facebookexperimental/Robyn
Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community. observed · 2026-08-28
Health v2 · maintenance only
52/100
- Activity 64
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2316
- days_rel: 622
- days_push: 219
- n_releases_24m: 1
Adoption not part of the score
1508 stars · 430 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Robyn is Meta Marketing Science's open-source, semi-automated Marketing Mix Modeling (MMM) package available in R and Python. It uses ridge regression, evolutionary hyperparameter optimization (Nevergrad), Prophet-based time-series decomposition, and gradient-based budget allocation to measure media channel effectiveness in a privacy-safe way.
Use cases
- build a marketing mix model to measure ad channel ROI
- allocate marketing budget across media channels optimally
- model adstock and saturation curves for digital campaigns
- run privacy-safe marketing measurement without cookies or PII
- decompose sales trends, seasonality, and holiday effects
- calibrate MMM against lift tests and ground-truth experiments
When to choose
- you have granular time-series marketing spend and outcome data
- you need a free, open-source, privacy-friendly alternative to paid MMM vendors
- you want semi-automated modeling with reduced human bias
- you work in R or Python and want community-supported marketing science tooling
When to avoid
- you need individual-level or real-time attribution rather than aggregate modeling
- your dataset is small or lacks sufficient historical media spend data
- you need a fully supported commercial product with vendor guarantees
- you require the Python version to be fully production-hardened (it is a beta LLM translation)
Facets
library · maturity active
machine-learning data-science analytics data-science analytics python cross-platform marketing-mix-modeling mmm adstock budget-allocation ridge-regression nevergrad prophet marketing-science econometrics hyperparameter-optimization optimization marketing r
2 sources
- readme: https://github.com/facebookexperimental/Robyn · fetched 2026-08-28 · d30cbf6f83af
- homepage: https://facebookexperimental.github.io/Robyn/ · fetched 2026-08-29 · 18fa3acb8f83
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| facebookexperimental/Robyn | main | 52 |
For agents
markdown · JSON · MCP: product_card(name="facebookexperimental/Robyn")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem