simY_bin

Draws a latent Gaussian array with the given dyadic correlation and
thresholds it at zero to produce a binary (0/1) sociomatrix. Self-ties on
the diagonal of a square (unipartite) matrix are set to <code>NA</code>;
rectangular (bipartite) matrices are returned with every entry populated.

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_bin function

<dl><dt>EZ</dt>
<dd>matrix giving the expected value of the latent <code>Z</code> matrix</dd>
<dt>rho</dt>
<dd>dyadic correlation</dd></dl>

Arguments

Author

Simulate a binary relational matrix from latent means — simY_bin

<dl>

<dt>EZ</dt>
<dd>matrix giving the expected value of the latent <code>Z</code> matrix</dd>


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

</dl>

simY_bin: Simulate a binary relational matrix from latent means

Description

Usage

Value

Arguments

Author