s (m a)      -> s -> m a
   msumOf :: MonadPlus m => Iso' s (m a)       -> s -> m a
   msumOf :: MonadPlus m => Traversal' s (m a) -> s -> m a
   msumOf :: MonadPlus m => Prism' s (m a)     -> s -> m a
   

6. data KB2Sum

   math-functions	Numeric.Sum

   Second-order Kahan-Babuška summation. This is more computationally costly than Kahan-Babuška-Neumaier summation, running at about a third the speed. Its advantage is that it can lose less precision (in admittedly obscure cases). This method compensates for error in both the sum and the first-order compensation term, hence the use of "second order" in the name.

7. KB2Sum :: Double -> Double -> Double -> KB2Sum

   math-functions	Numeric.Sum

   No documentation available.

8. data KBNSum

   math-functions	Numeric.Sum

   Kahan-Babuška-Neumaier summation. This is a little more computationally costly than plain Kahan summation, but is always at least as accurate.

9. KBNSum :: Double -> Double -> KBNSum

   math-functions	Numeric.Sum

   No documentation available.

10. data KahanSum

    math-functions	Numeric.Sum

    Kahan summation. This is the least accurate of the compensated summation methods. In practice, it only beats naive summation for inputs with large magnitude. Kahan summation can be less accurate than naive summation for small-magnitude inputs. This summation method is included for completeness. Its use is not recommended. In practice, KBNSum is both 30% faster and more accurate.

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