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

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

`dp` |
a numeric vector (in the univariate case, for class |

`family` |
a character string which identifies the parametric
family among those admissible for classes |

`object` |
an object of class |

a numeric vector

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.

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")
``` |

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