waic

Lightweight S3 generics so calls of the form <code>loo(fit)</code> dispatch
through R's S3 system even when the loo package is not loaded.
When loo is loaded, its generic resolves first; this
fallback only fires for bare-namespace use.

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

loo function

<dl><dt>x</dt>
<dd>a fitted <code>ame</code> or <code>lame</code> object with <code>$log_lik</code>.</dd>
<dt>...</dt>
<dd>passed to the relevant method.</dd></dl>

Arguments

Generic dispatcher for loo / waic on ame / lame fits — loo

<dl>

<dt>x</dt>
<dd>a fitted <code>ame</code> or <code>lame</code> object with <code>$log_lik</code>.</dd>


<dt>...</dt>
<dd>passed to the relevant method.</dd>

</dl>

loo: Generic dispatcher for loo / waic on ame / lame fits

Description

Usage

Value

Arguments