sample_beta_static_cpp

Conjugate Gaussian update for the static coefficient block, treating the
dynamic path as known. Uses a flat-ish ridge-style prior <code>prior_prec</code> on
the static beta (typically diag(1/g) to match the existing g-prior path).

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

sample_beta_static_cpp function

<dl><dt>Xdyn_list</dt>
<dd>T-length list of long-format dynamic design matrices.</dd>
<dt>Xstat_list</dt>
<dd>T-length list of long-format static design matrices.</dd>
<dt>Z_list</dt>
<dd>T-length list of latent (n x n) matrices.</dd>
<dt>offset_list</dt>
<dd>T-length list of (n x n) offsets.</dd>
<dt>beta_dyn_path</dt>
<dd>(T x p_dyn) dynamic path matrix.</dd>
<dt>prior_mean</dt>
<dd>Length p_static prior mean.</dd>
<dt>prior_prec</dt>
<dd>p_static x p_static prior precision.</dd>
<dt>s2</dt>
<dd>Dyadic variance.</dd>
<dt>dyad_rho</dt>
<dd>Dyadic correlation.</dd>
<dt>bipartite</dt>
<dd>TRUE/FALSE.</dd>
<dt>symmetric</dt>
<dd>TRUE/FALSE.</dd>
<dt>use_dyad_rho</dt>
<dd>TRUE/FALSE.</dd></dl>

Arguments

Sample the static-block beta conditional on the dynamic path — sample_beta_static_cpp

<dl>

<dt>Xdyn_list</dt>
<dd>T-length list of long-format dynamic design matrices.</dd>


<dt>Xstat_list</dt>
<dd>T-length list of long-format static design matrices.</dd>


<dt>Z_list</dt>
<dd>T-length list of latent (n x n) matrices.</dd>


<dt>offset_list</dt>
<dd>T-length list of (n x n) offsets.</dd>


<dt>beta_dyn_path</dt>
<dd>(T x p_dyn) dynamic path matrix.</dd>


<dt>prior_mean</dt>
<dd>Length p_static prior mean.</dd>


<dt>prior_prec</dt>
<dd>p_static x p_static prior precision.</dd>


<dt>s2</dt>
<dd>Dyadic variance.</dd>


<dt>dyad_rho</dt>
<dd>Dyadic correlation.</dd>


<dt>bipartite</dt>
<dd>TRUE/FALSE.</dd>


<dt>symmetric</dt>
<dd>TRUE/FALSE.</dd>


<dt>use_dyad_rho</dt>
<dd>TRUE/FALSE.</dd>

</dl>

sample_beta_static_cpp: Sample the static-block beta conditional on the dynamic path

Description

Usage

Value

Arguments