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Uniform distribution in OOP way. Based on AbstractDist
ROOPSD::AbstractDist -> Uniform
ROOPSD::AbstractDist
Uniform
min
[double] min of the uniform law
max
[double] max of the uniform law
params
[vector] params of the uniform law
Uniform$new()
Uniform$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 Uniform object.
Uniform$new(min = 0, max = 1)
[double] Min of the uniform law
[double] Max of the uniform law
A new `Uniform` object.
clone()
The objects of this class are cloneable with this method.
Uniform$clone(deep = FALSE)
deep
Whether to make a deep clone.
See AbstractDist for generic methods
## Generate sample min = -1 max = 1 unifl = ROOPSD::Uniform$new( min = min , max = max ) X = unifl$rvs( n = 1000 ) ## And fit parameters unifl$fit(X)
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