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1. class (Finite self a ~ af, Finite self b ~ bf) => FiniteSum self (a :: Type -> Type) (b :: Type -> Type) (af :: Bool) (bf :: Bool)

   generic-arbitrary	Test.QuickCheck.Arbitrary.Generic

   No documentation available.

2. class FiniteSumElem (self :: k) (a :: k1 -> Type)

   generic-arbitrary	Test.QuickCheck.Arbitrary.Generic

   No documentation available.

3. 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.

4. KB2Sum :: Double -> Double -> Double -> KB2Sum

   math-functions	Numeric.Sum

   No documentation available.

5. 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.

6. KBNSum :: Double -> Double -> KBNSum

   math-functions	Numeric.Sum

   No documentation available.

7. 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.

8. KahanSum :: Double -> Double -> KahanSum

   math-functions	Numeric.Sum

   No documentation available.

9. pairwiseSum :: Vector v Double => v Double -> Double

   math-functions	Numeric.Sum

   O(n) Sum a vector of values using pairwise summation. This approach is perhaps 10% faster than KBNSum, but has poorer bounds on its error growth. Instead of having roughly constant error regardless of the size of the input vector, in the worst case its accumulated error grows with O(log n).

10. DeepSubsumption :: Extension

    ghc-boot-th	GHC.LanguageExtensions.Type

    No documentation available.

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