posterior_options

Options for saving posterior samples during MCMC

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

posterior_options function

<dl><dt>save_UV</dt>
<dd>Logical; whether to save samples of U and V matrices (default FALSE)</dd>
<dt>save_ab</dt>
<dd>Logical; whether to save samples of additive effects a and b (default FALSE)</dd>
<dt>thin_UV</dt>
<dd>Integer; thinning interval for U/V samples (default 10)</dd>
<dt>thin_ab</dt>
<dd>Integer; thinning interval for a/b samples (default 10)</dd></dl>

Arguments

Cassy Dorff, Shahryar Minhas, Tosin Salau

Author

Options for saving posterior samples during MCMC — posterior_options

<dl>

<dt>save_UV</dt>
<dd>Logical; whether to save samples of U and V matrices (default FALSE)</dd>


<dt>save_ab</dt>
<dd>Logical; whether to save samples of additive effects a and b (default FALSE)</dd>


<dt>thin_UV</dt>
<dd>Integer; thinning interval for U/V samples (default 10)</dd>


<dt>thin_ab</dt>
<dd>Integer; thinning interval for a/b samples (default 10)</dd>

</dl>

posterior_options: Options for saving posterior samples during MCMC

Description

Usage

Value

Arguments

Author