matrix.eager_cov

Creates an eager covariance matrix.

Extremely efficient procedures for fitting the entire group lasso and group elastic net regularization path for GLMs, multinomial, the Cox model and multi-task Gaussian models. Similar to the R package 'glmnet' in scope of models, and in computational speed. This package provides R bindings to the C++ code underlying the corresponding Python package 'adelie'. These bindings offer a general purpose group elastic net solver,
a wide range of matrix classes that can exploit special structure
to allow large-scale inputs, and an assortment of
generalized linear model classes for fitting various types of data.
The package is an implementation of Yang, J. and Hastie, T. (2024) <doi:10.48550/arXiv.2405.08631>.

Trevor Hastie

adelie

Group Lasso and Elastic Net Solver for Generalized Linear Models

James Yang

Balasubramanian Narasimhan

matrix.eager_cov function

<dl><dt>mat</dt>
<dd>A dense matrix to be used with the <code>gaussian_cov()</code> solver.</dd>
<dt>n_threads</dt>
<dd>Number of threads.</dd></dl>

Arguments

Creates an eager covariance matrix. — matrix.eager_cov

<dl>

<dt>mat</dt>
<dd>A dense matrix to be used with the <code>gaussian_cov()</code> solver.</dd>


<dt>n_threads</dt>
<dd>Number of threads.</dd>

</dl>

matrix.eager_cov: Creates an eager covariance matrix.

Description

Usage

Value

Arguments

Examples