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 $x \in \R$.

Value

Returns an R6 object inheriting from class Kernel.

Constructor

NormalKernel$new(decorators = NULL)

Constructor Arguments

Argument	Type	Details
`decorators`	Decorator	decorators to add functionality.

Public Variables

Variable	Return
`name`	Name of distribution.
`short_name`	Id of distribution.
`description`	Brief description of distribution.

Public Methods

Accessor Methods	Link
`decorators`	`decorators`
`traits`	`traits`
`valueSupport`	`valueSupport`
`variateForm`	`variateForm`
`type`	`type`
`properties`	`properties`
`support`	`support`
`symmetry`	`symmetry`
`sup`	`sup`
`inf`	`inf`
`dmax`	`dmax`
`dmin`	`dmin`
`skewnessType`	`skewnessType`
`kurtosisType`	`kurtosisType`



d/p/q/r Methods	Link
`pdf(x1, ..., log = FALSE, simplify = TRUE)`	`pdf`
`cdf(x1, ..., lower.tail = TRUE, log.p = FALSE, simplify = TRUE)`	`cdf`
`quantile(p, ..., lower.tail = TRUE, log.p = FALSE, simplify = TRUE)`	`quantile.Distribution`
`rand(n, simplify = TRUE)`	`rand`



Statistical Methods	Link
`squared2Norm()`	`squared2Norm`
`prec()`	`prec`
`stdev()`	`stdev`
`mode(which = "all")`	`mode`
`mean()`	`mean.Distribution`
`median()`	`median.Distribution`
`iqr()`	`iqr`
`correlation()`	`correlation`



Parameter Methods	Link
`parameters(id)`	`parameters`
`getParameterValue(id, error = "warn")`	`getParameterValue`
`setParameterValue(..., lst = NULL, error = "warn")`	`setParameterValue`



Validation Methods	Link
`liesInSupport(x, all = TRUE, bound = FALSE)`	`liesInSupport`
`liesInType(x, all = TRUE, bound = FALSE)`	`liesInType`



Representation Methods	Link
`strprint(n = 2)`	`strprint`
`print(n = 2)`	`print`
`summary(full = T)`	`summary.Distribution`

Details

We use the erf and erfinv error and inverse error functions from the Pracma package.