print.als_dynamic_beta

Print method for penalised ALS time-varying beta

Additive and multiplicative effects models for both cross-sectional and longitudinal network analysis. The package provides two main functions: ame() for cross-sectional networks and lame() for longitudinal networks. It supports square and rectangular network structures. Key features include: (1) Cross-sectional network analysis via ame() with support for binary, continuous, ordinal, and count data; (2) Longitudinal network analysis via lame() with additive sender/receiver and multiplicative latent-factor effects that can evolve over time through AR(1) processes (Sewell and Chen (2015) <doi:10.1080/01621459.2014.988214>; Durante and Dunson (2014) <doi:10.1093/biomet/asu040>); (3) Handling of changing actor compositions across time periods in longitudinal models; (4) Performance improvements through C++ implementations via 'Rcpp' and 'RcppArmadillo'.

Shahryar Minhas

lame

Longitudinal Additive and Multiplicative Effects Models for
Networks

Cassy Dorff

Tosin Salau

print.als_dynamic_beta function

<dl><dt>x</dt>
<dd>An <code>als_dynamic_beta</code> result.</dd>
<dt>digits</dt>
<dd>Number of significant digits.</dd>
<dt>...</dt>
<dd>Ignored.</dd></dl>

Arguments

Print method for penalised ALS time-varying beta — print.als_dynamic_beta

<dl>

<dt>x</dt>
<dd>An <code>als_dynamic_beta</code> result.</dd>


<dt>digits</dt>
<dd>Number of significant digits.</dd>


<dt>...</dt>
<dd>Ignored.</dd>

</dl>

print.als_dynamic_beta: Print method for penalised ALS time-varying beta

Description

Usage

Value

Arguments