hypvoltmvnorm

Approximate the hypervolume of a multivariate dataset by assuming
a truncated multivariate normal (TMVN) distribution within given radius.

Extend the functionality of the 'mclust' package for Gaussian
finite mixture modeling by including: density estimation for data with
bounded support (Scrucca, 2019 <doi:10.1002/bimj.201800174>); modal
clustering using MEM (Modal EM) algorithm for Gaussian mixtures
(Scrucca, 2021 <doi:10.1002/sam.11527>); entropy estimation via
Gaussian mixture modeling (Robin & Scrucca, 2023
<doi:10.1016/j.csda.2022.107582>); Gaussian mixtures modeling of
financial log-returns (Scrucca, 2024 <doi:10.3390/e26110907>).

Luca Scrucca

mclustAddons

Addons for the 'mclust' Package

hypvoltmvnorm function

<dl><dt>data</dt>
<dd>A numeric vector, matrix, or data frame. If a matrix or
data frame, rows correspond to observations and columns correspond
to variables. Categorical variables and missing values are not allowed.</dd>
<dt>radius</dt>
<dd>A numerical value specifying the radius to be used for
truncating the multivariate Gaussian distribution. If not provided
is set to \(sqrt(d+1)\), where \(d\) is the dimensionality of
the data, i.e. number of columns of <code>data</code>.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Author

Approximate hypervolume for multivariate data — hypvoltmvnorm

<dl>

<dt>data</dt>
<dd>A numeric vector, matrix, or data frame. If a matrix or
data frame, rows correspond to observations and columns correspond
to variables. Categorical variables and missing values are not allowed.</dd>


<dt>radius</dt>
<dd>A numerical value specifying the radius to be used for
truncating the multivariate Gaussian distribution. If not provided
is set to \(sqrt(d+1)\), where \(d\) is the dimensionality of
the data, i.e. number of columns of <code>data</code>.</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd>

</dl>

hypvoltmvnorm: Approximate hypervolume for multivariate data

Description

Usage

Value

Arguments

Author

Examples