rZ_nrm_batch_cpp

Replaces the per-time-period R loop with a single C++ call that loops
internally, reducing R-to-C++ transition overhead.

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

rZ_nrm_batch_cpp function

<dl><dt>Z</dt>
<dd>3D array of latent values (n x n x T)</dd>
<dt>EZ</dt>
<dd>3D array of expected values (n x n x T)</dd>
<dt>rho</dt>
<dd>Dyadic correlation parameter</dd>
<dt>s2</dt>
<dd>Dyadic variance</dd>
<dt>Y</dt>
<dd>3D array of observed values (n x n x T)</dd></dl>

Arguments

Batch normal Z sampling across all time periods — rZ_nrm_batch_cpp

<dl>

<dt>Z</dt>
<dd>3D array of latent values (n x n x T)</dd>


<dt>EZ</dt>
<dd>3D array of expected values (n x n x T)</dd>


<dt>rho</dt>
<dd>Dyadic correlation parameter</dd>


<dt>s2</dt>
<dd>Dyadic variance</dd>


<dt>Y</dt>
<dd>3D array of observed values (n x n x T)</dd>

</dl>

rZ_nrm_batch_cpp: Batch normal Z sampling across all time periods

Description

Usage

Value

Arguments