el2sm

Construct a sociomatrix from an edgelist. Each row of <code>el</code> gives the
two endpoints of an edge and, optionally, a weight in a third column
(unweighted edges default to 1).

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

el2sm function

<dl><dt>el</dt>
<dd>a matrix in which each row contains the indices of an edge and possibly the weight for the edge</dd>
<dt>directed</dt>
<dd>if FALSE, then a relation is placed in both entry ij and ji of the sociomatrix, for each edge ij (or ji)</dd>
<dt>nadiag</dt>
<dd>put NAs on the diagonal</dd></dl>

Arguments

Author

Edgelist to sociomatrix — el2sm

<dl>

<dt>el</dt>
<dd>a matrix in which each row contains the indices of an edge and possibly the weight for the edge</dd>


<dt>directed</dt>
<dd>if FALSE, then a relation is placed in both entry ij and ji of the sociomatrix, for each edge ij (or ji)</dd>


<dt>nadiag</dt>
<dd>put NAs on the diagonal</dd>

</dl>

el2sm: Edgelist to sociomatrix

Description

Usage

Value

Arguments

Author

Examples