# montanaflynn/stats

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

Repository: https://github.com/montanaflynn/stats
Canonical: https://ross.abutalabs.com/products/montanaflynn-stats
Language: Go
License: MIT
License Family: permissive
Topics: go, statistics, math, data, analytics, stats, rounding, machine-learning, algorithms
Last push: 2026-08-17T21:46:45+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 98, longevity 100
- inputs: {"age_days": 4278, "days_push": 16, "days_rel": 16, "gap_med": 2.0, "n_releases_24m": 11}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3025, forks 175 (observed 2026-08-28T04:07:38.642571+00:00)

## What it is
A comprehensive, dependency-free statistics library for Go providing descriptive statistics, correlation, regression, and related math functions. It is well tested and usable as a standard Go package/module.

## Use cases
- compute median, mean, and percentiles in Go
- calculate correlation and covariance of datasets
- fit linear regression in a Go program
- round and clean numeric data in Go
- get summary statistics without external dependencies

## When to choose
- you need statistics functions in a Go project with zero dependencies
- you want a small, well-tested, MIT-licensed stats package
- you need flexible input handling for mixed numeric data types

## When to avoid
- you need advanced statistical modeling or distributions beyond descriptive stats
- you are working in a language other than Go
- you need GPU-accelerated or large-scale numerical computing

## Facets
- artifact type: library
- maturity: stable
- function: math, data-science, analytics
- domain: data-science, analytics
- platform: go, cross-platform
- tags: statistics, no-dependencies, descriptive-statistics, regression, algorithms

## Member repositories
- montanaflynn/stats (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:38.642571+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:29:54.469394+00:00, confidence not recorded.
  - readme: https://github.com/montanaflynn/stats (fetched 2026-08-28T04:07:38.642571+00:00, sha bed3d3544a0a)
- Data as of 2026-08-30T08:39:29.467469+00:00.