{ "_id": "6ad4de8d4a34890ab4938246", "Package": "amorem", "Title": "Augmented Modelling of Relational Events", "Version": "1.2.0", "Authors@R": "c(\nperson(given = \"Francisco\", family = \"Richter\", email = \"richtf@usi.ch\", role = c(\"aut\", \"cre\")),\nperson(given = \"Martina\", family = \"Boschi\", role = \"aut\"),\nperson(given = \"Ernst C.\", family = \"Wit\", role = \"aut\"),\nperson(given = \"Melania\", family = \"Lembo\", role = \"aut\"))", "Description": "An integrated workflow for relational event models, in\nwhich timestamped interactions among actors arrive in\ncontinuous time and the event rate depends on the history of\nthe network itself. Event streams are simulated by an exact\nGillespie kernel or an approximate tau-leap kernel; non-events\nare drawn by case-control sampling in the manner of Lerner and\nLomi (2020) , or from a time-shifted\ncopy of the process so that covariates shared by all dyads at a\ngiven time remain estimable (Lembo et al. 2025,\n); the timing- and closure-based\nendogenous statistics of Juozaitiene and Wit (2024)\n are computed from that history;\nand one front-end fits the resulting case-control data by\nconditional-logistic partial likelihood, by penalised\nregression on its degenerate one-control binomial form (which\nadmits smooth, time-varying and actor random effects), or by\nstochastic gradient descent, including the additive-spline\nconstruction of Filippi-Mazzola and Wit (2024)\n. Martingale-residual\ngoodness-of-fit diagnostics are provided for the linear fit,\ntogether with simulation and statistic computation for\nrelational hyper-events, a collection of documented event logs\nfrom published interaction networks, and a reconstructed El\nNino index as a global covariate.", "License": "MIT + file LICENSE", "URL": "https://franciscorichter.github.io/amorem/,\nhttps://github.com/franciscorichter/amorem", "BugReports": "https://github.com/franciscorichter/amorem/issues", "Encoding": "UTF-8", "LazyData": "true", "Config/testthat/edition": "3", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Repository": "https://franciscorichter.r-universe.dev", "Date/Publication": "2026-10-18 12:46:11 UTC", "RemoteUrl": "https://github.com/franciscorichter/amorem", "RemoteRef": "HEAD", "RemoteSha": "56add2030cd05c6880e0de17de03a3cfb8d9a3e3", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-18 14:47:27 UTC", "User": "root" }, "Author": "Francisco Richter [aut, cre],\nMartina Boschi [aut],\nErnst C. Wit [aut],\nMelania Lembo [aut]", "Maintainer": "Francisco Richter ", "_user": "franciscorichter", "_type": "src", "_file": "amorem_1.2.0.tar.gz", "_fileid": "https://r2.ropensci.org/b3c3447dadb085ed9187a8153eda4ede4fb93876cccbf8ec30bc6018666d3e5b", "_filesize": 3106828, "_sha256": "b3c3447dadb085ed9187a8153eda4ede4fb93876cccbf8ec30bc6018666d3e5b", "_expires": "2027-01-26T14:58:19.000Z", "_created": "2026-10-18T14:47:27.000Z", "_published": "2026-10-18T14:58:21.373Z", "_buildurl": "https://github.com/r-universe/franciscorichter/actions/runs/39137344299", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 117705212201, "time": 230, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "12140905077" }, { "job": 117705212112, "time": 188, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12140681490" }, { "job": 117705212130, "time": 191, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12140133723" }, { 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{ "name": "daily_weather", "title": "Daily weather at the places and periods of the bundled event logs", "object": "daily_weather", "class": [ "data.frame" ], "fields": [ "station", "name", "date", "tmax", "tmin", "prcp" ], "rows": 944, "table": true, "tojson": true }, { "name": "dist_matrix", "title": "US state distance matrix", "object": "dist_matrix", "class": [ "matrix", "array" ], "fields": [ "Wyoming", "Alaska", "Maryland", "American Samoa", "Arkansas", "North Dakota", "Delaware", "Guam", "New Mexico", "Utah", "West Virginia", "Kansas", "Georgia", "Mississippi", "Minnesota", "New Hampshire", "Missouri", "Idaho", "South Dakota", "Pennsylvania", "Oklahoma", "Indiana", "New Jersey", "Nebraska", "Colorado", "New York", "Montana", "Kentucky", "Vermont", "District of Columbia", "Florida", "Nevada", "Commonwealth of the Northern Mariana Islands", "Tennessee", "Iowa", "Alabama", "Michigan", "California", "Connecticut", "Arizona", "Texas", "Virginia", "Illinois", "Hawaii", "Oregon", 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"type_of_conflict" ], "rows": 3518, "table": true, "tojson": true }, { "name": "us_leader_visits", "title": "US leader-to-leader visits, 1945-2019", "object": "us_leader_visits", "class": [ "data.frame" ], "fields": [ "time", "date", "sender", "receiver", "direction" ], "rows": 3945, "table": true, "tojson": true } ], "_help": [ { "page": "alien_invasions", "title": "Alien species first records, prepared for relational event models", "topics": [ "alien_invasions" ] }, { "page": "alien_mammals", "title": "Alien mammals: risk table with covariates (Juozaitienė et al. 2023)", "topics": [ "alien_mammals" ] }, { "page": "attach_static_covariates", "title": "Attach static covariates to an event log", "topics": [ "attach_static_covariates" ] }, { "page": "classroom_actors", "title": "Classroom