How to Install and Uninstall ghc-statistics-linreg Package on openSUSE Leap

Last updated: December 28,2024

1. Install "ghc-statistics-linreg" package

Please follow the guidelines below to install ghc-statistics-linreg on openSUSE Leap


    
        
            $
        

        
            
                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                ghc-statistics-linreg
            
                    
    
    Copied

2. Uninstall "ghc-statistics-linreg" package

Please follow the guidelines below to uninstall ghc-statistics-linreg on openSUSE Leap:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                ghc-statistics-linreg
            
                    
    
    Copied

3. Information about the ghc-statistics-linreg package on openSUSE Leap

Information for package ghc-statistics-linreg:
----------------------------------------------
Repository : Main Repository
Name : ghc-statistics-linreg
Version : 0.3-bp155.2.14
Arch : x86_64
Vendor : openSUSE
Installed Size : 127.0 KiB
Installed : No
Status : not installed
Source package : ghc-statistics-linreg-0.3-bp155.2.14.src
Upstream URL : https://hackage.haskell.org/package/statistics-linreg
Summary : Linear regression between two samples, based on the 'statistics' package
Description :
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.