/reference/index.html Exact operational and technical reference for marketbayesmeta. Pages CLI Configuration Outputs API Hugo en-gb /reference/cli/index.html Mon, 01 Jan 0001 00:00:00 +0000 /reference/cli/index.html marketbayesmeta installs three console scripts. Check a tracker marketbayesmeta-check-tracker path/to/tracker.csv marketbayesmeta-check-tracker path/to/tracker.csv --audit --include-partial The audit mode reports model-readiness and tracker-quality checks for supported evidence/scale pairs present in the tracker. Check a config marketbayesmeta-check-config examples/config.yaml This validates the YAML schema and confirms that the configured tracker path exists. /reference/configuration/index.html Mon, 01 Jan 0001 00:00:00 +0000 /reference/configuration/index.html Use YAML config files for repeatable analysis runs. Core fields Field Required Default Notes project.name No marketbayesmeta_analysis Human-readable project name. project.analyst No null Analyst or owner. project.notes No "" Short project note. data.tracker Yes None CSV tracker path, resolved relative to the config file. data.include_partial No false Include analysis_ready=partial rows. analysis.evidence_type Yes None Currently pooled by default: geo_test, bls. analysis.metric Yes None Exact metric name to pool. analysis.scale Yes None log_relative or percentage_point. analysis.max_abs_percent_uplift No 90 Hard limit for % geo uplift to log-relative conversion. model.draws No 2000 Posterior draws per chain. model.tune No 1000 Tuning draws per chain. model.chains No 4 Number of chains. model.target_accept No 0.9 Passed to PyMC sampling. model.random_seed No null Sampling seed. model.progressbar No false Keep false for scripted runs. priors.mu_sd No scale-aware Prior SD for pooled mean mu. priors.tau_scale No scale-aware HalfNormal scale for heterogeneity tau. diagnostics.allow_directional No false Allow directional readiness runs only for exploratory work. diagnostics.allow_not_recommended No false Override only for explicit exploratory work. diagnostics.fail_on_diagnostic_failure No true Make failed sampler diagnostics blocking. outputs.directory No output/analysis Output folder, resolved relative to config file. Uncertainty fields Rows with intervals must state ci_type. They must also state ci_level unless uncertainty.default_ci_level is explicitly set in config. /reference/outputs/index.html Mon, 01 Jan 0001 00:00:00 +0000 /reference/outputs/index.html Config runs write a small artefact set intended for review and downstream reporting. File Purpose config.resolved.yaml Config with defaults made explicit. model_input.csv Effects and standard errors used for modelling. effect_preparation.csv Row-level source values, uncertainty provenance, transformations, exclusions, and warnings. readiness.csv Model-readiness status and messages. tracker_issues.csv Tracker quality warnings/errors. effect_summary.csv Pooled mean posterior summary. future_true_effect_summary.csv Latent true effect for a comparable future study. analysis_report.md Analyst-facing summary of reportability, diagnostics, readiness, and headline effects. diagnostics.csv Sampler diagnostic checks. prior_diagnostics.csv Approximate prior-information warning for mu plus tau prior-predictive checks. ppc.csv In-sample study-level posterior predictive checks. run_status.json Machine-readable run outcome, including reportable. outputs_manifest.json Machine-readable artefact list. Optional files: /reference/api/index.html Mon, 01 Jan 0001 00:00:00 +0000 /reference/api/index.html 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))