actor attributes (McFarland 2001)", "topics": [ "classroom_actors" ] }, { "page": "classroom_events", "title": "Classroom interaction events (McFarland 2001)", "topics": [ "classroom_events" ] }, { "page": "college_msg", "title": "CollegeMsg: private messages on a university online community", "topics": [ "college_msg" ] }, { "page": "compare_models", "title": "Compare candidate endogenous specifications by AIC", "topics": [ "compare_models" ] }, { "page": "compare_models_global", "title": "Compare REM specifications with global covariate effects", "topics": [ "compare_models_global" ] }, { "page": "compare_models_smooth", "title": "Compare candidate specifications with smooth (TV / NL / TVNL) effects", "topics": [ "compare_models_smooth" ] }, { "page": "congress_bills_118", "title": "Bills of the 118th US Congress as hyperevents", "topics": [ "congress_bills_118" ] }, { "page": "congress_members_118", "title": "Members of the 118th US Congress", "topics": [ "congress_members_118" ] }, { "page": "copenhagen", "title": "Copenhagen Networks Study: phone calls, text messages and gender", "topics": [ "copenhagen", "copenhagen_actors", "copenhagen_calls", "copenhagen_sms" ] }, { "page": "cpp_supported_stats", "title": "Endogenous statistics with a compiled fast path", "topics": [ "cpp_supported_stats" ] }, { "page": "daily_weather", "title": "Daily weather at the places and periods of the bundled event logs", "topics": [ "daily_weather" ] }, { "page": "dist_matrix", "title": "US state distance matrix", "topics": [ "dist_matrix" ] }, { "page": "email_eu_core", "title": "Email-Eu-Core temporal (single-department subset)", "topics": [ "email_eu_core" ] }, { "page": "endogenous_features", "title": "Compute endogenous event-network statistics", "topics": [ "endogenous_features" ] }, { "page": "enso_nino34", "title": "Reconstructed Niño3.4 ENSO index, 1301-2005 (Li et al. 2013)", "topics": [ "enso_nino34" ] }, { "page": "football_results", "title": "Men's international football results since 1872", "topics": [ "football_results" ] }, { "page": "gof_auxiliary", "title": "GOF test for an auxiliary (unmodelled) statistic", "topics": [ "gof_auxiliary" ] }, { "page": "gof_global", "title": "Omnibus GOF test via Cauchy combination", "topics": [ "gof_global" ] }, { "page": "gof_multivariate", "title": "Multivariate GOF test for smooth or random-effect covariates", "topics": [ "gof_multivariate" ] }, { "page": "gof_rem", "title": "Goodness of fit for a fitted relational event model", "topics": [ "gof_rem" ] }, { "page": "gof_univariate", "title": "Goodness-of-fit test for a single FLE covariate", "topics": [ "gof_univariate" ] }, { "page": "hyperedge_activity", "title": "Activity counter for hyperedge subsets", "topics": [ "hyperedge_activity" ] }, { "page": "hyperedge_features", "title": "Endogenous features for a hyperedge event log", "topics": [ "hyperedge_features" ] }, { "page": "hyperedge_log", "title": "Build / detect / convert hyperedge event logs", "topics": [ "as_dyadic_log", "as_hyperedge_log", "hyperedge_log", "is_hyperedge_log" ] }, { "page": "hyperedge_sizes", "title": "Cardinality columns for a hyperedge event log", "topics": [ "hyperedge_sizes" ] }, { "page": "hyperedge_subrep", "title": "Subset repetition statistic for a hyperedge event log", "topics": [ "hyperedge_subrep" ] }, { "page": "martingale_residuals", "title": "Martingale residuals from a case-control partial-likelihood fit", "topics": [ "martingale_residuals" ] }, { "page": "nn_control", "title": "Control parameters for the neural-network backend of rem()", "topics": [ "nn_control" ] }, { "page": "nn_uncertainty", "title": "Bootstrap uncertainty for the neural rem() backend", "topics": [ "nn_uncertainty" ] }, { "page": "oni", "title": "Oceanic Niño Index, 1950-present", "topics": [ "oni" ] }, { "page": "plot.nn_uncertainty", "title": "Plot partial-dependence uncertainty bands", "topics": [ "plot.nn_uncertainty" ] }, { "page": "public_holidays", "title": "Public holidays for the periods of the bundled event logs", "topics": [ "public_holidays" ] }, { "page": "radoslaw_email", "title": "Manufacturing-company email events (Nurek & Michalski 2020)", "topics": [ "radoslaw_email" ] }, { "page": "radoslaw_hierarchy", "title": "Reporting hierarchy of the manufacturing company (Nurek & Michalski 2020)", "topics": [ "radoslaw_hierarchy" ] }, { "page": "rem", "title": "Fit a relational (hyper)event model on preprocessed case-control data", "topics": [ "rem" ] }, { "page": "sample_non_events", "title": "Sample non-events for inference", "topics": [ "sample_non_events" ] }, { "page": "simulate_actor_covariates", "title": "Simulate exogenous actor covariates", "topics": [ "simulate_actor_covariates" ] }, { "page": "simulate_directed_hyperedge_events", "title": "Simulate directed two-mode hyperedge events", "topics": [ "simulate_directed_hyperedge_events" ] }, { "page": "simulate_directed_hyperevents_tvnl", "title": "Simulate directed hyper-events with time-varying and non-linear effects", "topics": [ "simulate_directed_hyperevents_tvnl" ] }, { "page": "simulate_hyperedge_events", "title": "Simulate undirected hyperedge events (multi-actor 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