rs2_rep_fc

Samples the dyadic variance <code>s2</code> from its Gibbs full conditional when
the residual cube stacks several independent replicate networks that share a
common within-dyad correlation <code>rho</code>. Each ordered dyad pair is whitened
by the symmetric inverse square root of the 2x2 correlation matrix and the
pooled whitened sum of squares drives an inverse-gamma draw.

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

rs2_rep_fc function

<dl><dt>E.T</dt>
<dd>numeric array of dimension n x n x N holding the residual
matrices along the third margin (one square slice per replicate).</dd>
<dt>rho</dt>
<dd>current value of the within-dyad correlation.</dd></dl>

Arguments

Author

Full-conditional draw of the dyadic variance for replicated relational data — rs2_rep_fc

<dl>

<dt>E.T</dt>
<dd>numeric array of dimension n x n x N holding the residual
matrices along the third margin (one square slice per replicate).</dd>


<dt>rho</dt>
<dd>current value of the within-dyad correlation.</dd>

</dl>

rs2_rep_fc: Full-conditional draw of the dyadic variance for replicated relational data

Description

Usage

Value

Arguments

Author