.sweep_per_actor_exact

One full sweep over actors: each actor's path is sampled from its
conditional AR(1) posterior via .actor_ffbs_path(), and the joint
sample is then projected to satisfy sum_i theta_i(t) = 0 per period
using the variance-weighted Lagrangian.

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

.sweep_per_actor_exact function

<dl><dt>H_mat</dt>
<dd>n_actors x T precision sufficient statistics</dd>
<dt>h_mat</dt>
<dd>n_actors x T cross-product sufficient statistics</dd>
<dt>rho_actor</dt>
<dd>AR(1) coefficient</dd>
<dt>sigma_actor2</dt>
<dd>AR(1) innovation variance</dd></dl>

Arguments

Per-actor sweep: unconstrained per-actor FFBS + exact projection — .sweep_per_actor_exact

<dl>

<dt>H_mat</dt>
<dd>n_actors x T precision sufficient statistics</dd>


<dt>h_mat</dt>
<dd>n_actors x T cross-product sufficient statistics</dd>


<dt>rho_actor</dt>
<dd>AR(1) coefficient</dd>


<dt>sigma_actor2</dt>
<dd>AR(1) innovation variance</dd>

</dl>

.sweep_per_actor_exact: Per-actor sweep: unconstrained per-actor FFBS + exact projection

Description

Usage

Value

Arguments