confint.ame_als

Wald confidence intervals for the intercept and dyadic-covariate
coefficients of an <code>ame_als</code> fit, built from the conditional
sandwich covariance (<code>vcov.ame_als</code>).

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

confint.ame_als function

<dl><dt>object</dt>
<dd>an <code>ame_als</code> fit.</dd>
<dt>parm</dt>
<dd>character vector of parameter names, or integer indices; if
<code>NULL</code> (default) all available coefficients are returned.</dd>
<dt>level</dt>
<dd>confidence level (default 0.95).</dd>
<dt>...</dt>
<dd>passed to <code>vcov.ame_als</code> (e.g. <code>cluster</code>).</dd></dl>

Arguments

Confidence intervals for a fast AME fit — confint.ame_als

<dl>

<dt>object</dt>
<dd>an <code>ame_als</code> fit.</dd>


<dt>parm</dt>
<dd>character vector of parameter names, or integer indices; if
<code>NULL</code> (default) all available coefficients are returned.</dd>


<dt>level</dt>
<dd>confidence level (default 0.95).</dd>


<dt>...</dt>
<dd>passed to <code>vcov.ame_als</code> (e.g. <code>cluster</code>).</dd>

</dl>

confint.ame_als: Confidence intervals for a fast AME fit

Description

Usage

Value

Arguments

Details

See Also