{
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  "Package": "dream",
  "Title": "Dynamic Relational Event Analysis and Modeling",
  "Version": "2.1.2",
  "Authors@R": "c(\nperson(given = c(\"Kevin\",\"A.\"), family = \"Carson\", email = \"kacarson@arizona.edu\",\nrole = c(\"aut\", \"cre\"),comment = c(ORCID = \"0009-0001-5762-2586\")),\nperson(given = c(\"Diego\",\"F.\"),family=  \"Leal\", email= \"dflc@arizona.edu\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0001-9973-4626\")))",
  "Maintainer": "Kevin A. Carson <kacarson@arizona.edu>",
  "Description": "A set of tools for relational and event analysis,\nincluding two- and one-mode network brokerage and structural\nmeasures, and helper functions optimized for relational event\nanalysis with large datasets, including creating relational\nrisk sets, computing network statistics, estimating relational\nevent models, and simulating relational event sequences. For\nmore information on relational event models, see Butts (2008)\n<doi:10.1111/j.1467-9531.2008.00203.x>, Lerner and Lomi (2020)\n<doi:10.1017/nws.2019.57>, Bianchi et al. (2024)\n<doi:10.1146/annurev-statistics-040722-060248>, and Butts et\nal. (2023) <doi:10.1017/nws.2023.9>. In terms of the structural\nmeasures in this package, see Leal (2025)\n<doi:10.1177/00491241251322517>, Burchard and Cornwell (2018)\n<doi:10.1016/j.socnet.2018.04.001>, and Fujimoto et al. (2018)\n<doi:10.1017/nws.2018.11>. This package was developed with\nsupport from the National Science Foundation’s (NSF) Human\nNetworks and Data Science Program (HNDS) under award number\n2241536 (PI: Diego F. Leal). Any opinions, findings, and\nconclusions, or recommendations expressed in this material are\nthose of the authors and do not necessarily reflect the views\nof the NSF.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.3",
  "URL": "https://github.com/kevinCarson/dream",
  "BugReports": "https://github.com/kevinCarson/dream/issues",
  "Repository": "https://kevincarson.r-universe.dev",
  "Date/Publication": "2026-06-02 22:11:45 UTC",
  "RemoteUrl": "https://github.com/kevincarson/dream",
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  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-06-02 23:45:24 UTC",
    "User": "root"
  },
  "Author": "Kevin A. Carson [aut, cre] (ORCID:\n<https://orcid.org/0009-0001-5762-2586>),\nDiego F. Leal [aut] (ORCID: <https://orcid.org/0000-0001-9973-4626>)",
  "MD5sum": "8027322b2bb082c1b851d79b62b1244e",
  "_user": "kevincarson",
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  "_created": "2026-06-02T23:45:24.000Z",
  "_published": "2026-06-02T23:54:09.247Z",
  "_distro": "noble",
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    "author": "Kevin A. Carson <kevincarson3718@gmail.com>",
    "committer": "Kevin A. Carson <kevincarson3718@gmail.com>",
    "message": "Updated the version to 2.1.2\n\nIncluded three new functions for the `dream_rem` class: gof_rem, plot, and residuals.\n",
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    "name": "Kevin A. Carson",
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  "_updates": [
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  ],
  "_tags": [],
  "_topics": [
    "network-analysis",
    "relational-event-models",
    "openblas",
    "cpp",
    "openmp"
  ],
  "_stars": 3,
  "_contributors": [
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      "uuid": 112520273
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    "type": "user",
    "name": "Kevin A. Carson",
    "description": "PhD Candidate at the University of Arizona School of Sociology"
  },
  "_downloads": {
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/dream"
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  "_devurl": "https://github.com/kevincarson/dream",
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  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/dream.html",
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    "extra/readme.md",
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  "_realowner": "kevincarson",
  "_cranurl": true,
  "_releases": [
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      "date": "2025-07-19"
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      "date": "2026-03-25"
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    "dream_sequence",
    "dreamstats_actor",
    "dreamstats_actorfe",
    "dreamstats_degree",
    "dreamstats_dyadcut",
    "dreamstats_dyadfe",
    "dreamstats_dyadic",
    "dreamstats_event",
    "dreamstats_fourcycles",
    "dreamstats_persistence",
    "dreamstats_prefattachment",
    "dreamstats_recency",
    "dreamstats_reciprocity",
    "dreamstats_repetition",
    "dreamstats_triads",
    "estimate_rem",
    "gof_rem",
    "netstats_om_constraint",
    "netstats_om_effective",
    "netstats_om_nwalks",
    "netstats_om_pib",
    "netstats_tm_constraint",
    "netstats_tm_degreecent",
    "netstats_tm_density",
    "netstats_tm_effective",
