Recommend Test Statistics via Offline KB Matching

Evaluates the KB decision table (category "stat_choice" rules) against the diagnostic signals extracted from a fitted EventStudyTask or a precomputed es_diagnostics object, and returns a severity-ranked es_advice S3 object.

Usage

recommend_stat(x, provider = NULL, ...)

# Default S3 method
recommend_stat(x, provider = NULL, ...)

# S3 method for class 'EventStudyTask'
recommend_stat(x, provider = NULL, ...)

# S3 method for class 'es_diagnostics'
recommend_stat(x, provider = NULL, ...)

Arguments

x

A fitted EventStudyTask (after fit_model()) or a precomputed es_diagnostics object returned by es_diagnostics().

provider

Accepted but ignored in the offline path — present only so the Phase 7 call shape is forward-compatible. Default NULL.

...

Additional arguments (currently ignored).

Value

An S3 object of class "es_advice" — a named list with:

source

"offline_kb" (character).

is_deterministic

TRUE — advice is rule-based, not LLM-generated.

rules_matched

Named list of matched rule records (severity-ranked: "error" first, then "warning", then "info"), each with fields id, recommendation, citation (list of author/year/key/venue), severity, category.

diagnostics_ref

The es_diagnostics list that was evaluated (possibly computed on-the-fly from the task).

Details

No LLM provider, network connection, or API key is required. Both functions are the always-available offline grounding layer (ADV-08). The returned es_advice object has the same shape as the Phase 7 Advice contract, flagged is_deterministic = TRUE and source = "offline_kb".

See also

flag_robustness, es_diagnostics, es_kb

Examples

if (FALSE) { # \dontrun{
task <- run_event_study(my_task, ParameterSet$new())
advice <- recommend_stat(task)
print(advice)
} # }