rZ_ord_sym_explicit_fc

Mirror image of <code>rZ_ord_sym_fc</code> (variance 1 on the upper
triangle, mirrored to the lower) parameterised by an explicit
<code>alpha</code> vector instead of data-induced cutpoints.

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

rZ_ord_sym_explicit_fc function

<dl><dt>Z</dt>
<dd>current symmetric latent matrix</dd>
<dt>EZ</dt>
<dd>expected value of Z</dd>
<dt>Y_int</dt>
<dd>integer ordinal Y (symmetric)</dd>
<dt>alpha</dt>
<dd>length-(K-1) cutpoints with <code>alpha[1] = 0</code></dd></dl>

Arguments

Symmetric ordinal Z sample given explicit cutpoints (R &gt;= 0 safe) — rZ_ord_sym_explicit_fc

<dl>

<dt>Z</dt>
<dd>current symmetric latent matrix</dd>


<dt>EZ</dt>
<dd>expected value of Z</dd>


<dt>Y_int</dt>
<dd>integer ordinal Y (symmetric)</dd>


<dt>alpha</dt>
<dd>length-(K-1) cutpoints with <code>alpha[1] = 0</code></dd>

</dl>

rZ_ord_sym_explicit_fc: Symmetric ordinal Z sample given explicit cutpoints (R >= 0 safe)

Description

Usage

Value

Arguments