NormalKernel | R Documentation |
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
.
We use the erf
and erfinv
error and inverse error functions from
pracma.
distr6::Distribution
-> distr6::Kernel
-> NormalKernel
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.
distr6::Distribution$cdf()
distr6::Distribution$confidence()
distr6::Distribution$correlation()
distr6::Distribution$getParameterValue()
distr6::Distribution$iqr()
distr6::Distribution$liesInSupport()
distr6::Distribution$liesInType()
distr6::Distribution$parameters()
distr6::Distribution$pdf()
distr6::Distribution$prec()
distr6::Distribution$print()
distr6::Distribution$quantile()
distr6::Distribution$rand()
distr6::Distribution$setParameterValue()
distr6::Distribution$stdev()
distr6::Distribution$strprint()
distr6::Distribution$summary()
distr6::Distribution$workingSupport()
distr6::Kernel$cdfSquared2Norm()
distr6::Kernel$mean()
distr6::Kernel$median()
distr6::Kernel$mode()
distr6::Kernel$skewness()
new()
Creates a new instance of this R6 class.
NormalKernel$new(decorators = NULL)
decorators
(character())
Decorators to add to the distribution during construction.
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.
NormalKernel$pdfSquared2Norm(x = 0, upper = Inf)
x
(numeric(1))
Amount to shift the result.
upper
(numeric(1))
Upper limit of the integral.
variance()
The variance of a distribution is defined by the formula
var_X = E[X^2] - E[X]^2
where E_X
is the expectation of distribution X. If the distribution is multivariate the
covariance matrix is returned.
NormalKernel$variance(...)
...
Unused.
clone()
The objects of this class are cloneable with this method.
NormalKernel$clone(deep = FALSE)
deep
Whether to make a deep clone.
Other kernels:
Cosine
,
Epanechnikov
,
LogisticKernel
,
Quartic
,
Sigmoid
,
Silverman
,
TriangularKernel
,
Tricube
,
Triweight
,
UniformKernel
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