rZ_ord_bip_fc

Rectangular analogue of <code>rZ_ord_fc</code>. Cutpoints are
data-induced (the boundary between rank <code>w</code> and rank <code>w+1</code>
is the maximum Z in rank <code>w</code> and minimum Z in rank <code>w+1</code>),
matching the existing unipartite convention. With rho = 0 in
bipartite, every cell at rank <code>w</code> is drawn independently from
a truncated normal centered at <code>EZ</code>.

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_bip_fc function

<dl><dt>Z</dt>
<dd>current rectangular latent matrix (nA x nB).</dd>
<dt>EZ</dt>
<dd>expected value (nA x nB).</dd>
<dt>Y</dt>
<dd>observed ordinal matrix (nA x nB).</dd></dl>

Arguments

Cassy Dorff, Shahryar Minhas, Tosin Salau

Author

Sample Z under bipartite ordinal data (rectangular) — rZ_ord_bip_fc

<dl>

<dt>Z</dt>
<dd>current rectangular latent matrix (nA x nB).</dd>


<dt>EZ</dt>
<dd>expected value (nA x nB).</dd>


<dt>Y</dt>
<dd>observed ordinal matrix (nA x nB).</dd>

</dl>

rZ_ord_bip_fc: Sample Z under bipartite ordinal data (rectangular)

Description

Usage

Value

Arguments

Author