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  "Title": "LLM Relational Event Models",
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  "Description": "Fit Cox proportional hazards relational event models\n(REMs), including a separable formulation that partitions\nevents into initiation and continuation sub-models. Optionally\naugments REM simulations with large language model (LLM) agents\nthat select targets conditioned on event history, supporting\nmultiple providers ('OpenAI', 'Anthropic', 'xAI'/'Grok',\n'Google Gemini', 'Ollama', 'AWS Bedrock') through a common\ninterface. See Butts (2008)\n<doi:10.1111/j.1467-9531.2008.00203.x> for description of\nrelational event modeling.",
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    "compute_hazard_surface",
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    "llm_provider_bedrock",
    "llm_provider_gemini",
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    "llm_provider_mock",
    "llm_provider_ollama",
    "llm_provider_openai",
    "llm_provider_openai_compat",
    "make_behavior_queries",
    "make_query_predict",
    "make_query_roleplay_random",
    "make_query_roleplay_sender",
    "make_stat_means",
    "make_suffstats",
    "mock_strategy_highest_indegree",
    "mock_strategy_max_id",
    "mock_strategy_min_id",
    "prepare_edgelist",
    "rem_cfg",
    "rem_covariates",
    "run_llm_rem",
    "run_multiagent_rem"
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        "id_rec"
      ],
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      "tojson": true
    },
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      "name": "n_nodes",
      "title": "Number of nodes in the MID edgelist",
      "object": "n_nodes",
      "file": "n_nodes.rda",
      "class": [
        "numeric"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
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      "page": "attach_covariates_df",
      "title": "Attach exogenous covariates to a pre-built risk-set data frame",
      "topics": [
        "attach_covariates_df"
      ]
    },
    {
      "page": "compare_rem_models",
      "title": "Forest plot comparing fitted REM coefficients",
      "topics": [
        "compare_rem_models"
      ]
    },
    {
      "page": "compute_hazard_surface",
      "title": "Compute the full hazard surface over all directed dyads",
      "topics": [
        "compute_hazard_surface"
      ]
    },
    {
      "page": "compute_seq_stat_means",
      "title": "Compute sufficient-statistic means for a sequence without fitting a REM",
      "topics": [
        "compute_seq_stat_means"
      ]
    },
    {
      "page": "cov_dyad_static",
      "title": "Create a static dyad-level covariate",
      "topics": [
        "cov_dyad_static"
      ]
    },
    {
      "page": "cov_dyad_temporal",
      "title": "Create a temporal dyad-level covariate",
      "topics": [
        "cov_dyad_temporal"
      ]
    },
    {
      "page": "cov_node_static",
      "title": "Create a static node-level covariate",
      "topics": [
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      ]
    },
    {
      "page": "cov_node_temporal",
      "title": "Create a temporal node-level covariate",
      "topics": [
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      ]
    },
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    },
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      "title": "Fit a Relational Event Model to an edgelist",
      "topics": [
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      ]
    },
    {
      "page": "fit_rem_sep",
      "title": "Fit a Separable Relational Event Model",
      "topics": [
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      ]
    },
    {
      "page": "llm_call",
      "title": "Call an LLM provider",
      "topics": [
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      ]
    },
    {
      "page": "llm_provider_anthropic",
      "title": "Create an Anthropic Claude LLM provider",
      "topics": [
        "llm_provider_anthropic"
      ]
    },
    {
      "page": "llm_provider_bedrock",
      "title": "Create an AWS Bedrock LLM provider",
      "topics": [
        "llm_provider_bedrock"
      ]
    },
    {
      "page": "llm_provider_gemini",
      "title": "Create a Google Gemini LLM provider",
      "topics": [
        "llm_provider_gemini"
      ]
    },
    {
      "page": "llm_provider_grok",
      "title": "Create an xAI Grok LLM provider",
      "topics": [
        "llm_provider_grok"
      ]
    },
    {
      "page": "llm_provider_mock",
      "title": "Create a mock LLM provider for testing",
      "topics": [
        "llm_provider_mock"
      ]
    },
    {
      "page": "llm_provider_ollama",
      "title": "Create a local or remote Ollama LLM provider",
      "topics": [
        "llm_provider_ollama"
      ]
    },
    {
      "page": "llm_provider_openai",
      "title": "Create an OpenAI LLM provider",
      "topics": [
        "llm_provider_openai"
      ]
    },
    {
      "page": "llm_provider_openai_compat",
      "title": "Create a generic OpenAI-compatible LLM provider",
      "topics": [
        "llm_provider_openai_compat"
      ]
    },
    {
      "page": "make_behavior_queries",
      "title": "Generate plain-language behavioral guidelines from empirical sufficient stats",
      "topics": [
        "make_behavior_queries"
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    },
    {
      "page": "make_query_predict",
      "title": "Query factory: LLM predicts the next event",
      "topics": [
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    },
    {
      "page": "make_query_roleplay_random",
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      "topics": [
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    },
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