Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.
C-Rank Grade: A (Welcoming) - 61/100
External PR Merge Rate: 77%
Response Time: 1d
First Timer Success: 76%
pymc-labs/pymc-marketing has a recorded first-timer success rate of 76.5%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in pymc-labs/pymc-marketing respond to incoming external pull requests in approximately 34.6 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 60.6 places pymc-labs/pymc-marketing in the A tier.
The external contributor pull request merge rate for pymc-labs/pymc-marketing is 77.2%, based on public PR activity from non-core contributors.
pymc-labs/pymc-marketing currently has 1 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/pymc-labs/pymc-marketing?utm_source=github&utm_medium=badge)
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~22 hours (96% approval). Large PRs (>500 lines) take ~166 hours (35% approval). Keep your diff compact for rapid review.
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This notebook opens by saying that "This will be a three part series.". However, I don't see any other posts under the "vector autoregressive model" tag. I believe @NathanielF wrote this notebook. If no other posts are planned, can we edit this so that no one goes looking for "the other 2" notebooks (or consider writing the other 2 posts)?
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When evaluating whether to contribute to pymc-labs/pymc-marketing, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of pymc-labs/pymc-marketing acknowledge new external contributions in approximately 1 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 77.2% were successfully merged into the primary branch.
pymc-labs/pymc-marketing has a recorded first-timer success rate of 76.5%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~1 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 60.6 places pymc-labs/pymc-marketing in the Welcoming tier.
The external pull request merge rate is 77.2%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, pymc-labs/pymc-marketing currently has 1 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for pymc-labs/pymc-marketing are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.