rbeta_ab_rep_fc

Draws jointly from the full conditional distribution of the regression
coefficients <code>beta</code> together with the additive sender/receiver
effects <code>a</code> and <code>b</code>, pooling the information contained in the
replicate slices of a longitudinal design that share a common
<code>beta</code>, <code>a</code> and <code>b</code>. The draw is obtained by forming the
complete joint Gaussian posterior precision matrix over
<code>(beta, a, b)</code> from sufficient statistics accumulated across slices,
after whitening the within-dyad correlation, and sampling from it.

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

rbeta_ab_rep_fc function

<dl><dt>Z.T</dt>
<dd>n x n x T array of latent relations with multiplicative
effects already removed; the third margin indexes the replicates</dd>
<dt>Sab</dt>
<dd>2 x 2 covariance matrix of the additive row/column effects</dd>
<dt>rho</dt>
<dd>within-dyad (reciprocity) correlation</dd>
<dt>X.T</dt>
<dd>n x n x p x T design array</dd>
<dt>s2</dt>
<dd>dyadic variance</dd></dl>

Arguments

Author

Gibbs update of regression coefficients and additive effects for
replicated relational data — rbeta_ab_rep_fc

<dl>

<dt>Z.T</dt>
<dd>n x n x T array of latent relations with multiplicative
effects already removed; the third margin indexes the replicates</dd>


<dt>Sab</dt>
<dd>2 x 2 covariance matrix of the additive row/column effects</dd>


<dt>rho</dt>
<dd>within-dyad (reciprocity) correlation</dd>


<dt>X.T</dt>
<dd>n x n x p x T design array</dd>


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

</dl>

rbeta_ab_rep_fc: Gibbs update of regression coefficients and additive effects for replicated relational data

Description

Usage

Value

Arguments

Author