# easystats/easystats

:milky_way: The R easystats-project

Repository: https://github.com/easystats/easystats
Canonical: https://ross.abutalabs.com/products/easystats
Homepage: https://easystats.github.io/easystats/
Language: R
License: NOASSERTION
License Family: other
Topics: easystats, r, rstats, models, statistics, regression-models, performance-metrics, datascience, dataanalytics, hacktoberfest
Last push: 2026-08-05T10:10:12+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 96, release rhythm 16, longevity 100
- inputs: {"age_days": 2774, "days_push": 28, "days_rel": 418, "gap_med": 154, "n_releases_24m": 2}
- 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 1159, forks 88 (observed 2026-08-28T04:03:48.634475+00:00)

## What it is
easystats is a collection of R packages providing a unifying, consistent framework for statistical modeling, visualization, and reporting. It offers a cohesive ecosystem with shared syntax covering data exploration, model fitting, diagnostics, interpretation, and result communication.

## Use cases
- run statistical analyses in R with consistent syntax
- diagnose and check regression model performance
- visualize statistical model results
- interpret model coefficients and effects
- build reproducible statistical reporting workflows
- extract and standardize model information across R model classes

## When to choose
- you do statistical modeling in R and want a unified, beginner-friendly ecosystem
- you need consistent diagnostics, visualization, and reporting across many model types
- you want lightweight modular packages to integrate into other R tooling

## When to avoid
- you need machine learning pipelines rather than statistical modeling
- you work outside R
- you need specialized Bayesian or bespoke analysis not covered by the ecosystem

## Facets
- artifact type: framework
- maturity: active
- function: data-science, data-visualization, math
- domain: data-science, data-visualization, developer-tools
- platform: cross-platform
- tags: rstats, statistics, regression-models, model-diagnostics, meta-package, cran, r

## Member repositories
- easystats/easystats (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:48.634475+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 2026-08-30T06:31:53.325538+00:00, confidence not recorded.
  - readme: https://github.com/easystats/easystats (fetched 2026-08-28T04:03:48.634475+00:00, sha f5b7c5d945e1)
  - homepage: https://easystats.github.io/easystats/ (fetched 2026-08-29T12:36:27.107611+00:00, sha f53fa3df04db)
- Data as of 2026-08-30T08:39:29.467469+00:00.