# JuliaStats/Distributions.jl

A Julia package for probability distributions and associated functions.

Repository: https://github.com/JuliaStats/Distributions.jl
Canonical: https://ross.abutalabs.com/products/distributionsjl
Language: Julia
License: NOASSERTION
License Family: other
Topics: julia, statistics, data-science, probability-distributions
Last push: 2026-08-20T12:08:03+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": 5045, "days_push": 13, "days_rel": 13, "gap_med": 24, "n_releases_24m": 20}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1197, forks 444 (observed 2026-08-28T04:03:57.419762+00:00)

## What it is
A Julia package providing a comprehensive collection of probability distributions and associated functions. It implements moments, entropy, density/mass functions, moment generating and characteristic functions, sampling, and maximum likelihood estimation.

## Use cases
- sample random numbers from a probability distribution in Julia
- compute pdf, logpdf, mean, variance, skewness, and kurtosis of a distribution
- fit distributions to data via maximum likelihood estimation
- evaluate moment generating and characteristic functions
- model uncertainty in statistical simulations
- work with entropy and other distribution properties

## When to choose
- you need a broad, well-tested library of probability distributions in Julia
- you are doing statistical modeling, simulation, or Bayesian work in Julia
- you need sampling, moments, and density functions with a consistent API

## When to avoid
- you need conjugate prior functionality (moved to ConjugatePriors.jl)
- you are working in a language other than Julia
- you need deep learning specific distributions or GPU-native sampling

## Facets
- artifact type: library
- maturity: stable
- function: math, data-science, machine-learning
- domain: data-science, mathematics
- platform: -
- tags: probability-distributions, sampling, maximum-likelihood, julia, statistics

## Member repositories
- JuliaStats/Distributions.jl (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:57.419762+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 2026-08-30T06:21:17.201060+00:00, confidence not recorded.
  - readme: https://github.com/JuliaStats/Distributions.jl (fetched 2026-08-28T04:03:57.419762+00:00, sha 79eeb2689b21)
- Data as of 2026-08-30T08:39:29.467469+00:00.