sample_rho_G_mh

Proposes <code>rho* = tanh(atanh(rho) + tau * N(0,1))</code>; under a uniform
<code>rho ~ U(-1, 1)</code> prior the proposal is symmetric in the Fisher-z
scale and the Jacobian is <code>log(1 - rho^2)</code>. The MH ratio
compares the AR(1) prior density of <code>vec(G)_path</code> at the
current vs proposed <code>rho</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

sample_rho_G_mh function

<dl><dt>rho_G</dt>
<dd>current AR(1) coefficient</dd>
<dt>vecG_path</dt>
<dd>p x N matrix of FFBS-sampled states</dd>
<dt>sigma_G2</dt>
<dd>state innovation variance</dd>
<dt>tau</dt>
<dd>RW proposal SD on the Fisher-z scale</dd></dl>

Arguments

Logit-scale MH update on the AR(1) coefficient rho_G — sample_rho_G_mh

<dl>

<dt>rho_G</dt>
<dd>current AR(1) coefficient</dd>


<dt>vecG_path</dt>
<dd>p x N matrix of FFBS-sampled states</dd>


<dt>sigma_G2</dt>
<dd>state innovation variance</dd>


<dt>tau</dt>
<dd>RW proposal SD on the Fisher-z scale</dd>

</dl>

sample_rho_G_mh: Logit-scale MH update on the AR(1) coefficient rho_G

Description

Usage

Value

Arguments