Mathematical and statistical functions for the NormalKernel kernel defined by the pdf, $$f(x) = exp(-x^2/2)/\sqrt{2\pi}$$ over the support \(x \in \R\).
distr6::Distribution -> distr6::Kernel -> NormalKernel
nameFull name of distribution.
short_nameShort name of distribution for printing.
descriptionBrief description of the distribution.
packagesPackages required to be installed in order to construct the distribution.
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)
deepWhether to make a deep clone.
We use the erf and erfinv error and inverse error functions from
pracma.
Other kernels:
Cosine,
Epanechnikov,
LogisticKernel,
Quartic,
Sigmoid,
Silverman,
TriangularKernel,
Tricube,
Triweight,
UniformKernel