Class: distribution.abstract.class

Base class for derived distributions

Super class

mastiff::R6.class.class -> distribution.abstract.class

Active bindings

name

The name of the distribution

param_names

The names of all distribution parameters

params

Named list of distribution parameters

interfaces

The list of available class interfaces

mean

Mean of the distribution

sd

Standard deviation of the distribution

var

Variance of the distribution

Methods

Public methods

• distribution.abstract.class$new()

• distribution.abstract.class$d()

• distribution.abstract.class$p()

• distribution.abstract.class$q()

• distribution.abstract.class$r()

• distribution.abstract.class$clone()

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Method new()

Create a new object of class distribution.abstract.class

Usage

distribution.abstract.class$new()

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Method d()

Template base class function for density function of a distribution

Usage

distribution.abstract.class$d(x, log = FALSE)

Arguments

x

vector of quantiles.

log

logical; if TRUE, probabilities p are given as log(p).

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Method p()

Template base class function for cumulative distribution function

Usage

distribution.abstract.class$p(q, lower.tail = TRUE, log.p = FALSE)

Arguments

q

vector of quantiles.

lower.tail

logical; if TRUE (default), probabilities are \(P[ X \leq x ]\), otherwise, \(P[X>x]\).

log.p

logical; if TRUE, probabilities p are given as log(p).

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Method q()

Template base class function for quantile function of a distribution

Usage

distribution.abstract.class$q(p, lower.tail = TRUE, log.p = FALSE)

Arguments

p

vector of probabilities.

lower.tail

logical; if TRUE (default), probabilities are \(P[ X \leq x ]\), otherwise, \(P[X>x]\).

log.p

logical; if TRUE, probabilities p are given as log(p).

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Method r()

Template base class function for sampling random variates

Usage

distribution.abstract.class$r(n)

Arguments

n

number of observations. If length( n ) > 1, the length is taken to be the number required.

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Method clone()

The objects of this class are cloneable with this method.

Usage

distribution.abstract.class$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.