    "netstats_tm_egodistance",
    "netstats_tm_homfourcycles",
    "netstats_tm_redundancy",
    "simulate_rem_seq"
  ],
  "_datasets": [
    {
      "name": "southern.women",
      "title": "Davis Southern Women's Dataset",
      "object": "southern.women",
      "file": "southern.women.rda",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "E1",
        "E2",
        "E3",
        "E4",
        "E5",
        "E6",
        "E7",
        "E8",
        "E9",
        "E10",
        "E11",
        "E12",
        "E13",
        "E14"
      ],
      "rows": 18,
      "table": true,
      "tojson": true
    },
    {
      "name": "WikiEvent2018.first100k",
      "title": "Wikipedia Edit Event Sequence 2018",
      "object": "WikiEvent2018.first100k",
      "file": "WikiEvent2018.first100k.rda",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "time",
        "user",
        "article",
        "eventID"
      ],
      "rows": 100000,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "as.data.frame.dream_sequence",
      "title": "Coerce a 'dream_sequence' Object into a 'data.frame' Object",
      "topics": [
        "as.data.frame.dream_sequence"
      ]
    },
    {
      "page": "coef.dream_rem",
      "title": "Extract the ML parameter estimates from Relational Event Model Fits",
      "topics": [
        "coef.dream_rem"
      ]
    },
    {
      "page": "create_res",
      "title": "Process One- and Two-Mode Relational Event Sequences and Create Post-Processing Relational Event Sequences",
      "topics": [
        "create_res"
      ]
    },
    {
      "page": "dream_information",
      "title": "dream: A Package for Dynamic Relational Event Analysis and Modeling",
      "topics": [
        "dream_information"
      ]
    },
    {
      "page": "dream_sequence",
      "title": "Helper Function to Create 'dream_sequence' Objects",
      "topics": [
        "dream_sequence"
      ]
    },
    {
      "page": "dreamstats_actor",
      "title": "Add Actor-Level Statistics for Event Dyads in a Relational Event Sequence",
      "topics": [
        "dreamstats_actor"
      ]
    },
    {
      "page": "dreamstats_actorfe",
      "title": "Add Actor-Level Fixed Effects for Event Dyads in a Relational Event Sequence",
      "topics": [
        "dreamstats_actorfe"
      ]
    },
    {
      "page": "dreamstats_degree",
      "title": "Compute Degree Network Statistics for Event Senders and Receivers in a Post-Processing Relational Event Sequence",
      "topics": [
        "dreamstats_degree"
      ]
    },
    {
      "page": "dreamstats_dyadcut",
      "title": "A Helper Function to Assist Researchers in Finding Dyadic Weight Cutoff Values",
      "topics": [
        "dreamstats_dyadcut"
      ]
    },
    {
      "page": "dreamstats_dyadfe",
      "title": "Add Dyadic-Level Fixed Effects for Event Dyads in a Relational Event Sequence",
      "topics": [
        "dreamstats_dyadfe"
      ]
    },
    {
      "page": "dreamstats_dyadic",
      "title": "Add Dyadic-Level Statistics for Event Dyads in a Relational Event Sequence",
      "topics": [
        "dreamstats_dyadic"
      ]
    },
    {
      "page": "dreamstats_event",
      "title": "Add Event-Level Statistics for a Relational Event Sequence",
      "topics": [
        "dreamstats_event"
      ]
    },
    {
      "page": "dreamstats_fourcycles",
      "title": "Compute the Four-Cycles Network Statistic for Event Dyads in a Relational Event Sequence",
      "topics": [
        "dreamstats_fourcycles"
      ]
    },
    {
      "page": "dreamstats_persistence",
      "title": "Compute Butts' (2008) Persistence Network Statistic for Event Dyads in a Relational Event Sequence",
      "topics": [
        "dreamstats_persistence"
      ]
    },
    {
      "page": "dreamstats_prefattachment",
      "title": "Compute Butts' (2008) Preferential Attachment Network Statistic for Event Dyads in a Relational Event Sequence",
      "topics": [
        "dreamstats_prefattachment"
      ]
    },
    {
      "page": "dreamstats_recency",
      "title": "Compute Butts' (2008) Recency Network Statistic for Event Dyads in a Relational Event Sequence",
      "topics": [
        "dreamstats_recency"
      ]
    },
    {
      "page": "dreamstats_reciprocity",
      "title": "Compute the Reciprocity Network Statistic for Event Dyads in a Relational Event Sequence",
      "topics": [
        "dreamstats_reciprocity"
      ]
    },
    {
      "page": "dreamstats_repetition",
      "title": "Compute Butts' (2008) Repetition Network Statistic for Event Dyads in a Relational Event Sequence",
      "topics": [
        "dreamstats_repetition"
      ]
    },
    {
      "page": "dreamstats_triads",
      "title": "Compute Butts' (2008) Triadic Formation Statistics for Relational Event Sequences",
      "topics": [
        "dreamstats_triads"
      ]
    },
    {
      "page": "estimate_rem",
      "title": "Fit a Maximum Likelihood Relational Event Model (REM) to A Processed Relational Event Sequence",
