Implementation of the CVE (Conditional Variance Estimation) method
proposed by Fertl, L. and Bura, E. (2021) <arXiv:2102.08782> and the ECVE
(Ensemble Conditional Variance Estimation) method introduced in
Fertl, L. and Bura, E. (2021) <arXiv:2102.13435>.
CVE and ECVE are sufficient dimension reduction methods
in regressions with continuous response and predictors. CVE applies to general
additive error regression models while ECVE generalizes to non-additive error
regression models. They operate under the assumption that the predictors can
be replaced by a lower dimensional projection without loss of information.
It is a semiparametric forward regression model based exhaustive sufficient
dimension reduction estimation method that is shown to be consistent under mild
assumptions.

Conditional Variance Estimator for Sufficient Dimension
Reduction

package-ubuntu-20.04-r-4.0.5#cran#CVarE-1.1

Random generation for generalized Normal Distribution.

rgnorm(n = 1, mu = 0, alpha = 1, beta = 1)

<table summary="R argblock">
<tr valign="top">
<td><code>n</code></td>
<td>
Number of generated samples.
</td>
</tr>
<tr valign="top">
<td><code>mu</code></td>
<td>
mean.
</td>
</tr>
<tr valign="top">
<td><code>alpha</code></td>
<td>
first shape parameter.
</td>
</tr>
<tr valign="top">
<td><code>beta</code></td>
<td>
second shape parameter.
</td>
</tr>
</table>

numeric array of length n.

package-ubuntu-20.04-r-4.0.5#cran#CVarE-1.1#docs#rgnorm

rgnorm

Description

Usage

Arguments

Value

CVarE

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`n`	Number of generated samples.
`mu`	mean.
`alpha`	first shape parameter.
`beta`	second shape parameter.