{ "_id": "6ac7843ede46216d5edd15d7", "Package": "lame", "Title": "Longitudinal Additive and Multiplicative Effects Models for\nNetworks", "Version": "1.3.5", "Authors@R": "c(\nperson(\"Cassy\", \"Dorff\", role = \"aut\"),\nperson(\"Shahryar\", \"Minhas\", , \"minhassh@msu.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Tosin\", \"Salau\", role = \"aut\"))", "Description": "Additive and multiplicative effects models for both\ncross-sectional and longitudinal network analysis. The package\nprovides two main functions: ame() for cross-sectional networks\nand lame() for longitudinal networks. It supports square and\nrectangular network structures. Key features include: (1)\nCross-sectional network analysis via ame() with support for\nbinary, continuous, ordinal, and count data; (2) Longitudinal\nnetwork analysis via lame() with additive sender/receiver and\nmultiplicative latent-factor effects that can evolve over time\nthrough AR(1) processes (Sewell and Chen (2015)\n; Durante and Dunson (2014)\n); (3) Handling of changing actor\ncompositions across time periods in longitudinal models; (4)\nPerformance improvements through C++ implementations via 'Rcpp'\nand 'RcppArmadillo'.", "Author": "Cassy Dorff [aut], Shahryar Minhas [aut, cre], Tosin Salau\n[aut]", "Maintainer": "Shahryar Minhas ", "License": "MIT + file LICENSE", "URL": "https://netify-dev.github.io/lame/,\nhttps://github.com/netify-dev/lame", "BugReports": "https://github.com/netify-dev/lame/issues", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", 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output for memory efficiency", "topics": [ "compact_ame" ] }, { "page": "compute_mcmc_diagnostics", "title": "Compute MCMC convergence diagnostics for multiple chains", "topics": [ "compute_mcmc_diagnostics" ] }, { "page": "compute_XtX_Xty_bip_cpp", "title": "Compute X'X and X'y for bipartite covariate regression", "topics": [ "compute_XtX_Xty_bip_cpp" ] }, { "page": "comtrade", "title": "Comtrade data", "topics": [ "comtrade" ] }, { "page": "confint.ame", "title": "Bayesian credible intervals for AME model parameters", "topics": [ "confint.ame", "confint.lame" ] }, { "page": "confint.ame_als", "title": "Confidence intervals for a fast AME fit", "topics": [ "confint.ame_als" ] }, { "page": "confint.boot_ame", "title": "Confidence intervals from a fast AME bootstrap", "topics": [ "confint.boot_ame" ] }, { "page": "design_array_listwisedel", "title": "Computes the design socioarray of covariate values", "topics": [ "design_array_listwisedel" ] }, { "page": "detect_change_point", "title": 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"gof", "title": "Compute GOF statistics from saved posterior samples", "topics": [ "gof" ] }, { "page": "gof_plot", "title": "Visualize goodness-of-fit statistics for AME and LAME models", "topics": [ "gof_plot" ] }, { "page": "gof_plot.ame_als", "title": "Goodness-of-fit check for an ame_als fit", "topics": [ "gof_plot.ame_als" ] }, { "page": "gof_stats", "title": "Goodness of fit statistics", "topics": [ "gof_stats" ] }, { "page": "gof_stats_bipartite", "title": "Goodness of fit statistics for bipartite networks", "topics": [ "gof_stats_bipartite" ] }, { "page": "gof_stats_unipartite", "title": "Goodness of fit statistics for unipartite networks", "topics": [ "gof_stats_unipartite" ] }, { "page": "gof_temporal", "title": "Posterior-predictive temporal-trend test", "topics": [ "gof_temporal" ] }, { "page": "init_dynamic_ab_cpp", "title": "Initialize dynamic additive effects with AR(1) structure", "topics": [ "init_dynamic_ab_cpp" ] }, { "page": "init_dynamic_positions", "title": "Initialize dynamic latent positions with AR(1) structure", "topics": [ "init_dynamic_positions" ] }, { "page": "IR90s", "title": "International relations in the 90s", "topics": [ "IR90s" ] }, { "page": "lame", "title": "AME model fitting routine for longitudinal relational data", "topics": [ "lame" ] }, { "page": "lame_als", "title": "Fast (MCMC-free) AME estimation for a longitudinal network", "topics": [ "lame_als" ] }, { "page": "lame_multi", "title": "Multi-panel lame() with shared coefficients", "topics": [ "lame_multi" ] }, { "page": "lame_parallel", "title": "Run LAME (longitudinal AME) with multiple parallel chains", "topics": [ "lame_parallel" ] }, { "page": "lame_resume", "title": "Resume a 'lame()' MCMC run from a checkpoint", "topics": [ "lame_resume" ] }, { "page": "lame_snap_als", "title": "Fast approximate dynamic snap-shift AME estimator", "topics": [ "lame_snap_als" ] }, { "page": "latent_positions", "title": "Extract latent positions as a tidy data frame", "topics": [ "latent_positions", "latent_positions.ame", "latent_positions.ame_als", "latent_positions.lame" ] }, { "page": "lazegalaw", "title": "Lazega's law firm data", "topics": [ "lazegalaw" ] }, { "page": "lfo", "title": "Exact rolling-origin leave-future-out cross-validation", "topics": [ "lfo" ] }, { "page": "list_to_array", "title": "Convert list to array", "topics": [ "list_to_array" ] }, { "page": "logLik.ame", "title": "Log-likelihood is not directly exposed for ame() / lame() fits", "topics": [ "logLik.ame", "logLik.lame" ] }, { "page": "logLik.ame_als", "title": "Log-likelihood is not defined for a fast AME fit", "topics": [ "logLik.ame_als" ] }, { "page": "loo", "title": "Generic dispatcher for loo / waic on ame / lame fits", "topics": [ "loo", "waic" ] }, { "page": "loo.ame", "title": "Approximate leave-one-out cross-validation for AME / LAME fits", "topics": [ "loo.ame", "loo.ame_als", "loo.lame" ] }, { "page": "mhalf", "title": "Symmetric square root of a matrix", "topics": [ "mhalf" ] }, { "page": "nobs.ame", "title": "Number of observed dyads in an AME / LAME fit", "topics": [ "nobs.ame", "nobs.lame" ] }, { "page": "nobs.ame_als", "title": "Number of observed dyads in an ame_als fit", "topics": [ "nobs.ame_als" ] }, { "page": "nodematch", "title": "ERGM-style covariate helpers for ame() / lame()", "topics": [ "absdiff", "nodefactor", "nodematch" ] }, { "page": "per_actor_slopes", "title": "Post-MCMC per-actor time-varying slopes", "topics": [ "per_actor_slopes" ] }, { "page": "plot.ame", "title": "Simple diagnostic plot for AME model fit", "topics": [ "plot.ame" ] }, { "page": "plot.ame_als", "title": "Plot the convergence of a fast AME fit", "topics": [ "plot.ame_als" ] }, { "page": "plot.lame", "title": "Plot diagnostics for a LAME model fit", "topics": [ "plot.lame" ] }, { "page": "posterior_options", "title": "Options for saving posterior samples during MCMC", "topics": [ "posterior_options" ] }, { "page": "posterior_quantiles", "title": "Extract posterior quantiles for model components", "topics": [ "posterior_quantiles" ] }, { "page": "predict.ame", "title": "Predict method for AME models", "topics": [ "predict.ame" ] }, { "page": "predict.ame_als", "title": "Predictions from a fast AME fit", "topics": [ "predict.ame_als" ] }, { "page": "predict.lame", "title": "Predict method for LAME models", "topics": [ "predict.lame" ] }, { "page": "prediction_draws_long", "title": "Long-format draws of the linear predictor for marginaleffects-style use", "topics": [ "prediction_draws_long" ] }, { "page": "print.als_dynamic_beta", "title": "Print method for penalised ALS time-varying beta", "topics": [ "print.als_dynamic_beta" ] }, { "page": "print.ame", "title": "Print method for AME model objects", "topics": [ "print.ame" ] }, { "page": "print.ame_als", "title": "Print an ame_als object", "topics": [ "print.ame_als" ] }, { "page": "print.ame.sim", "title": "Print methods for AME and LAME simulation objects", "topics": [ "print.ame.sim", "print.lame.sim" ] }, { "page": "print.boot_ame", "title": "Print bootstrap results for a fast AME fit", "topics": [ "print.boot_ame" ] }, { "page": "print.gof_temporal", "title": "Print method for gof_temporal output", "topics": [ "print.gof_temporal" ] }, { "page": "print.lame", "title": "Print method for LAME objects", "topics": [ "print.lame" ] }, { "page": "print.lame_multi", "title": "Print method for lame_multi", "topics": [ "print.lame_multi" ] }, { "page": "print.lfo_lame", "title": "Print method for lfo() results", "topics": [ "print.lfo_lame" ] }, { "page": "print.per_actor_slopes", "title": "Print method for per_actor_slopes", "topics": [ "print.per_actor_slopes" ] }, { "page": "print.summary.ame", "title": "Print method for summary.ame objects", "topics": [ "print.summary.ame" ] }, { "page": "print.summary.ame_als", "title": "Print a fast AME summary", "topics": [ "print.summary.ame_als" ] }, { "page": "print.summary.lame", "title": "Print method for summary.lame objects", "topics": [ "print.summary.lame" ] }, { "page": "prior_summary", "title": "Print the priors used by an AME / LAME / ame_als fit", "topics": [ "prior_summary", "prior_summary.ame", "prior_summary.ame_als", "prior_summary.default", "prior_summary.lame" ] }, { "page": "procrustes_align", "title": "Procrustes alignment of latent positions across time", "topics": [ "procrustes_align" ] }, { "page": "rbeta_ab_bip_gibbs_cpp", "title": "Full bipartite Gibbs update for beta, a, b", "topics": [ "rbeta_ab_bip_gibbs_cpp" ] }, { "page": "read_log_lik", "title": "Read the per-iteration log-lik matrix back from on-disk chunks", "topics": [ "read_log_lik" ] }, { "page": "reconstruct_EZ", "title": "Reconstruct EZ and UVPM matrices from AME model output", "topics": [ "reconstruct_EZ", "reconstruct_UVPM" ] }, { "page": "residuals.ame", "title": "Extract residuals from AME model", "topics": [ "residuals.ame" ] }, { "page": "residuals.ame_als", "title": "Residuals from a fast AME fit", "topics": [ "residuals.ame_als" ] }, { "page": "residuals.lame", "title": "Extract residuals from LAME model", "topics": [ "residuals.lame" ] }, { "page": "rhat_dynamic_beta", "title": "Multivariate split-R-hat for dynamic_beta coefficient paths", "topics": [ "rhat_dynamic_beta" ] }, { "page": "rmvnorm", "title": "Simulation from a multivariate normal distribution", "topics": [ "rmvnorm" ] }, { "page": "rSab_fc", "title": "Gibbs update for additive effects covariance", "topics": [ "rSab_fc" ] }, { "page": "rUV_dynamic_bip_fc_cpp", "title": "Bipartite dynamic UV Gibbs update", "topics": [ "rUV_dynamic_bip_fc_cpp" ] }, { "page": "rUV_dynamic_fc", "title": "Gibbs sampling of dynamic U and V with AR(1) evolution", "topics": [ "rUV_dynamic_fc" ] }, { "page": "rUV_dynamic_fc_cpp", "title": "Update dynamic latent positions using AR(1) process", "topics": [ "rUV_dynamic_fc_cpp" ] }, { "page": "rUV_dynamic_snap_fc", "title": "Gibbs sampling of dynamic U and V with snap-shift dynamics", "topics": [ "rUV_dynamic_snap_fc" ] }, { "page": "rUV_dynamic_snap_fc_cpp", "title": "Update dynamic latent positions with snap-shift model selection", "topics": [ "rUV_dynamic_snap_fc_cpp" ] }, { "page": "rUV_dynamic_t_fc", "title": "Gibbs sampling of dynamic U and V with heavy-tailed (Student-t) innovations", "topics": [ "rUV_dynamic_t_fc" ] }, { "page": "rUV_dynamic_t_fc_cpp", "title": "Update dynamic latent positions with heavy-tailed (Student-t) AR(1) innovations", "topics": [ "rUV_dynamic_t_fc_cpp" ] }, { "page": "rUV_sym_fc", "title": "Gibbs