      "topics": [
        "estimate_rem"
      ]
    },
    {
      "page": "gof_rem",
      "title": "Estimate the proportion of dyads predicted by 'dream_rem' relational event model fits",
      "topics": [
        "gof_rem"
      ]
    },
    {
      "page": "logLik.dream_rem",
      "title": "Extract the model log-likelihood from Relational Event Model Fits",
      "topics": [
        "logLik.dream_rem"
      ]
    },
    {
      "page": "netstats_om_constraint",
      "title": "Compute Burt's (1992) Constraint for Ego Networks from a Sociomatrix",
      "topics": [
        "netstats_om_constraint"
      ]
    },
    {
      "page": "netstats_om_effective",
      "title": "Compute Burt's (1992) Effective Size for Ego Networks from a Sociomatrix",
      "topics": [
        "netstats_om_effective"
      ]
    },
    {
      "page": "netstats_om_nwalks",
      "title": "Compute the Number of Walks of Length K in a One-Mode Network",
      "topics": [
        "netstats_om_nwalks"
      ]
    },
    {
      "page": "netstats_om_pib",
      "title": "Compute Potential for Intercultural Brokerage (PIB) Based on Leal (2025)",
      "topics": [
        "netstats_om_pib"
      ]
    },
    {
      "page": "netstats_tm_constraint",
      "title": "Compute Burchard and Cornwell's (2018) Two-Mode Constraint",
      "topics": [
        "netstats_tm_constraint"
      ]
    },
    {
      "page": "netstats_tm_degreecent",
      "title": "Compute Degree Centrality Values for Two-Mode Networks",
      "topics": [
        "netstats_tm_degreecent"
      ]
    },
    {
      "page": "netstats_tm_density",
      "title": "Compute Level-Specific Graph Density for Two-Mode Networks",
      "topics": [
        "netstats_tm_density"
      ]
    },
    {
      "page": "netstats_tm_effective",
      "title": "Compute Burchard and Cornwell's (2018) Two-Mode Effective Size",
      "topics": [
        "netstats_tm_effective"
      ]
    },
    {
      "page": "netstats_tm_egodistance",
      "title": "Compute Fujimoto, Snijders, and Valente's (2018) Ego Homophily Distance for Two-Mode Networks",
      "topics": [
        "netstats_tm_egodistance"
      ]
    },
    {
      "page": "netstats_tm_homfourcycles",
      "title": "Compute Fujimoto, Snijders, and Valente's (2018) Homophilous Four-Cycles for Two-Mode Networks",
      "topics": [
        "netstats_tm_homfourcycles"
      ]
    },
    {
      "page": "netstats_tm_redundancy",
      "title": "Compute Burchard and Cornwell's (2018) Two-Mode Redundancy",
      "topics": [
        "netstats_tm_redundancy"
      ]
    },
    {
      "page": "plot.dream_rem",
      "title": "Plot method for 'dream_rem' Relational Event Model Fits",
      "topics": [
        "plot.dream_rem"
      ]
    },
    {
      "page": "predict.dream_rem",
      "title": "Predict method for Relational Event Model Fits",
      "topics": [
        "predict.dream_rem"
      ]
    },
    {
      "page": "print.dream_rem",
      "title": "Print Method for dreamrem Model",
      "topics": [
        "print.dream_rem"
      ]
    },
    {
      "page": "print.dream_sequence",
      "title": "Print Method for 'dream' object",
      "topics": [
        "print.dream_sequence"
      ]
    },
    {
      "page": "print.summary.dream_rem",
      "title": "Print Method for dreamrem Model",
      "topics": [
        "print.summary.dream_rem"
      ]
    },
    {
      "page": "print.summary.dream_sequence",
      "title": "Print Method for dream Model",
      "topics": [
        "print.summary.dream_sequence"
      ]
    },
    {
      "page": "residuals.dream_rem",
      "title": "Model residuals for 'dream_rem' relational event model fits",
      "topics": [
        "residuals.dream_rem"
      ]
    },
    {
      "page": "simulate_rem_seq",
      "title": "Simulate a Random One-Mode Relational Event Sequence",
      "topics": [
        "simulate_rem_seq"
      ]
    },
    {
      "page": "southern.women",
      "title": "Davis Southern Women's Dataset",
      "topics": [
        "southern.women"
      ]
    },
    {
      "page": "summary.dream_rem",
      "title": "Summary Method for dreamrem Objects",
      "topics": [
        "summary.dream_rem"
      ]
    },
    {
      "page": "summary.dream_sequence",
      "title": "Summary Method for dream_sequence Objects",
      "topics": [
        "summary.dream_sequence"
      ]
    },
    {
      "page": "vcov.dream_rem",
      "title": "Extract variance-covariance matrix from Relational Event Model Fits",
      "topics": [
        "vcov.dream_rem"
      ]
    },
    {
      "page": "WikiEvent2018.first100k",
      "title": "Wikipedia Edit Event Sequence 2018",
      "topics": [
        "WikiEvent2018.first100k"
      ]
    }
  ],
  "_readme": "https://github.com/kevincarson/dream/raw/HEAD/README.md",
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  "_indexed": true,
  "_nocasepkg": "dream",
  "_universes": [
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