# modeSECdistr: The mode of a skew-elliptically contoured (SEC) distribution In sn: The Skew-Normal and Related Distributions, such as the Skew-t

## Description

Compute compute the mode of a univariate or multivariate SEC distribution.

## Usage

 `1` ``` modeSECdistr(dp, family, object=NULL) ```

## Arguments

 `dp` a numeric vector (in the univariate case, for class `SECdistrUv`) or a list (in the multivariate case, , for class `SECdistrUv`) of parameters which identify the specific distribution within the named `family`. `family` a character string which identifies the parametric family among those admissible for classes `SECdistrUv` or `SECdistrMv`. `object` an object of class `SECdistrUv` or `SECdistrMv` as created by `makeSECdistr` or `extractSECdistr.`

a numeric vector

## Background

The mode is obtained through numerical maximization. In the multivariate case, the problem is reduced to a one-dimensional search using Propositions 5.14 and 6.2 of the reference below.

## References

Azzalini, A. with the collaboration of Capitanio, A. (2014). The Skew-Normal and Related Families. Cambridge University Press, IMS Monographs series.

`makeSECdistr` and `extractSECdistr` for additional information and for constructing a suitable `object`,
`SECdistrUv-class` and `SECdistrMv-class` for methods `mean` and `vcov` which compute the mean (vector) and the variance (matrix) of the `object` distribution
 ```1 2 3 4 5 6``` ```dp3 <- list(xi=1:3, Omega=toeplitz(1/(1:3)), alpha=c(3,-1,2), nu=5) st3 <- makeSECdistr(dp3, family="ST", name="ST3", compNames=c("U", "V", "W")) A <- matrix(c(1,-1,1, 3,0,-2), 3, 2) new.st <- affineTransSECdistr(st3, a=c(-3,0), A=A) # st2 <- marginalSECdistr(st3, comp=c(3,1), name="2D marginal of ST3") ```