sampling of U and V", "topics": [ "rUV_sym_fc" ] }, { "page": "rZ_bin_bip_batch_cpp", "title": "Batch binary Z sampling across all time periods (bipartite, rho=0)", "topics": [ "rZ_bin_bip_batch_cpp" ] }, { "page": "rZ_nrm_batch_cpp", "title": "Batch normal Z sampling across all time periods", "topics": [ "rZ_nrm_batch_cpp" ] }, { "page": "rZ_nrm_fc", "title": "Simulate missing values in a normal AME model", "topics": [ "rZ_nrm_fc" ] }, { "page": "rZ_pois_fc", "title": "Gibbs update for latent variable in a Poisson AME model", "topics": [ "rZ_pois_fc" ] }, { "page": "sample_beta_dynamic_cpp", "title": "Sample the dynamic-block beta path via FFBS", "topics": [ "sample_beta_dynamic_cpp" ] }, { "page": "sample_beta_static_cpp", "title": "Sample the static-block beta conditional on the dynamic path", "topics": [ "sample_beta_static_cpp" ] }, { "page": "sample_dynamic_ab_cpp", "title": "Sample dynamic additive effects with AR(1) evolution", "topics": [ "sample_dynamic_ab_cpp" ] }, { "page": "sample_rho_ab_cpp", "title": "Sample AR(1) parameter for dynamic additive effects", "topics": [ "sample_rho_ab_cpp" ] }, { "page": "sample_rho_beta_cpp", "title": "Sample the AR(1) rho for each dynamic block", "topics": [ "sample_rho_beta_cpp" ] }, { "page": "sample_rho_uv", "title": "Sample AR(1) parameter for dynamic latent factors", "topics": [ "sample_rho_uv" ] }, { "page": "sample_sigma_ab_cpp", "title": "Sample innovation variance for dynamic additive effects", "topics": [ "sample_sigma_ab_cpp" ] }, { "page": "sample_sigma_beta_cpp", "title": "Sample the AR(1) innovation sigma for each dynamic block", "topics": [ "sample_sigma_beta_cpp" ] }, { "page": "sample_sigma_uv", "title": "Sample innovation variance for dynamic latent factors", "topics": [ "sample_sigma_uv" ] }, { "page": "sampler_describe", "title": "Describe the estimator behind a fitted object", "topics": [ "sampler_describe", "sampler_describe.ame", "sampler_describe.ame_als", "sampler_describe.boot_ame", "sampler_describe.lame" ] }, { "page": "sampsonmonks", "title": "Sampson's monastery data", "topics": [ "sampsonmonks" ] }, { "page": "sheep", "title": "Sheep dominance data", "topics": [ "sheep" ] }, { "page": "simulate_posterior", "title": "Simulate posterior distributions from fitted AME model", "topics": [ "simulate_posterior" ] }, { "page": "simulate.ame", "title": "Simulate networks from a fitted AME model", "topics": [ "simulate.ame" ] }, { "page": "simulate.ame_als", "title": "Simulate networks from a fitted ame_als model", "topics": [ "simulate.ame_als" ] }, { "page": "simulate.lame", "title": "Simulate longitudinal networks from a fitted LAME model", "topics": [ "simulate.lame" ] }, { "page": "simY_pois", "title": "Simulate a Poisson relational matrix", "topics": [ "simY_pois" ] }, { "page": "snap_category_summary", "title": "Summarize snap indices by actor category", "topics": [ "snap_category_summary" ] }, { "page": "snap_index_draws", "title": "Extract posterior draws of snap indices", "topics": [ "snap_index_draws" ] }, { "page": "snap_index_summary", "title": "Summarize posterior snap indices", "topics": [ "snap_index_summary" ] }, { "page": "snap_rank_summary", "title": "Summarize posterior rank uncertainty for snap years", "topics": [ "snap_rank_summary" ] }, { "page": "summary.ame", "title": "Summary of an AME object", "topics": [ "summary.ame" ] }, { "page": "summary.ame_als", "title": "Summarize an ame_als object", "topics": [ "summary.ame_als" ] }, { "page": "summary.ame.sim", "title": "Summary method for AME simulations", "topics": [ "summary.ame.sim" ] }, { "page": "summary.boot_ame", "title": "Summarize bootstrap results for a fast AME