NormalDistribution {rdecision}

A parametrized Normal distribution

Description

An R6 class representing a parametrized Normal distribution.

Details

A Normal distribution with hyperparameters mean (mu) and standard deviation (sd). Inherits from class Distribution.

Super class

rdecision::Distribution -> NormalDistribution

Methods

Public methods

• NormalDistribution$new()

• NormalDistribution$distribution()

• NormalDistribution$sample()

• NormalDistribution$mean()

• NormalDistribution$SD()

• NormalDistribution$quantile()

• NormalDistribution$clone()

Inherited methods

• rdecision::Distribution$mode()
• rdecision::Distribution$order()
• rdecision::Distribution$r()
• rdecision::Distribution$varcov()

Method new()

Create a parametrized normal distribution.

Usage

NormalDistribution$new(mu, sigma)

Arguments

mu

Mean of the Normal distribution.

sigma

Standard deviation of the Normal distribution.

Returns

A NormalDistribution object.

Method distribution()

Accessor function for the name of the distribution.

Usage

NormalDistribution$distribution()

Returns

Distribution name as character string.

Method sample()

Draw a random sample from the model variable.

Usage

NormalDistribution$sample(expected = FALSE)

Arguments

expected

If TRUE, sets the next value retrieved by a call to r() to be the mean of the distribution.

Returns

A sample drawn at random.

Method mean()

Return the mean value of the distribution.

Usage

NormalDistribution$mean()

Returns

Expected value as a numeric value.

Method SD()

Return the standard deviation of the distribution.

Usage

NormalDistribution$SD()

Returns

Standard deviation as a numeric value

Method quantile()

Return the quantiles of the Normal uncertainty distribution.

Usage

NormalDistribution$quantile(probs)

Arguments

probs

Vector of probabilities, in range [0,1].

Returns

Vector of quantiles.

Method clone()

The objects of this class are cloneable with this method.

Usage

NormalDistribution$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

Andrew J. Sims [e-mail]