.exact_center_per_actor

<p>Given an unconstrained sample <code>theta_star</code> (n_actors x T) and
per-actor per-period posterior variances <code>V</code> (same shape),
projects to the sum-to-zero manifold per period using the
variance-weighted Lagrangian projection (exact under block-diagonal
covariance across actors).</p>

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

.exact_center_per_actor function

Exact variance-weighted sum-to-zero projection for the per-actor block — .exact_center_per_actor

Exact variance-weighted sum-to-zero projection for the per-actor block

.exact_center_per_actor: Exact variance-weighted sum-to-zero projection for the per-actor block

Description

Usage

Arguments