Mathematical and statistical functions for the NormalKernel kernel defined by the pdf, f(x)=exp(−x2/2)/√2π over the support x∈\R.
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
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.
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()pdfSquared2Norm()The squared 2-norm of the pdf is defined by ∫ba(fX(u))2du where X is the Distribution, fX is its pdf and a,b are the distribution support limits.
variance()The variance of a distribution is defined by the formula varX=E[X2]−E[X]2 where EX is the expectation of distribution X. If the distribution is multivariate the covariance matrix is returned.