design_array

Stacks the row, column and dyadic covariates into a single \(n \times n
\times p\) array in which slice \(k\) holds the dyad-level values of the
\(k\)th predictor. A sender attribute is broadcast down the rows, a
receiver attribute is broadcast across the columns, and a dyadic covariate
is copied verbatim. An intercept slice of ones is prepended unless it is
switched off or would duplicate an already-constant predictor.

internal

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

design_array function

<dl><dt>Xrow</dt>
<dd>an n x pr matrix of row (sender) covariates</dd>
<dt>Xcol</dt>
<dd>an n x pc matrix of column (receiver) covariates</dd>
<dt>Xdyad</dt>
<dd>an n x n x pd array of dyadic covariates</dd>
<dt>intercept</dt>
<dd>logical; prepend a constant slice of ones</dd>
<dt>n</dt>
<dd>number of rows/columns</dd>
<dt>warn</dt>
<dd>logical; warn when missing covariate values are zero-filled
(default <code>TRUE</code>). Set <code>FALSE</code> when the caller has already
propagated the missingness into the response.</dd></dl>

Arguments

Author

Assemble the dyadic design socioarray for an AME model — design_array

<dl>

<dt>Xrow</dt>
<dd>an n x pr matrix of row (sender) covariates</dd>


<dt>Xcol</dt>
<dd>an n x pc matrix of column (receiver) covariates</dd>


<dt>Xdyad</dt>
<dd>an n x n x pd array of dyadic covariates</dd>


<dt>intercept</dt>
<dd>logical; prepend a constant slice of ones</dd>


<dt>n</dt>
<dd>number of rows/columns</dd>


<dt>warn</dt>
<dd>logical; warn when missing covariate values are zero-filled
(default <code>TRUE</code>). Set <code>FALSE</code> when the caller has already
propagated the missingness into the response.</dd>

</dl>

design_array: Assemble the dyadic design socioarray for an AME model

Description

Usage

Value

Arguments

Author