Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 23:46 UTC

bashtage / linearmodels

Additional linear models including instrumental variable and panel data models that are missing from statsmodels.

PythonNCSA★ 1,070 stars⑂ 200 forkssince Feb 2017View on GitHub ↗

bashtage/linearmodels holds a health index of 86 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (90/100) and lowest on Sustainability & Governance (63/100). It was last updated 2 days ago. A single contributor accounts for most of its recent work.

overall / 100

Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met

Excellent80-92Strong across the board; minor gaps

Good65-79Healthy; gaps are limited and manageable

Moderate50-64Acceptable with notable gaps; review recommended

Weak35-49Material weaknesses across several areas

At Risk20-34Significant weaknesses; adoption warrants caution

Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 72 is calibrated to 86 on the published index scale (record calibration 2026-08-02).

Ownership

Kevin SheppardPersonal account

1,450 followers40 public repossince Oct 2013

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

Registry	Package	Version	Downloads / mo	Versions	Last publish	Tags
PyPI	linearmodels	7.0	-	45	331 days ago	linear-models regression instrumental-variables iv panel fixed-effects clustered heteroskedasticity endogeneity instruments statistics statistical-inference econometrics

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

77Good · 21% of overall

Development activity

82Excellent

How it's scored

36/36	Push recency — last push 2 days ago
18/36	Commit cadence — 26/52 weeks with commits
18/18	Commit volume — 136 commits in the last year
10/10	OpenSSF Scorecard: Maintained — 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10

Inputs used

commits_last_year	136
human_commit_share	0.82
days_since_last_push	2
active_weeks_last_year	26

Release discipline

70Good

How it's scored

27/27	Ships releases — 46 releases published
16.2/36	Release recency — latest release 331 days ago
19.8/27	Release cadence — a release every ~101 days
0/10	OpenSSF Scorecard: Signed-Releases — no data

Inputs used

releases_count	46
latest_release_tag	v7.0
releases_from_tags	no
days_since_latest_release	331
mean_days_between_releases	101

Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

64Moderate · 17% of overall

Popularity & adoption

76Good

How it's scored

49.1/60	Stars — 1,070 stars
19.2/25	Forks — 200 forks
7.5/15	Watchers — 23 watchers

Inputs used

forks	200
stars	1,070
watchers	23
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history

Community health

50Moderate

How it's scored

22.5/22.5	README
22.5/22.5	License — recognized license (NCSA)
0/18	CONTRIBUTING guide
0/13.5	Code of conduct
0/7.2	Issue template
0/6.3	PR template

Inputs used

has_readme	yes
has_license	yes
readme_badges	3
has_contributing	no
has_issue_template	no
has_code_of_conduct	no
readme_badge_services	badge.fury.io, codecov.io, dev.azure.com
has_pull_request_template	no

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

63Moderate · 23% of overall

Maintainer resilience (bus factor)

29At Risk

How it's scored

9/54	Bus factor — 1 contributor(s) cover half of all commits
0.7/22.5	Commit distribution — top contributor authored 97% of commits
13.5/13.5	Contributor breadth — 19 contributors
6/10	OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6

Inputs used

bus_factor	1
contributors_sampled	19
top_contributor_share	0.97

Issue & PR responsiveness

73Good

How it's scored

32.6/42	Issue resolution — 78% of issues closed
24.5/30	PR acceptance — 433/530 decided PRs merged
13/13	Newcomer PR acceptance — 2/2 first-time contributors' PRs merged in 30d
3/15	OpenSSF Scorecard: Code-Review — Found 2/10 approved changesets -- score normalized to 2

Inputs used

merged_prs	433
open_issues	41
closed_issues	143
prs_merged_7d	3
prs_decided_7d	3
prs_merged_30d	3
prs_decided_30d	3
issue_closed_ratio	0.777
closed_unmerged_prs	97
first_time_authors_30d	2
first_time_prs_merged_30d	2
first_time_prs_decided_30d	2

Ownership & stewardship

70Good

How it's scored

10/30	Ownership backing — personal (user) account
0/20	Verified domain — not applicable to user accounts
22.7/25	Owner reach — 1,450 followers of bashtage
23.7/25	Track record — 40 public repos, account ~12 yr old

