hmatrix-gsl-stats-0.4.1.2: GSL Statistics interface

Copyright(c) A. V. H. McPhail 2010
LicenseBSD3
Maintainerhaskell.vivian.mcphail <at> gmail <dot> com
Stabilityprovisional
Portabilityuses ffi
Safe HaskellNone
LanguageHaskell2010

Numeric.GSL.Statistics

Description

Synopsis

Documentation

mean :: Vector Double -> Double Source

the mean of the elements of a vector

variance :: Vector Double -> Double Source

the sample variance

variance_m :: Double -> Vector Double -> Double Source

the sample variance given the precomputed mean

variance_pm :: Double -> Vector Double -> Double Source

the population variance given the a priori mean

stddev :: Vector Double -> Double Source

the sample standard deviation

stddev_m :: Double -> Vector Double -> Double Source

the sample standard deviation given the precomputed mean

stddev_pm :: Double -> Vector Double -> Double Source

the population standard deviation given the a priori mean

tot_sumsq :: Vector Double -> Double Source

the total sum of squares about the mean

tot_sumsq_m :: Double -> Vector Double -> Double Source

the total sum of squares about the precomputed mean

absdev :: Vector Double -> Double Source

the absolute deviation from the mean

absdev_m :: Double -> Vector Double -> Double Source

the absolute deviation from the precomputed mean

skew :: Vector Double -> Double Source

the skewness of the data (asymmetry of tails)

skew_m_sd :: Double -> Double -> Vector Double -> Double Source

the skewness of the data (asymmetry of tails) with precomputed mean and sd

kurtosis :: Vector Double -> Double Source

the kurtosis of the data (sharpness of peak relative to width)

kurtosis_m_sd :: Double -> Double -> Vector Double -> Double Source

the kurtosis of the data (sharpness of peak relative to width) with precomputed mean and sd

mean_w Source

Arguments

:: Vector Double

weights

-> Vector Double

dataset

-> Double 

the weighted mean of the elements of a vector

variance_w :: Vector Double -> Vector Double -> Double Source

the weighted sample variance

variance_w_m :: Double -> Vector Double -> Vector Double -> Double Source

the weighted sample variance given the precomputed mean

variance_w_pm :: Double -> Vector Double -> Vector Double -> Double Source

the weighted population variance given the a priori mean

stddev_w :: Vector Double -> Vector Double -> Double Source

the weighted sample standard deviation

stddev_w_m :: Double -> Vector Double -> Vector Double -> Double Source

the weighted sample standard deviation given the precomputed mean

stddev_w_pm :: Double -> Vector Double -> Vector Double -> Double Source

the weighted population standard deviation given the a priori mean

tot_sumsq_w :: Vector Double -> Vector Double -> Double Source

the weighted total sum of squares about the mean

tot_sumsq_w_m :: Double -> Vector Double -> Vector Double -> Double Source

the weighted total sum of squares about the precomputed mean

absdev_w :: Vector Double -> Vector Double -> Double Source

the weighted absolute deviation from the mean

absdev_w_m :: Double -> Vector Double -> Vector Double -> Double Source

the weighted absolute deviation from the precomputed mean

skew_w :: Vector Double -> Vector Double -> Double Source

the weighted skewness of the data (asymmetry of tails)

skew_w_m_sd :: Double -> Double -> Vector Double -> Vector Double -> Double Source

the weighted skewness of the data (asymmetry of tails) with precomputed mean and sd

kurtosis_w :: Vector Double -> Vector Double -> Double Source

the weighted kurtosis of the data (sharpness of peak relative to width)

kurtosis_w_m_sd :: Double -> Double -> Vector Double -> Vector Double -> Double Source

the weighted kurtosis of the data (sharpness of peak relative to width) with precomputed mean and sd

lag1auto :: Vector Double -> Double Source

the lag-1 autocorrelation of the data

covariance :: Vector Double -> Vector Double -> Double Source

the covariance of two datasets of the same length

covariance_m :: Double -> Double -> Vector Double -> Vector Double -> Double Source

the covariance of two datasets of the same length

correlation :: Vector Double -> Vector Double -> Double Source

the Pearson correlation of two datasets of the same length

median :: Vector Double -> Double Source

the median value of the dataset, which must be sorted

quantile Source

Arguments

:: Double

the desired quantile from [0..1]

-> Vector Double

the dataset

-> Double 

the quantile value of the dataset, which must be sorted