rUV_dynamic_t_fc_cpp

Like rUV_dynamic_fc_cpp but each AR(1) innovation is Student-t rather than
Gaussian, via a scale-mixture: the innovation for <code>u[t, i]</code> has variance
<code>sigma^2 / lambda[t, i]</code> with <code>lambda[t, i]</code> distributed as
Gamma(nu/2, nu/2). Provides a
continuous heavy-tailed alternative to the discrete snap-shift model.

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

rUV_dynamic_t_fc_cpp function

<dl><dt>U_current</dt>
<dd>Current 3D array of U positions (n x R x T)</dd>
<dt>V_current</dt>
<dd>Current 3D array of V positions (n x R x T)</dd>
<dt>ET</dt>
<dd>3D array of residuals (n x n x T)</dd>
<dt>rho_uv</dt>
<dd>AR(1) autoregressive parameter</dd>
<dt>sigma_uv</dt>
<dd>Innovation scale</dd>
<dt>s2</dt>
<dd>Dyadic variance</dd>
<dt>nu</dt>
<dd>Student-t degrees of freedom</dd>
<dt>lambda_u</dt>
<dd>Current local scales for U (n x T)</dd>
<dt>lambda_v</dt>
<dd>Current local scales for V (n x T)</dd>
<dt>shrink</dt>
<dd>Whether to apply shrinkage</dd>
<dt>symmetric</dt>
<dd>Whether network is symmetric</dd></dl>

Arguments

Update dynamic latent positions with heavy-tailed (Student-t) AR(1) innovations — rUV_dynamic_t_fc_cpp

<dl>

<dt>U_current</dt>
<dd>Current 3D array of U positions (n x R x T)</dd>


<dt>V_current</dt>
<dd>Current 3D array of V positions (n x R x T)</dd>


<dt>ET</dt>
<dd>3D array of residuals (n x n x T)</dd>


<dt>rho_uv</dt>
<dd>AR(1) autoregressive parameter</dd>


<dt>sigma_uv</dt>
<dd>Innovation scale</dd>


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


<dt>nu</dt>
<dd>Student-t degrees of freedom</dd>


<dt>lambda_u</dt>
<dd>Current local scales for U (n x T)</dd>


<dt>lambda_v</dt>
<dd>Current local scales for V (n x T)</dd>


<dt>shrink</dt>
<dd>Whether to apply shrinkage</dd>


<dt>symmetric</dt>
<dd>Whether network is symmetric</dd>

</dl>

rUV_dynamic_t_fc_cpp: Update dynamic latent positions with heavy-tailed (Student-t) AR(1) innovations

Description

Usage

Value

Arguments