read_log_lik

For fits run with <code>save_log_lik = "chunked"</code>, the per-iteration
pointwise log-likelihood lives in per-column-chunk binary files instead
of <code>fit$log_lik</code>. This helper reconstitutes the
<code>[n_stored, n_obs]</code> matrix in memory by issuing one <code>readBin</code>
per chunk. <code>loo.lame()</code> / <code>waic.lame()</code> call this transparently.

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

read_log_lik function

<dl><dt>x</dt>
<dd>A fitted <code>ame</code>/<code>lame</code> object with
<code>$log_lik_chunks</code> (i.e. fit with <code>save_log_lik = "chunked"</code>).</dd></dl>

Arguments

Read the per-iteration log-lik matrix back from on-disk chunks — read_log_lik

<dl>

<dt>x</dt>
<dd>A fitted <code>ame</code>/<code>lame</code> object with
<code>$log_lik_chunks</code> (i.e. fit with <code>save_log_lik = "chunked"</code>).</dd>

</dl>

read_log_lik: Read the per-iteration log-lik matrix back from on-disk chunks

Description

Usage

Value

Arguments