Inputs used

followers	1,450
owner_type	User
is_verified	—
owner_login	bashtage
public_repos	40
account_age_days	4,734

Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

91Excellent

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
26/35	Publish recency — latest publish 331 days ago
20/20	Version history — 45 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	linearmodels
ecosystems	pypi
any_deprecated	no
min_days_since_publish	331

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 19% of overall

Engineering practices

84Excellent

How it's scored

24/24	CI workflows — 3 workflow(s)
24/24	Tests present
16/16	Linter config — .flake8, pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
0/9.6	Pre-commit hooks
0/6.4	.editorconfig
20/20	OpenSSF Scorecard: CI-Tests — 15 out of 15 merged PRs checked by a CI test -- score normalized to 10

Inputs used

has_ci	yes
has_tests	yes
has_editorconfig	no
has_linter_config	yes
has_precommit_config	no

Documentation

100Exceptional

How it's scored

30/30	README
25/25	Documentation directory
15/15	Documentation / homepage site — https://bashtage.github.io/linearmodels/
10/10	Repository description
10/10	Topics — 20 topics
10/10	Wiki

Inputs used

topics	iv, instrumental-variable, panel, regression, statistical-model, ols, gmm, fixed-effects, random-effects, between-estimator, first-difference, clustered-standard-errors, pooled-ols, linear-models, panel-data, panel-models, panel-regression, fama-macbeth, asset-pricing, seemingly-unrelated-regression
has_wiki	yes
homepage	https://bashtage.github.io/linearmodels/
docs_site	https://bashtage.github.io/linearmodels/
has_readme	yes
has_docs_dir	yes
has_description	yes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

69Good · 16% of overall

Security posture

61Moderate

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
0/7.5	Branch-Protection — no data
2.5/2.5	CI-Tests — 15 out of 15 merged PRs checked by a CI test -- score normalized to 10
0/2.5	CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
1.5/7.5	Code-Review — Found 2/10 approved changesets -- score normalized to 2
1.5/2.5	Contributors — project has 2 contributing companies or organizations -- score normalized to 6
10/10	Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5	Dependency-Update-Tool — update tool detected
0/5	Fuzzing — project is not fuzzed
2.5/2.5	License — license file detected
7.5/7.5	Maintained — 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
0/5	Packaging — no data
0/5	Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
4.5/5	SAST — SAST tool detected but not run on all commits
0/5	Security-Policy — security policy file not detected
0/7.5	Signed-Releases — no data
0/7.5	Token-Permissions — detected GitHub workflow tokens with excessive permissions
6.8/7.5	Vulnerabilities — 1 existing vulnerabilities detected

Inputs used

source	openssf_scorecard
checks_evaluated	15
scorecard_version	v5.5.0
checks_inconclusive	3
scorecard_aggregate	6.1

Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional

How it's scored

35/35	Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25	Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40	No advisories left outstanding — no advisory carries a publication date

Inputs used

source	osv
advisories	0
affected_packages	0
assessed_packages	15
unassessed_packages	0
affected_by_severity	none
direct_affected_packages	0

Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:linearmodels@7.0 runtime dependency closure — what installing the published package pulls in — 15 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

67Good · 4% of overall

Agent context & guidance

38Weak

How it's scored

0/45	Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15	Machine-readable docs (llms.txt)
37.7/40	Legible commit history — 58 of 82 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	no
llms_txt_url	—
legible_history_share	0.707
agent_instruction_files	—
agent_instruction_max_bytes	—

Verify loop (build / test / typecheck)

70Good

How it's scored

18/18	One-command bootstrap — doc/Makefile
22/22	Automated tests
11/11	Lint / format config — .flake8, pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
11/11	Static type checking — linearmodels/py.typed
0/10	Reproducible environment
0/10	Demonstrated agent practice — no agent-authored commits among the last 100
8/8	Automated maintenance — 18 of the last 100 commits are automated dependency updates
0/10	OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0

Inputs used

has_nix	no
has_tests	yes
lockfiles	—
has_dockerfile	no
typed_language	no
bootstrap_files	doc/Makefile
has_devcontainer	no
has_linter_config	yes
typecheck_configs	linearmodels/py.typed
agent_commit_share	0
toolchain_manifests	—
dependency_bot_commit_share	0.18

