Abstract Kernel Class

Abstract class that cannot be constructed directly.

Value

Returns error. Abstract classes cannot be constructed directly.

Super class

distr6::Distribution -> Kernel

Public fields

package

Deprecated, use $packages instead.

packages

Packages required to be installed in order to construct the distribution.

Methods

Public methods

• Kernel$new()

• Kernel$mode()

• Kernel$mean()

• Kernel$median()

• Kernel$pdfSquared2Norm()

• Kernel$cdfSquared2Norm()

• Kernel$skewness()

• Kernel$clone()

Inherited methods

• 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()

---

Method new()

Creates a new instance of this R6 class.

Usage

Kernel$new(decorators = NULL, support = Interval$new(-1, 1))

Arguments

decorators

(character())
Decorators to add to the distribution during construction.

support

[set6::Set]
Support of the distribution.

---

Method mode()

Calculates the mode of the distribution.

Usage

Kernel$mode(which = "all")

Arguments

which

(character(1) | numeric(1)
Ignored if distribution is unimodal. Otherwise "all" returns all modes, otherwise specifies which mode to return.

---

Method mean()

Calculates the mean (expectation) of the distribution.

Usage

Kernel$mean(...)

Arguments

...

Unused.

---

Method median()

Calculates the median of the distribution.

Usage

Kernel$median()

---

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

Kernel$pdfSquared2Norm(x = 0, upper = Inf)

Arguments

x

(numeric(1))
Amount to shift the result.

upper

(numeric(1))
Upper limit of the integral.

---

Method cdfSquared2Norm()

The squared 2-norm of the cdf 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

Kernel$cdfSquared2Norm(x = 0, upper = Inf)

Arguments

x

(numeric(1))
Amount to shift the result.

upper

(numeric(1))
Upper limit of the integral.

---

Method skewness()

The skewness of a distribution is defined by the third standardised moment, $$sk_X = E_X[\frac{x - \mu}{\sigma}^3]$$ where \(E_X\) is the expectation of distribution X, \(\mu\) is the mean of the distribution and \(\sigma\) is the standard deviation of the distribution.

Usage

Kernel$skewness(...)

Arguments

...

Unused.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Kernel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.