sample_sigma_ab_cpp

Sample innovation variance for dynamic additive effects

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

sample_sigma_ab_cpp function

<dl><dt>a_mat</dt>
<dd>Matrix of row effects (n x T)</dd>
<dt>b_mat</dt>
<dd>Matrix of column effects (n x T)</dd>
<dt>rho_ab</dt>
<dd>AR(1) parameter</dd>
<dt>symmetric</dt>
<dd>Whether the network is symmetric</dd>
<dt>prior_shape</dt>
<dd>Shape parameter for inverse gamma prior</dd>
<dt>prior_scale</dt>
<dd>Scale parameter for inverse gamma prior</dd></dl>

Arguments

Sample innovation variance for dynamic additive effects — sample_sigma_ab_cpp

<dl>

<dt>a_mat</dt>
<dd>Matrix of row effects (n x T)</dd>


<dt>b_mat</dt>
<dd>Matrix of column effects (n x T)</dd>


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


<dt>symmetric</dt>
<dd>Whether the network is symmetric</dd>


<dt>prior_shape</dt>
<dd>Shape parameter for inverse gamma prior</dd>


<dt>prior_scale</dt>
<dd>Scale parameter for inverse gamma prior</dd>

</dl>

sample_sigma_ab_cpp: Sample innovation variance for dynamic additive effects

Description

Usage

Value

Arguments