A Python library for efficient Bayesian modeling with deep learning
C-Rank Grade: A (Welcoming) - 62/100
External PR Merge Rate: 86%
Response Time: 2d
First Timer Success: 56%
bayesflow-org/bayesflow has a recorded first-timer success rate of 55.6%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in bayesflow-org/bayesflow respond to incoming external pull requests in approximately 48.7 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 61.7 places bayesflow-org/bayesflow in the A tier.
The external contributor pull request merge rate for bayesflow-org/bayesflow is 86.3%, based on public PR activity from non-core contributors.
bayesflow-org/bayesflow 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.
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Is your feature request related to a problem? Please describe. Special support for .h5 files would be nice. Currently, the user has to supply this themselves, as it is not a case covered by the generic DiskDataset. Describe the solution you'd like Given a single .h5 file with appropriate internal structure, the H5Dataset class should be able to load batches of simulations without further help from the user. Describe alternatives you've considered We currently don't provide alternatives. Additional context We should make sure the "appropriate internal structure" of the .h5 file is easy to achieve and is according to best-practices with HDF5, particularly with respect to varying sequence-lengths of the simulations. We could provide helper methods to save simulations in such a way that they are compatible with the dataset class.
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When evaluating whether to contribute to bayesflow-org/bayesflow, 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 bayesflow-org/bayesflow acknowledge new external contributions in approximately 2 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 86.3% were successfully merged into the primary branch.
bayesflow-org/bayesflow has a recorded first-timer success rate of 55.6%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~2 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 61.7 places bayesflow-org/bayesflow in the Welcoming tier.
The external pull request merge rate is 86.3%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, bayesflow-org/bayesflow 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 bayesflow-org/bayesflow 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.