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))