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1. data LinearTransform d

   statistics	Statistics.Distribution.Transform

   Linear transformation applied to distribution.

   LinearTransform μ σ _
   x' = μ + σ·x
   

2. LinearTransform :: Double -> Double -> d -> LinearTransform d

   statistics	Statistics.Distribution.Transform

   No documentation available.

3. module Statistics.Distribution.Uniform

   Variate distributed uniformly in the interval.

4. data UniformDistribution

   statistics	Statistics.Distribution.Uniform

   Uniform distribution from A to B

5. uniformA :: UniformDistribution -> Double

   statistics	Statistics.Distribution.Uniform

   Low boundary of distribution

6. uniformB :: UniformDistribution -> Double

   statistics	Statistics.Distribution.Uniform

   Upper boundary of distribution

7. uniformDistr :: Double -> Double -> UniformDistribution

   statistics	Statistics.Distribution.Uniform

   Create uniform distribution.

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

   statistics	Statistics.Distribution.Uniform

   Create uniform distribution.

9. 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).

10. 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.

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