simZ

Draws a latent normal array from the social relations model: each
directed cell has mean <code>EZ</code> and marginal variance <code>s2</code>, and
the two cells of a dyad share within-dyad correlation <code>rho</code>.
Rectangular (bipartite) inputs carry no within-dyad correlation and are
drawn independently.

internal

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

simZ function

<dl><dt>EZ</dt>
<dd>expected value of Z</dd>
<dt>rho</dt>
<dd>dyadic correlation</dd>
<dt>s2</dt>
<dd>dyadic variance</dd></dl>

Arguments

Author

Simulate a latent relational Gaussian array — simZ

<dl>

<dt>EZ</dt>
<dd>expected value of Z</dd>


<dt>rho</dt>
<dd>dyadic correlation</dd>


<dt>s2</dt>
<dd>dyadic variance</dd>

</dl>

simZ: Simulate a latent relational Gaussian array

Description

Usage

Value

Arguments

Author