MaintainerPulse

temporal prediction of dependency maintenance risk

pybigtools jackh726/bigtools ● elevated

54%

no release in the next 12 months

67%

new issues will go unanswered (30 days)

100%

maintainer activity collapse within 12 months

45%

chance of a release within 12 months (survival model)

Model risk over time

calibrated P(no release next 12mo) at each historical monthly snapshot

Repository activity, last 36 months

pushes issues PyPI release

What drives this score

TreeSHAP contributions; raises risk / lowers risk

Survival curve

P(still no release) m months ahead, discrete-time hazard model

What if? — poke the model

drag a signal and the deployed models rescore this package live (implied signals move together — two years without a release also zeroes "releases, last 12mo"). The needle tracks the activity-collapse model, a logistic regression: monotone by construction, so it responds smoothly to counterfactuals where the tree models step.

maintainer activity collapse, 12mo

Maintained alternatives

similar packages with low predicted risk; ranked by summary similarity blended with shared-dependents overlap, validated against known migrations

package	what it is	similarity	its 12mo risk
fastdigest	A fast t-digest library for Python built on Rust.	0.70	29%
fastwarc	The world's fastest WARC parsing library written in Rust with bindings for Python.	0.62	2%
rustworkx	A High-Performance Graph Library for Python	0.60	25%
uv	An extremely fast Python package and project manager, written in Rust.	0.60	0%
libipld	Python binding to the Rust IPLD library	0.59	9%

Signals at the latest snapshot

days since last release	150	releases, last 12mo	2
people pushing, last 12mo	2	bus factor (top pusher share)	67%
issues opened, last 12mo	2	30-day response rate	0%
stars accumulated	106	open vulns without fix	0

Probabilities are isotonic-calibrated on a held-out validation year and evaluated on future snapshots the models never saw (PR-AUC 0.894, precision@50 = 1.00 on the headline target).