staf

Statistics for Foldable containers

Monthly Downloads: 0

Programming language: Haskell

License: LicenseRef-OtherLicense

Tags: Math    

README

Stats.Foldable

A pure Haskell module that implements a safe interface to a set of statistical computations for all Foldable containers. Folding (i.e. reducing or crushing) is a great fit for statistics, where a full data set has to be reduced to a single object that represents the set in a meaningful way.

Install

There are two standard ways of obtainig the module:

• by cloning the GitHub repository: git clone https://github.com/lovasko/staf
• by using the central Hackage server: cabal install staf

API

All staf functions use the Maybe type to represent a failure (e.g. an empty container provided as the input). All numerical arguments are limited to the least-assuming typeclass. The module exports the following 7 functions:

Arithmetic mean

amean :: (F.Foldable f, Floating a)
     => f a     -- ^ population
     -> Maybe a -- ^ arithmetic mean

Minimum

min :: (F.Foldable f, Ord a)
    => f a     -- ^ population
    -> Maybe a -- ^ minimal value

Maximum

max :: (F.Foldable f, Ord a)
    => f a     -- ^ population
    -> Maybe a -- ^ maximal value

Variance

var :: (F.Foldable f, Floating a)
    => f a     -- ^ population
    -> Maybe a -- ^ variance

Standard deviation

stddev :: (F.Foldable f, Floating a)
       => f a     -- ^ population
       -> Maybe a -- ^ standard deviation

Covariance

covar :: (F.Foldable f, Floating a)
      => f a     -- ^ first population
      -> f a     -- ^ second population
      -> Maybe a -- ^ covariance

Pearson correlation

correl :: (F.Foldable f, Floating a)
       => f a     -- ^ first population
       -> f a     -- ^ second population
       -> Maybe a -- ^ correlation

Numerical stability

There is no current stability guarantee in any of the provided algorithms.

License

The staf module is licensed under the [2-clause BSD license](LICENSE). In case that any other licensing is needed, feel free to contact the author.

Author

Daniel Lovasko [email protected]