arma_mv

Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) <doi:10.48550/arXiv.2305.09467>) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) and group-based OSCAR models (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) for computational speed-up.

Fabio Feser

Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control

arma_mv function

<dl><dt>m</dt>
<dd>numeric matrix</dd>
<dt>v</dt>
<dd>numeric vector</dd></dl>

Arguments

Matrix Product in RcppArmadillo. — arma_mv

<dl>

<dt>m</dt>
<dd>numeric matrix</dd>


<dt>v</dt>
<dd>numeric vector</dd>

</dl>

arma_mv: Matrix Product in RcppArmadillo.

Description

Usage

Value

Arguments