hmatrix-gsl-stats-0.1.2.12: GSL Statistics interface

Portabilityuses ffi
Stabilityprovisional
Maintainerhaskell.vivian.mcphail <at> gmail <dot> com
Safe HaskellSafe-Infered

Numeric.GSL.Statistics

Description

GSL statistics functions

http://www.gnu.org/software/gsl/manual/

Synopsis

Documentation

mean :: Vector Double -> DoubleSource

the mean of the elements of a vector

variance :: Vector Double -> DoubleSource

the sample variance

variance_m :: Double -> Vector Double -> DoubleSource

the sample variance given the precomputed mean

variance_pm :: Double -> Vector Double -> DoubleSource

the population variance given the a priori mean

stddev :: Vector Double -> DoubleSource

the sample standard deviation

stddev_m :: Double -> Vector Double -> DoubleSource

the sample standard deviation given the precomputed mean

stddev_pm :: Double -> Vector Double -> DoubleSource

the population standard deviation given the a priori mean

tot_sumsq :: Vector Double -> DoubleSource

the total sum of squares about the mean

tot_sumsq_m :: Double -> Vector Double -> DoubleSource

the total sum of squares about the precomputed mean

absdev :: Vector Double -> DoubleSource

the absolute deviation from the mean

absdev_m :: Double -> Vector Double -> DoubleSource

the absolute deviation from the precomputed mean

skew :: Vector Double -> DoubleSource

the skewness of the data (asymmetry of tails)

skew_m_sd :: Double -> Double -> Vector Double -> DoubleSource

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

kurtosis :: Vector Double -> DoubleSource

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

kurtosis_m_sd :: Double -> Double -> Vector Double -> DoubleSource

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

mean_wSource

Arguments

:: Vector Double

weights

-> Vector Double

dataset

-> Double 

the weighted mean of the elements of a vector

variance_w :: Vector Double -> Vector Double -> DoubleSource

the weighted sample variance

variance_w_m :: Double -> Vector Double -> Vector Double -> DoubleSource

the weighted sample variance given the precomputed mean

variance_w_pm :: Double -> Vector Double -> Vector Double -> DoubleSource

the weighted population variance given the a priori mean

stddev_w :: Vector Double -> Vector Double -> DoubleSource

the weighted sample standard deviation

stddev_w_m :: Double -> Vector Double -> Vector Double -> DoubleSource

the weighted sample standard deviation given the precomputed mean

stddev_w_pm :: Double -> Vector Double -> Vector Double -> DoubleSource

the weighted population standard deviation given the a priori mean

tot_sumsq_w :: Vector Double -> Vector Double -> DoubleSource

the weighted total sum of squares about the mean

tot_sumsq_w_m :: Double -> Vector Double -> Vector Double -> DoubleSource

the weighted total sum of squares about the precomputed mean

absdev_w :: Vector Double -> Vector Double -> DoubleSource

the weighted absolute deviation from the mean

absdev_w_m :: Double -> Vector Double -> Vector Double -> DoubleSource

the weighted absolute deviation from the precomputed mean

skew_w :: Vector Double -> Vector Double -> DoubleSource

the weighted skewness of the data (asymmetry of tails)

skew_w_m_sd :: Double -> Double -> Vector Double -> Vector Double -> DoubleSource

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

kurtosis_w :: Vector Double -> Vector Double -> DoubleSource

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

kurtosis_w_m_sd :: Double -> Double -> Vector Double -> Vector Double -> DoubleSource

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

lag1auto :: Vector Double -> DoubleSource

the lag-1 autocorrelation of the data

covariance :: Vector Double -> Vector Double -> DoubleSource

the covariance of two datasets of the same length

covariance_m :: Double -> Double -> Vector Double -> Vector Double -> DoubleSource

the covariance of two datasets of the same length

correlation :: Vector Double -> Vector Double -> DoubleSource

the Pearson correlation of two datasets of the same length

median :: Vector Double -> DoubleSource

the median value of the dataset, which must be sorted

quantileSource

Arguments

:: Double

the desired quantile from [0..1]

-> Vector Double

the dataset

-> Double 

the quantile value of the dataset, which must be sorted