# gonum/gonum

Gonum is a set of numeric libraries for the Go programming language. It contains libraries for matrices, statistics, optimization, and more

Repository: https://github.com/gonum/gonum
Canonical: https://ross.abutalabs.com/products/gonum
Homepage: https://www.gonum.org/
Language: Go
License: BSD-3-Clause
License Family: permissive
Topics: go, golang, scientific-computing, data-analysis, matrix, statistics, graph
Last push: 2026-07-21T09:35:08+00:00

## Health v2 (maintenance only)
Score: 73/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 93, release rhythm 33, longevity 100
- inputs: {"age_days": 3448, "days_push": 43, "days_rel": 235, "gap_med": 295, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8427, forks 581 (observed 2026-08-28T04:10:21.142884+00:00)

## What it is
Gonum is a suite of numerical and scientific computing libraries for Go, covering matrices and linear algebra, statistics and probability, optimization, differentiation, integration, and graph/network analysis. It is written in pure Go with optional assembly for performance and follows a regular release schedule aligned with Go releases.

## Use cases
- perform matrix and linear algebra operations in Go
- compute statistics and sample from probability distributions
- optimize or integrate numerical functions
- build and analyze graphs and networks
- do scientific computing and data analysis in Go without C dependencies

## When to choose
- you need performant numerical code in Go with transparent, pure-Go sources
- you want composable, well-tested scientific libraries with a regular release cadence
- you need graph algorithms or statistics alongside linear algebra in one ecosystem

## When to avoid
- you need GPU-accelerated deep learning or tensor frameworks like PyTorch
- you require the breadth of SciPy/NumPy ecosystems or their tooling
- you need guaranteed identical floating-point results across architectures

## Facets
- artifact type: library
- maturity: active
- function: math, data-science, machine-learning, graphics
- domain: data-science, mathematics, developer-tools
- platform: go, windows, cross-platform
- tags: linear-algebra, statistics, optimization, graph-analysis, scientific-computing, matrices, numerical-methods, algorithms, linux, macos

## Member repositories
- gonum/gonum (main) score 73

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:21.142884+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 2026-08-29T17:27:15.749645+00:00, confidence not recorded.
  - readme: https://github.com/gonum/gonum (fetched 2026-08-28T04:10:21.142884+00:00, sha 1806ee5940db)
  - homepage: https://www.gonum.org/ (fetched 2026-08-29T08:27:08.625919+00:00, sha 5ba091fe3ee8)
  - site_page: https://www.gonum.org/talk/accidental (fetched 2026-08-29T08:27:08.628532+00:00, sha 684ff7c0d909)
- Data as of 2026-08-30T08:39:29.467469+00:00.