@hackage statistics-linreg0.3
Linear regression between two samples, based on the 'statistics' package.
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MIT
Maintainer
Alp Mestanogullari <alpmestan@gmail.com>
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Dependencies (7)
- MonadRandom >=0.1
- base >=4 && <5
- random >=1.0
- random-shuffle >=0.0.4
- safe >=0.3
- statistics >=0.5 Show all…
Dependents (3)
@hackage/eventlog2html, @hackage/acme-everything, @cardano/network-mux
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.