https://msuconflictworkshop.r-universe.devRecent package updates in msuconflictworkshopR-universehttps://github.com/msuconflictworkshop.pnghttps://msuconflictworkshop.r-universe.devWed, 21 Oct 2026 20:36:56 GMTminhassh@msu.edu (Shahryar Minhas)Builds, validates, analyzes, and visualizes network data from dyadic, event, matrix, 'igraph', and 'network' inputs. Supports cross-sectional, longitudinal, bipartite, and multi-layer networks, with conversion helpers for common modeling workflows and plotting utilities for exploratory analysis. Network methods are described in Wasserman and Faust (1994, ISBN:9780521387071), Cranmer et al. (2021) <doi:10.1017/9781316662915>, and Minhas et al. (2022) <doi:10.1017/psrm.2021.56>.https://github.com/r-universe/netify-dev/actions/runs/39673076114Wed, 21 Oct 2026 20:36:56 GMTnetify1.5.4successhttps://netify-dev.r-universe.devhttps://github.com/netify-dev/netifyresources.Rmdresources.htmlHelpful Resources2025-05-21 02:15:532026-06-05 00:00:57internals.Rmdinternals.htmlInternals: object structure & writing custom extensions2026-05-27 21:41:012026-06-05 00:00:57pipeline_netify_ergm.Rmdpipeline_netify_ergm.htmlPipeline: netify to ergm (statnet)2026-05-27 21:41:012026-06-05 00:00:57quickstart_inference.Rmdquickstart_inference.htmlQuickstart to Inference2026-06-05 00:00:572026-06-05 00:00:57minhassh@msu.edu (Shahryar Minhas)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'.https://github.com/r-universe/netify-dev/actions/runs/39655540612Tue, 08 Sep 2026 19:06:44 GMTlame1.3.5successhttps://netify-dev.r-universe.devhttps://github.com/netify-dev/lamebipartite.Rmdbipartite.htmlBipartite Network Analysis2025-08-12 00:36:282026-07-20 11:55:37dynamic_effects.Rmddynamic_effects.htmlDynamic Effects in Longitudinal AME Models2025-08-11 13:22:492026-07-20 11:55:37fast_estimation.Rmdfast_estimation.htmlFast (MCMC-free) AME Estimation2026-05-28 21:45:062026-07-20 11:55:37forecasting.Rmdforecasting.htmlForecasting Longitudinal Networks with lame2026-05-28 21:45:062026-07-20 11:55:37lame.Rmdlame.htmlGetting Started with lame2025-08-12 00:36:282026-07-20 11:55:37lame-overview.Rmdlame-overview.htmllame Overview2025-06-10 00:29:372026-07-21 11:18:45cross_sec_ame.Rmdcross_sec_ame.htmlYour First AME Model2025-08-27 01:35:162026-07-21 11:18:45