NormalKernel: Normal Kernel

NormalKernelR Documentation

Normal Kernel

Description

Mathematical and statistical functions for the NormalKernel kernel defined by the pdf,

f(x) = exp(-x^2/2)/√{2π}

over the support x ε R.

Details

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

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

Inherited 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.

See Also

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


distr6 documentation built on March 28, 2022, 1:05 a.m.