mscale_algorithm_options

Options for the M-scale Estimation Algorithm

Robust penalized (adaptive) elastic net S and M estimators for
linear regression. The adaptive methods are proposed in
Kepplinger, D. (2023) <doi:10.1016/j.csda.2023.107730> and the
non-adaptive methods in
Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E.
(2019) <doi:10.1214/19-AOAS1269>.
The package implements robust hyper-parameter selection with robust
information sharing cross-validation according to Kepplinger & Wei (2025)
<doi:10.1080/00401706.2025.2540970>.

David Kepplinger

pense

Penalized Elastic Net S/MM-Estimator of Regression

Matías Salibián-Barrera

Gabriela Cohen Freue

mscale_algorithm_options function

<dl><dt>rho</dt>
<dd>the \(\rho\) function to use. See <code>rho_function()</code> for
possible values.</dd>
<dt>max_it</dt>
<dd>maximum number of iterations.</dd>
<dt>eps</dt>
<dd>numerical tolerance to check for convergence.</dd></dl>

Arguments

Options for the M-scale Estimation Algorithm — mscale_algorithm_options

<dl>

<dt>rho</dt>
<dd>the \(\rho\) function to use. See <code>rho_function()</code> for
possible values.</dd>


<dt>max_it</dt>
<dd>maximum number of iterations.</dd>


<dt>eps</dt>
<dd>numerical tolerance to check for convergence.</dd>

</dl>

mscale_algorithm_options: Options for the M-scale Estimation Algorithm

Description

Usage

Value

Arguments

See Also