fit", "topics": [ "print.summary.boot_ame", "summary.boot_ame" ] }, { "page": "summary.lame", "title": "Summary of a LAME object", "topics": [ "summary.lame" ] }, { "page": "summary.lame.sim", "title": "Summary method for LAME simulations", "topics": [ "summary.lame.sim" ] }, { "page": "tidy", "title": "S3 generic for 'tidy'", "topics": [ "tidy" ] }, { "page": "tidy.ame", "title": "Tidy method for fitted 'ame' / 'lame' objects", "topics": [ "tidy.ame", "tidy.lame" ] }, { "page": "tidy.ame_als", "title": "Tidy method for fitted 'ame_als' / 'lame_als' objects", "topics": [ "tidy.ame_als", "tidy.lame_als" ] }, { "page": "tidy.boot_ame", "title": "Tidy method for a standalone bootstrap object ('boot_ame')", "topics": [ "tidy.boot_ame" ] }, { "page": "trace_plot", "title": "MCMC trace plots and density plots for AME/LAME model parameters", "topics": [ "trace_plot" ] }, { "page": "update.ame", "title": "Update an AME / LAME fit", "topics": [ "update.ame", "update.lame" ] }, { "page": "update.ame_als", "title": "Update an 'ame_als' / 'lame_als' fit", "topics": [ "update.ame_als", "update.lame_als" ] }, { "page": "uv_plot", "title": "Visualize multiplicative effects (latent factors) from AME models", "topics": [ "uv_plot" ] }, { "page": "vcov.ame", "title": "Posterior covariance of AME model coefficients", "topics": [ "vcov.ame", "vcov.lame" ] }, { "page": "vcov.ame_als", "title": "Sandwich covariance for the regression coefficients of a fast AME fit", "topics": [ "vcov.ame_als" ] }, { "page": "vcov.boot_ame", "title": "Bootstrap covariance of the regression coefficients", "topics": [ "vcov.boot_ame" ] }, { "page": "vignette_data", "title": "TIES sanctions data for vignettes", "topics": [ "vignette_data" ] }, { "page": "waic.ame", "title": "WAIC for AME / LAME fits", "topics": [ "waic.ame", "waic.ame_als", "waic.lame" ] }, { "page": "Xbeta", "title": "Linear combinations of submatrices of an array", "topics": [ "Xbeta" ] }, { "page": "Xbeta_bip_cpp", "title": "Compute Xbeta product for bipartite networks", "topics": [ "Xbeta_bip_cpp" ] }, { "page": "Xcol", "title": "Column covariates", "topics": [ "Xcol" ] }, { "page": "Xdyad", "title": "Dyadic covariates", "topics": [ "Xdyad" ] }, { "page": "Xrow", "title": "Row covariates", "topics": [ "Xrow" ] }, { "page": "Y", "title": "Relational matrix", "topics": [ "Y" ] }, { "page": "YX_bin", "title": "binary relational data and covariates", "topics": [ "YX_bin" ] }, { "page": "YX_bin_list", "title": "Synthetic longitudinal binary relational data, list-form (latent-scale)", "topics": [ "YX_bin_list" ] }, { "page": "YX_bin_long", "title": "synthetic longitudinal binary relational data (latent-scale)", "topics": [ "YX_bin_long" ] }, { "page": "YX_cbin", "title": "Censored 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Baseline", "Dynamic Latent Positions (dynamic_uv = TRUE)", "Dynamic Additive Effects (dynamic_ab = TRUE)", "Dynamic Regression Coefficients (dynamic_beta)", "A worked dynamic_beta example", "Choosing a prior family: dynamic_beta_kind", "Hierarchical pooling: dynamic_beta_pool", "Per-actor time-varying slopes: dynamic_beta_per_actor", "Diagnosing your dynamic fit", "Temporal-trend posterior-predictive check: gof_temporal()", "Inspecting the priors actually in effect: prior_summary()", "Multi-chain $\\hat R$ via lame_parallel() and rhat_dynamic_beta()", "Forecasting", "Visualising the coefficient path: autoplot.lame", "LOO / WAIC via save_log_lik = TRUE", "Prior Specifications", "Genuine Dynamics: What They Look Like", "What Happens Without Temporal Structure?", "Visualizing the Null Case", "Comparing Model Specifications", "When to Use Dynamic Effects", "Dealing with Rotational