montanaflynn/stats

A well tested and comprehensive Golang statistics library package with no dependencies.

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montanaflynn/stats has 3k+ stars and 150+ forks on GitHub, is written in Go, and was last pushed on Oct 6, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 29 minutes ago
Added to GitGenius on September 22nd, 2026
Created on December 16th, 2014
Open Issues & Pull Requests: 1 (+0)
GitHub issues: Enabled
Number of forks: 177
Total Stargazers: 3,030 (+0)
Total Subscribers: 50 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 5.0 days
Mean response time: 289.9 days
90th percentile: 839.2 days
Tracked items: 18

Maintainer activity

1 person did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 11, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits. How this is counted.

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Issue Activity (beta)

Open issues: 1
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 616 days
Stale 30+ days: 1
Stale 90+ days: 1

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • enhancement (8)
  • help wanted (3)
  • v2 (2)

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Detailed Description

Stats is a Go statistics library package that provides comprehensive statistical functions with no external dependencies.

The library solves the need for statistical calculations in Go applications by offering a collection of well-tested functions covering common statistical operations. It bundles algorithms for analytics and data analysis into a single importable package, eliminating the need to implement these functions from scratch or depend on external libraries.

Developers working on data analysis, machine learning, or analytics features in Go should consider this library. It suits any Go project requiring statistical computations where keeping dependencies minimal is a priority. The package is particularly valuable for applications that need reliable, tested statistical functions without introducing additional dependency management overhead.

The project maintains rigorous code quality standards through mandatory test coverage at one hundred percent and enforces conventional commit formatting for all changes. Pull requests are actively welcomed and the contribution process is well-documented with a Makefile providing helper targets for testing, linting, and coverage reporting. Releases are automated through GoReleaser and GitHub Actions, with changelog generation tied directly to commit message conventions. The maintainers require that new features include example code in the examples directory and corresponding documentation updates before acceptance.