R: LLM Relational Event Models

# LLM Relational Event Models

## Documentation for package ‘llrem’ version 0.1.1

- DESCRIPTION file.
- Package NEWS.

## Help Pages

attach_covariates_df | Attach exogenous covariates to a pre-built risk-set data frame
compare_rem_models | Forest plot comparing fitted REM coefficients
compute_hazard_surface | Compute the full hazard surface over all directed dyads
compute_seq_stat_means | Compute sufficient-statistic means for a sequence without fitting a REM
cov_dyad_static | Create a static dyad-level covariate
cov_dyad_temporal | Create a temporal dyad-level covariate
cov_node_static | Create a static node-level covariate
cov_node_temporal | Create a temporal node-level covariate
edgelist | Militarized Interstate Dispute conflict edgelist
fit_rem | Fit a Relational Event Model to an edgelist
fit_rem_sep | Fit a Separable Relational Event Model
llm_call | Call an LLM provider
llm_provider_anthropic | Create an Anthropic Claude LLM provider
llm_provider_bedrock | Create an AWS Bedrock LLM provider
llm_provider_gemini | Create a Google Gemini LLM provider
llm_provider_grok | Create an xAI Grok LLM provider
llm_provider_mock | Create a mock LLM provider for testing
llm_provider_ollama | Create a local or remote Ollama LLM provider
llm_provider_openai | Create an OpenAI LLM provider
llm_provider_openai_compat | Create a generic OpenAI-compatible LLM provider
make_behavior_queries | Generate plain-language behavioral guidelines from empirical sufficient stats
make_query_predict | Query factory: LLM predicts the next event
make_query_roleplay_random | Query factory: LLM roleplays as a randomly assigned node
make_query_roleplay_sender | Query factory: LLM roleplays as the empirical sender
make_stat_means | Compute per-term mean sufficient statistics for realized events
make_suffstats | Extract sufficient-statistic means from a REM risk-set data frame
mock_strategy_highest_indegree | Mock strategy: pick the valid target with the highest in-degree
mock_strategy_max_id | Mock strategy: always pick the largest valid target
mock_strategy_min_id | Mock strategy: always pick the smallest valid target
n_nodes | Number of nodes in the MID edgelist
prepare_edgelist | Normalise a raw edgelist to the standard three-column format
rem_cfg | Create a prompt configuration object
rem_covariates | Collect covariate objects for use in fit_rem
run_llm_rem | Generate an LLM event sequence and fit a REM
run_multiagent_rem | Run a multi-agent REM-LLM conflict simulation