compute_mcmc_diagnostics

Computes Gelman-Rubin R-hat and effective sample size (ESS) across two or
more independently-seeded chains, for both the regression coefficients
(<code>BETA</code>) and the variance components (<code>VC</code>). ESS is computed on
the pooled draws, so genuinely non-converged chains report a low ESS rather
than an inflated one.

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

compute_mcmc_diagnostics function

<dl><dt>chain_list</dt>
<dd>a list of fitted <code>ame</code>/<code>lame</code> objects, one per
chain (each must carry a <code>BETA</code> matrix).</dd></dl>

Arguments

Cassy Dorff, Shahryar Minhas, Tosin Salau

Author

Compute MCMC convergence diagnostics for multiple chains — compute_mcmc_diagnostics

<dl>

<dt>chain_list</dt>
<dd>a list of fitted <code>ame</code>/<code>lame</code> objects, one per
chain (each must carry a <code>BETA</code> matrix).</dd>

</dl>

compute_mcmc_diagnostics: Compute MCMC convergence diagnostics for multiple chains

Description

Usage

Value

Arguments

Author