tidy.boot_ame

<code>ame_als_bootstrap()</code> returns an object of class <code>"boot_ame"</code>
(not <code>"ame_als"</code>); this tidy method exposes the bootstrap
estimates as a broom-style data frame so the standalone object
composes with modelsummary the same way an embedded
<code>ame_als(..., bootstrap = N)</code> fit does.

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

tidy.boot_ame function

<dl><dt>x</dt>
<dd>A <code>boot_ame</code> object.</dd>
<dt>conf.level</dt>
<dd>Confidence level. Default <code>0.95</code>.</dd>
<dt>...</dt>
<dd>Ignored.</dd></dl>

Arguments

Tidy method for a standalone bootstrap object (boot_ame) — tidy.boot_ame

<dl>

<dt>x</dt>
<dd>A <code>boot_ame</code> object.</dd>


<dt>conf.level</dt>
<dd>Confidence level. Default <code>0.95</code>.</dd>


<dt>...</dt>
<dd>Ignored.</dd>

</dl>

tidy.boot_ame: Tidy method for a standalone bootstrap object (`boot_ame`)

Description

Usage

Value

Arguments