Learn R Programming

distr6 (version 1.5.2)

NormalKernel: Normal Kernel

Description

Mathematical and statistical functions for the NormalKernel kernel defined by the pdf, $$f(x) = exp(-x^2/2)/\sqrt{2\pi}$$ over the support \(x \in \R\).

Arguments

Super classes

distr6::Distribution -> distr6::Kernel -> NormalKernel

Public fields

name

Full name of distribution.

short_name

Short name of distribution for printing.

description

Brief description of the distribution.

packages

Packages required to be installed in order to construct the distribution.

Methods

Public methods

Method new()

Creates a new instance of this R6 class.

Usage

NormalKernel$new(decorators = NULL)

Arguments

decorators

(character()) Decorators to add to the distribution during construction.

Method pdfSquared2Norm()

The squared 2-norm of the pdf is defined by $$\int_a^b (f_X(u))^2 du$$ where X is the Distribution, \(f_X\) is its pdf and \(a, b\) are the distribution support limits.

Usage

NormalKernel$pdfSquared2Norm(x = 0, upper = Inf)

Arguments

x

(numeric(1)) Amount to shift the result.

upper

(numeric(1)) Upper limit of the integral.

Method variance()

The variance of a distribution is defined by the formula $$var_X = E[X^2] - E[X]^2$$ where \(E_X\) is the expectation of distribution X. If the distribution is multivariate the covariance matrix is returned.

Usage

NormalKernel$variance(...)

Arguments

...

Unused.

Method clone()

The objects of this class are cloneable with this method.

Usage

NormalKernel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

We use the erf and erfinv error and inverse error functions from pracma.

See Also

Other kernels: Cosine, Epanechnikov, LogisticKernel, Quartic, Sigmoid, Silverman, TriangularKernel, Tricube, Triweight, UniformKernel