Indeterminacy", "References" ], "created": "2025-08-11 13:22:49", "modified": "2026-07-20 11:55:37", "commits": 15 }, { "source": "fast_estimation.Rmd", "filename": "fast_estimation.html", "title": "Fast (MCMC-free) AME Estimation", "author": "Cassy Dorff, Shahryar Minhas, and Tosin Salau", "engine": "knitr::rmarkdown", "headings": [ "When to use the fast estimator", "Wall-clock benchmark", "What ALS can do vs. what only MCMC does", "A first fit", "Choosing the rank R", "Non-normal families", "Uncertainty", "ALS bootstrap vs MCMC posterior on the same data", "Longitudinal data", "Summary" ], "created": "2026-05-28 21:45:06", "modified": "2026-07-20 11:55:37", "commits": 6 }, { "source": "forecasting.Rmd", "filename": "forecasting.html", "title": "Forecasting Longitudinal Networks with lame", "author": "Cassy Dorff, Tosin Salau, Shahryar Minhas", "engine": "knitr::rmarkdown", "headings": [ "What this vignette covers", "Step 1: Fit", "Step 2: Forecast h periods ahead", "Visualising the forecast", "Poisson exposure offsets via period_exposure", "Step 3: Counterfactual forecasts", "Step 4: Visualise the coefficient path", "Step 5: Forecast diagnostics", "Watching it fail: a sparse, short, drifting panel", "Probability-integral-transform (PIT) calibration", "Comparing two fits with loo_compare()", "Memory-conscious long runs", "See also" ], "created": "2026-05-28 21:45:06", "modified": "2026-07-20 11:55:37", "commits": 4 }, { "source": "lame.Rmd", "filename": "lame.html", "title": "Getting Started with lame", "author": "Cassy Dorff, Shahryar Minhas, and Tosin Salau", "engine": "knitr::rmarkdown", "headings": [ "What Is lame?", "Coming from a tidy edgelist? Use netify", "A 5-Minute Example", "What Can You Do with a Fitted Model?", "Checking Your Model", "Visualizing Network Structure", "Cross-Sectional Models", "Dynamic Effects", "Bipartite Networks", "Supported Data Types", "Power-User Features", "Where to Go Next", "References" ], "created": "2025-08-12 00:36:28", "modified": "2026-07-20 11:55:37", "commits": 11 }, { "source": "lame-overview.Rmd", "filename": "lame-overview.html", "title": "lame Overview", "author": "Cassy Dorff, Shahryar Minhas, and Tosin Salau", "engine": "knitr::rmarkdown", "headings": [ "Package Overview", "Migrating from amen", "Application: Dutch College Friendships", "Fitting the Model", "Interpreting the Results", "Checking Convergence", "Bayesian-ecosystem diagnostics: posterior::as_draws()", "Goodness of Fit", "Posterior-predictive temporal-trend test: gof_temporal()", "Latent Space", "Extracting Latent Positions", "Tidy-data round-trip via netify", "Entry: tidy edgelist → lame()", "Exit: tidy(), autoplot(), prediction_draws_long()", "Coefficient tables: glance() + tidy() -> modelsummary / gt / kableExtra", "Model comparison: loo::loo_compare() as a one-liner", "Reproducibility: sessionInfo() and renv.lock", "What's Next?" ], "created": "2025-06-10 00:29:37", "modified": "2026-07-21 11:18:45", "commits": 21 }, { "source": "cross_sec_ame.Rmd", "filename": "cross_sec_ame.html", "title": "Your First AME Model", "author": "Cassy Dorff, Shahryar Minhas, and Tosin Salau", "engine": "knitr::rmarkdown", "headings": [ "Why Network Models?", "The Data", "Building Covariates", "Fitting the Model", "A note for ERGM users: what AME assumes", "What Did We Find?", "Did the Sampler Converge?", "Numerical convergence diagnostics: posterior::as_draws()", "Between-chain $\\hat R$: ame_parallel(n_chains = 4)", "Other quick checks", "Model comparison: loo() and loo_compare()", "Does the Model Fit the Data?", "Visualizing the Latent Space", "Who Are the Most Social / Popular Students?", "Making Predictions", 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