ffbs_vecG

One Carter-Kohn sweep over the per-period observations of the form
<code>vec(E_t) = H_t g_t + eps_t</code>, with state transition
<code>g_t = rho * g_{t-1} + eta_t</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

ffbs_vecG function

<dl><dt>E_cube</dt>
<dd>nA x nB x T residual cube (Z minus base - a - b - UV')</dd>
<dt>U_cube</dt>
<dd>nA x RA x T latent row factor cube</dd>
<dt>V_cube</dt>
<dd>nB x RB x T latent column factor cube</dd>
<dt>s2</dt>
<dd>scalar observation variance</dd>
<dt>rho_G</dt>
<dd>AR(1) coefficient in <code>(-1, 1)</code> (use <code>rho = 1</code> for
the RW1 limit; the forward variance is clamped via a tiny floor).</dd>
<dt>sigma_G2</dt>
<dd>state innovation variance</dd></dl>

Arguments

Forward-filter / backward-sample for vec(G_t) under AR(1) state prior — ffbs_vecG

<dl>

<dt>E_cube</dt>
<dd>nA x nB x T residual cube (Z minus base - a - b - UV')</dd>


<dt>U_cube</dt>
<dd>nA x RA x T latent row factor cube</dd>


<dt>V_cube</dt>
<dd>nB x RB x T latent column factor cube</dd>


<dt>s2</dt>
<dd>scalar observation variance</dd>


<dt>rho_G</dt>
<dd>AR(1) coefficient in <code>(-1, 1)</code> (use <code>rho = 1</code> for
the RW1 limit; the forward variance is clamped via a tiny floor).</dd>


<dt>sigma_G2</dt>
<dd>state innovation variance</dd>

</dl>

ffbs_vecG: Forward-filter / backward-sample for vec(G_t) under AR(1) state prior

Description

Usage

Value

Arguments