recommend_stat: Recommend Test Statistics via Offline KB Matching

In EventStudy: Event Study Analysis

Recommend Test Statistics via Offline KB Matching

Description

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).

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".

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).

See Also

flag_robustness, es_diagnostics, es_kb

Other eventstudy-advisor: AnthropicProvider, CustomProvider, OpenAICompatProvider, ProviderBase, es_advise(), es_diagnostics(), es_kb(), flag_robustness(), provider()

Examples

data(dieselgate)
task <- EventStudyTask$new(dieselgate$firm, dieselgate$index, dieselgate$request)
task <- run_event_study(task, ParameterSet$new())
advice <- recommend_stat(task)
print(advice)

EventStudy documentation built on Oct. 31, 2026, 1:07 a.m.