Documentation
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Overview ¶
Statistics package.
Copyright 2012 The Probab Authors. All rights reserved. See the LICENSE file.
Index ¶
- func Agostino(x []float64, alternative int) (skew, z, pVal float64)
- func Anscombe(x []float64, alternative int) (kurt, z, pVal float64)
- func BinomPConfInt(n, k int64, alpha float64) (lo, hi float64)
- func Bonett(x []float64, alternative int) (kurt, z, pVal float64)
- func Cov(data *DenseMatrix) *DenseMatrix
- func Geary(x []float64) float64
- func Jarque(x []float64) (jb, pVal float64)
- func Kurt(x []float64) float64
- func Mean(x []float64) float64
- func SCov(data *DenseMatrix) *DenseMatrix
- func SampleMeanVar(x []float64) (μ, σ2 float64)
- func Skew(x []float64) float64
- type Vector
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func Anscombe ¶
Anscombe performs Anscombe-Glynn test of kurtosis for normally distributed data vector.
func BinomPConfInt ¶
BinomPConfInt returns a one-sided frequentist Confidence Interval for binomial parameter estimated from a random sample. Ref.: Hahn & Meeker (1991).
func Bonett ¶
Bonett performs Bonett-Seier test of Geary’s measure of kurtosis for normally distributed data vector.
func Cov ¶
func Cov(data *DenseMatrix) *DenseMatrix
Covariance matrix between columns of data matrix, two-pass algorithm http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Covariance
func Jarque ¶
Jarque performs performs the Jarque-Bera test on the given data sample to determine if the data are sample drawn from a normal population.
func Kurt ¶
Kurt returns the estimator of Pearson’s measure of kurtosis of the data vector. This is NOT the Excess Kustosis!
func SCov ¶
func SCov(data *DenseMatrix) *DenseMatrix
Sample covariance matrix between columns of data matrix, for samples This is R:cov()
func SampleMeanVar ¶
Sample mean and unbiased (Bessel correction) variance estimates for a data vector.
Types ¶
type Vector ¶
func SVar ¶
func SVar(data *DenseMatrix) *Vector
Sample variance vector of columns of data matrix, one-pass algorithm This is R:var()
func Var ¶
func Var(data *DenseMatrix) *Vector
Population variance vector of columns of data matrix, one-pass algorithm http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#On-line_algorithm
Source Files