init_dynamic_ab_cpp

Initialize dynamic additive effects with AR(1) structure

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

init_dynamic_ab_cpp function

<dl><dt>n</dt>
<dd>Number of actors</dd>
<dt>Tn</dt>
<dd>Number of time points</dd>
<dt>rho_ab</dt>
<dd>AR(1) parameter</dd>
<dt>sigma_ab</dt>
<dd>Innovation standard deviation</dd>
<dt>mean_a</dt>
<dd>Mean for row effects</dd>
<dt>mean_b</dt>
<dd>Mean for column effects</dd></dl>

Arguments

Initialize dynamic additive effects with AR(1) structure — init_dynamic_ab_cpp

<dl>

<dt>n</dt>
<dd>Number of actors</dd>


<dt>Tn</dt>
<dd>Number of time points</dd>


<dt>rho_ab</dt>
<dd>AR(1) parameter</dd>


<dt>sigma_ab</dt>
<dd>Innovation standard deviation</dd>


<dt>mean_a</dt>
<dd>Mean for row effects</dd>


<dt>mean_b</dt>
<dd>Mean for column effects</dd>

</dl>

init_dynamic_ab_cpp: Initialize dynamic additive effects with AR(1) structure

Description

Usage

Value

Arguments