rmvnorm

Simulates a matrix where the rows are i.i.d. samples from a multivariate
normal distribution

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

rmvnorm function

<dl><dt>n</dt>
<dd>sample size</dd>
<dt>mu</dt>
<dd>multivariate mean vector</dd>
<dt>Sigma</dt>
<dd>covariance matrix</dd>
<dt>Sigma.chol</dt>
<dd>Cholesky factorization of <code>Sigma</code></dd></dl>

Arguments

Cassy Dorff, Shahryar Minhas, Tosin Salau

Author

Simulation from a multivariate normal distribution — rmvnorm

<dl>

<dt>n</dt>
<dd>sample size</dd>


<dt>mu</dt>
<dd>multivariate mean vector</dd>


<dt>Sigma</dt>
<dd>covariance matrix</dd>


<dt>Sigma.chol</dt>
<dd>Cholesky factorization of <code>Sigma</code></dd>

</dl>

rmvnorm: Simulation from a multivariate normal distribution

Description

Usage

Value

Arguments

Author