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Gamma distribution in OOP way. Based on AbstractDist
ROOPSD::AbstractDist -> Gamma
ROOPSD::AbstractDist
Gamma
shape
[double] shape of the gamma law
scale
[double] scale of the gamma law
params
[vector] params of the gamma law
Gamma$new()
Gamma$clone()
Inherited methods ROOPSD::AbstractDist$cdf() ROOPSD::AbstractDist$density() ROOPSD::AbstractDist$diagnostic() ROOPSD::AbstractDist$fit() ROOPSD::AbstractDist$icdf() ROOPSD::AbstractDist$isf() ROOPSD::AbstractDist$logdensity() ROOPSD::AbstractDist$pdeltaCI() ROOPSD::AbstractDist$qdeltaCI() ROOPSD::AbstractDist$qgradient() ROOPSD::AbstractDist$rvs() ROOPSD::AbstractDist$sf()
ROOPSD::AbstractDist$cdf()
ROOPSD::AbstractDist$density()
ROOPSD::AbstractDist$diagnostic()
ROOPSD::AbstractDist$fit()
ROOPSD::AbstractDist$icdf()
ROOPSD::AbstractDist$isf()
ROOPSD::AbstractDist$logdensity()
ROOPSD::AbstractDist$pdeltaCI()
ROOPSD::AbstractDist$qdeltaCI()
ROOPSD::AbstractDist$qgradient()
ROOPSD::AbstractDist$rvs()
ROOPSD::AbstractDist$sf()
new()
Create a new Gamma object.
Gamma$new(shape = 0.5, scale = 1)
[double] shape parameter
[double] scale parameter
A new `Gamma` object.
clone()
The objects of this class are cloneable with this method.
Gamma$clone(deep = FALSE)
deep
Whether to make a deep clone.
See AbstractDist for generic methods
## Generate sample scale = 1.5 shape = 0.5 gaml = ROOPSD::Gamma$new( scale = scale , shape = shape ) X = gaml$rvs( n = 1000 ) ## And fit parameters gaml$fit(X)
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