API Reference

The package exports the main workflow helpers from marketbayesmeta.

Tracker and readiness

  • load_tracker_csv
  • assess_model_readiness
  • tracker_quality_issues
  • readiness_frame
  • tracker_issue_frame

Model input and fitting

  • make_meta_analysis_input
  • make_effect_preparation_rows
  • fit_random_effects

Reporting and diagnostics

  • summarise_effect
  • summarise_diagnostics
  • check_diagnostics
  • make_posterior_predictive_check_rows
  • posterior_predictive_check_frame

Priors and sensitivity

  • default_priors_for_scale
  • assess_prior_influence
  • fit_prior_sensitivity
  • make_prior_sensitivity_report_frame
  • make_sensitivity_inputs
  • make_sensitivity_report_frame

Config runner

  • load_config
  • run_analysis
  • run_config

Minimal Python example

from marketbayesmeta import EffectScale, EvidenceType
from marketbayesmeta import fit_random_effects, load_tracker_csv, make_meta_analysis_input
from marketbayesmeta.reporting import summarise_effect

dataset = load_tracker_csv("examples/example_tracker.csv")
model_input = make_meta_analysis_input(
    dataset,
    evidence_type=EvidenceType.GEO_TEST,
    scale=EffectScale.LOG_RELATIVE,
    metric="Sales uplift",
    include_partial=True,
)

result = fit_random_effects(model_input, random_seed=20260521)
print(summarise_effect(result.idata, scale=model_input.scale))