statistics -package:gitlab-haskell

A library of statistical types, data, and functions This library provides a number of common functions and types useful in statistics. We focus on high performance, numerical robustness, and use of good algorithms. Where possible, we provide references to the statistical literature. . The library's facilities can be divided into four broad categories: . * Working with widely used discrete and continuous probability distributions. (There are dozens of exotic distributions in use; we focus on the most common.) . * Computing with sample data: quantile estimation, kernel density estimation, histograms, bootstrap methods, significance testing, and regression and autocorrelation analysis. . * Random variate generation under several different distributions. . * Common statistical tests for significant differences between samples.

Obtain statistics about a running supervisor.

Obtain statistics (from/to anywhere) about a mailbox.

Track the number of tests that were run and failures of a TestTree or sub-tree.

Collect statistics.

Statistics gathered on a running expression builder. See getStatistics.

This module provides a set of statistics over the execution of the registry. This allows to get better insights over the execution or test that the registry is well configured

This datatype records: - the created values - the applied functions - the specializations used to create values

This module returns creation data about the values created when created a value of a given type

GSL statistics functions http://www.gnu.org/software/gsl/manual/

Basic bounded statistics. In the following, a bound n is given stating the number of periods over which to compute the statistic (n == 1 computes it only over the current period).

Provides the main interface for retrieving statistics tabulated by a histogram.

Useful statistical functions

Set the statistics.interval.ms for the producer.

Set the statistics.interval.ms for the producer.

Linear regression between two samples, based on the 'statistics' package. Provides functions to perform a linear regression between 2 samples, see the documentation of the linearRegression functions. This library is based on the statistics package.

• 0.3: you can now use all functions on any instance of the Vector class (not just unboxed vectors).
• 0.2.4: added distribution estimations for standard regression parameters.
• 0.2.3: added robust-fit support.
• 0.2.2: added the Total-Least-Squares version and made some refactoring to eliminate code duplication
• 0.2.1: added the r-squared version and improved the performances.

Code sample:

import qualified Data.Vector.Unboxed as U

test :: Int -> IO ()
test k = do
let n = 10000000
let a = k*n + 1
let b = (k+1)*n
let xs = U.fromList [a..b]
let ys = U.map (\x -> x*100 + 2000) xs
-- thus 100 and 2000 are the alpha and beta we want
putStrLn "linearRegression:"
print $ linearRegression xs ys

The r-squared and Total-Least-Squares versions work the same way.

Not on Stackage, so not searched. Functions for working with Dirichlet densities and mixtures on vectors.

Not on Stackage, so not searched. An implementation of high performance, minimal statistics functions

Not on Stackage, so not searched. Random variate generation from hypergeometric distributions

Not on Stackage, so not searched. A library of statistical types, data, and functions