Normal {ROOPSD}

Normal

Description

Normal distribution in OOP way. Based on AbstractDist

Details

See AbstractDist for generic methods

Super class

ROOPSD::AbstractDist -> Normal

Active bindings

mean

[double] mean of the normal law

sd

[double] standard deviation of the normal law

params

[vector] params of the normal law

Methods

Public methods

• Normal$new()

• Normal$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()

Method new()

Create a new Normal object.

Usage

Normal$new(mean = 0, sd = 1)

Arguments

mean

[double] Mean of the normal law

sd

[double] Standard deviation of the normal law

Returns

A new 'Normal' object.

Method clone()

The objects of this class are cloneable with this method.

Usage

Normal$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

## Generate sample
mean  = 1
sd    = 0.5
norml = ROOPSD::Normal$new( mean = mean , sd = sd )
X     = norml$rvs( n = 1000 )

## And fit parameters
norml$fit(X)