Longitudinal Additive and Multiplicative Effects Models for Networks

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) ; Durante and Dunson (2014) ); (3) Handling of changing actor compositions across time periods in longitudinal models; (4) Performance improvements through C++ implementations via 'Rcpp' and 'RcppArmadillo'.


Reference manual

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install.packages("lame")

1.3.4 by Shahryar Minhas, 2 months ago


https://netify-dev.github.io/lame/, https://github.com/netify-dev/lame


Report a bug at https://github.com/netify-dev/lame/issues


Browse source code at https://github.com/cran/lame


Authors: Cassy Dorff [aut] , Shahryar Minhas [aut, cre] , Tosin Salau [aut]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports Rcpp, ggplot2, ggrepel, ggforce, gridExtra, coda, patchwork, cli, MASS, Matrix, abind, netify, graphics, grDevices, parallel, stats, utils

Suggests knitr, rmarkdown, testthat, igraph, network, posterior, loo, digest, tibble, broom, generics, pROC, precrec, statmod, dplyr, modelsummary, callr, amen

Linking to Rcpp, RcppArmadillo


See at CRAN