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Within LTS Haskell 24.39 (ghc-9.10.3)

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  1. module Statistics.Distribution.Uniform

    Variate distributed uniformly in the interval.

  2. data UniformDistribution

    statistics Statistics.Distribution.Uniform

    Uniform distribution from A to B

  3. uniformA :: UniformDistribution -> Double

    statistics Statistics.Distribution.Uniform

    Low boundary of distribution

  4. uniformB :: UniformDistribution -> Double

    statistics Statistics.Distribution.Uniform

    Upper boundary of distribution

  5. uniformDistr :: Double -> Double -> UniformDistribution

    statistics Statistics.Distribution.Uniform

    Create uniform distribution.

  6. uniformDistrE :: Double -> Double -> Maybe UniformDistribution

    statistics Statistics.Distribution.Uniform

    Create uniform distribution.

  7. rfor :: Monad m => Int -> Int -> (Int -> m ()) -> m ()

    statistics Statistics.Function

    Simple reverse-for loop. Counts from start-1 to end (which must be less than start).

  8. welfordMean :: Vector v Double => v Double -> Double

    statistics Statistics.Sample

    O(n) Arithmetic mean. This uses Welford's algorithm to provide numerical stability, using a single pass over the sample data. Compared to mean, this loses a surprising amount of precision unless the inputs are very large.

  9. module Statistics.Transform

    Fourier-related transformations of mathematical functions. These functions are written for simplicity and correctness, not speed. If you need a fast FFT implementation for your application, you should strongly consider using a library of FFTW bindings instead.

  10. fromForeignPtr :: ForeignPtr a -> Int -> Vector a

    storablevector Data.StorableVector.Base

    O(1) Build a Vector from a ForeignPtr

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