simY_nrm

Draws a Gaussian sociomatrix whose entries have expectation <code>EY</code>,
dyadic variance <code>s2</code> and within-dyad correlation <code>rho</code>.

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

simY_nrm function

<dl><dt>EY</dt>
<dd>square matrix giving the expected value of the relational matrix</dd>
<dt>rho</dt>
<dd>dyadic correlation</dd>
<dt>s2</dt>
<dd>dyadic variance</dd></dl>

Arguments

Author

Simulate a normal relational matrix — simY_nrm

<dl>

<dt>EY</dt>
<dd>square matrix giving the expected value of the relational matrix</dd>


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


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

</dl>

simY_nrm: Simulate a normal relational matrix

Description

Usage

Value

Arguments

Author