sample_rho_ab_cpp

Sample AR(1) parameter 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_rho_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>sigma_ab</dt>
<dd>Innovation standard deviation</dd>
<dt>rho_current</dt>
<dd>Current value of rho</dd>
<dt>symmetric</dt>
<dd>Whether the network is symmetric</dd>
<dt>prior_mean</dt>
<dd>Prior mean for rho. Used only when <code>prior_sd &gt; 0</code>.</dd>
<dt>prior_sd</dt>
<dd>Prior SD for rho. <code>prior_sd &lt; 0</code> (the default)
selects a Jeffreys-like flat prior; a positive value switches to a
truncated Normal(prior_mean, prior_sd^2) prior.</dd></dl>

Arguments

Sample AR(1) parameter for dynamic additive effects — sample_rho_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>sigma_ab</dt>
<dd>Innovation standard deviation</dd>


<dt>rho_current</dt>
<dd>Current value of rho</dd>


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


<dt>prior_mean</dt>
<dd>Prior mean for rho. Used only when <code>prior_sd &gt; 0</code>.</dd>


<dt>prior_sd</dt>
<dd>Prior SD for rho. <code>prior_sd &lt; 0</code> (the default)
selects a Jeffreys-like flat prior; a positive value switches to a
truncated Normal(prior_mean, prior_sd^2) prior.</dd>

</dl>

sample_rho_ab_cpp: Sample AR(1) parameter for dynamic additive effects

Description

Usage

Value

Arguments