compute_XtX_Xty_bip_cpp

Replaces the O(T x p^2 x n^2) nested R loop for bipartite XtX and Xty
computation with a single C++ call.

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

compute_XtX_Xty_bip_cpp function

<dl><dt>Xlist</dt>
<dd>List of T arrays, each nA x nB x p</dd>
<dt>resid</dt>
<dd>3D array of residuals nA x nB x T</dd>
<dt>p</dt>
<dd>Number of covariates</dd></dl>

Arguments

Compute X'X and X'y for bipartite covariate regression — compute_XtX_Xty_bip_cpp

<dl>

<dt>Xlist</dt>
<dd>List of T arrays, each nA x nB x p</dd>


<dt>resid</dt>
<dd>3D array of residuals nA x nB x T</dd>


<dt>p</dt>
<dd>Number of covariates</dd>

</dl>

compute_XtX_Xty_bip_cpp: Compute X'X and X'y for bipartite covariate regression

Description

Usage

Value

Arguments