Abacus Documentation
ABACUS is a Bayesian MMM library built on PyMC and PyTensor.
The public PanelMMM API includes released named presets for one aggregate
time series, one-unit fixed effects (FE), and one-unit correlated random effects
(CRE). Start with Choose an Estimator
before preparing a panel model. The random-effects (re) preset remains
release-gated.
Start with a task
These Markdown pages describe the Abacus checkout they accompany. Open the section indexes directly in your repository viewer or editor; keep the library and documentation at the same revision.
| Task | Start here |
|---|---|
| Fit a first model in Python | Python quickstart |
| Execute a small disk-backed run | Bounded runner smoke |
| Choose an estimator and check its restrictions | Estimator selection |
| Evaluate predictions on a held-out time window | Blocked holdout validation |
| Check available scenario operations | Supported scenario surface |
| Run contributor checks | Local verification |
The YAML builder returns a built model for subsequent fitting and analysis. The runner executes a staged workflow and writes artefacts to disk. Their configuration surfaces differ; use the tutorial for the route you intend to run.
Documentation Sections
- Getting Started — Installation, quickstarts, first model
- Data Preparation — Input data requirements and layout
- Model Specification — Estimator choice,
PanelMMMequation, transforms, priors, and calibration - Model Fitting — Fitting, prior predictive checks, save/load
- Post-Modeling — Diagnostics, contributions, response curves, export
- Optimization — Budget allocation and interpretation
- Scenario Planning — Scenario specifications, Python and CLI workflows, comparison outputs
- Pipeline Runner — Structured runner, YAML config, staged outputs
- FAQ — Econometrics explainers for practitioners
- Contributing — Architecture, development setup, testing
- API Reference — Module and class reference