Code legibility for models

81Excellent

How it's scored

27/45	Type-checkable code — Python with type-check config (linearmodels/py.typed)
54.2/55	Manageable file sizes — 2/131 source files over 60KB

Inputs used

primary_language	Python
largest_source_bytes	118,971
source_files_sampled	131
oversized_source_files	2

Machine-readable interfaces

100Exceptional

How it's scored

0/40	API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20	MCP server — not applicable to this kind of software
40/40	Runnable examples — examples, notebooks

Inputs used

example_dirs	examples, notebooks
has_mcp_signal	no
api_schema_files	—
interfaces_expected_of	—

Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

1,070GitHub stars

19contributors

136commits, last 12 months

2days since last push

46releases

1bus factor

41open issues

PyPIpackage ecosystems

Data collection warnings

Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
pypi download statistics for 'linearmodels' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced

More detail

Star and fork history 0 ★ / 200 ⇿

0Stars

200Forks

44Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

Major 0Minor 0Patch 4

Each point covers 9 days.

OpenSSF Scorecard 6.1 / 10

6.1aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-09-17 23:45 UTC

10	Binary-Artifacts	no binaries found in the repo
n/a	Branch-Protection	internal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
10	CI-Tests	15 out of 15 merged PRs checked by a CI test -- score normalized to 10
0	CII-Best-Practices	no effort to earn an OpenSSF best practices badge detected
2	Code-Review	Found 2/10 approved changesets -- score normalized to 2
6	Contributors	project has 2 contributing companies or organizations -- score normalized to 6
10	Dangerous-Workflow	no dangerous workflow patterns detected
10	Dependency-Update-Tool	update tool detected
0	Fuzzing	project is not fuzzed
10	License	license file detected
10	Maintained	30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
n/a	Packaging	packaging workflow not detected
0	Pinned-Dependencies	dependency not pinned by hash detected -- score normalized to 0
9	SAST	SAST tool detected but not run on all commits
0	Security-Policy	security policy file not detected
n/a	Signed-Releases	no releases found
0	Token-Permissions	detected GitHub workflow tokens with excessive permissions
9	Vulnerabilities	1 existing vulnerabilities detected

Direct dependencies 7

Registry	Package	Version constraint	Manifest
PyPI	numpy	`>=1.22.3,<3`	pyproject.toml
PyPI	pandas	`>=1.4.0`	pyproject.toml
PyPI	scipy	`>=1.8.0`	pyproject.toml
PyPI	statsmodels	`>=0.13.0`	pyproject.toml
PyPI	mypy_extensions	`>=0.4`	pyproject.toml
PyPI	pyhdfe	`>=0.1`	pyproject.toml
PyPI	formulaic	`>=1.2.1`	pyproject.toml

All dependencies 58

Full resolved dependency set from the GitHub dependency graph: 7 direct and 51 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	formulaic	`—`	direct
PyPI	mypy-extensions	`—`	direct
PyPI	numpy	`—`	direct
PyPI	pandas	`—`	direct
PyPI	pyhdfe	`—`	direct
PyPI	scipy	`—`	direct
PyPI	statsmodels	`—`	direct
PyPI	black	`—`	indirect
PyPI	colorama	`—`	indirect
PyPI	coverage	`—`	indirect
PyPI	cython	`—`	indirect
PyPI	flake8	`—`	indirect
PyPI	flake8-bugbear	`—`	indirect
PyPI	fonttools	`—`	indirect
PyPI	ipython	`—`	indirect
PyPI	isort	`—`	indirect
PyPI	jupyter	`—`	indirect
PyPI	jupyter-server	`—`	indirect
PyPI	jupyterlab	`—`	indirect
PyPI	jupyterlab-code-formatter	`—`	indirect
PyPI	matplotlib	`—`	indirect
PyPI	meson	`—`	indirect
PyPI	meson-python	`—`	indirect
PyPI	mistune	`—`	indirect
PyPI	mypy	`—`	indirect
PyPI	nbconvert	`—`	indirect
PyPI	nbformat	`—`	indirect
PyPI	nbsphinx	`—`	indirect
PyPI	ninja	`—`	indirect
PyPI	notebook	`—`	indirect
PyPI	numba	`—`	indirect
PyPI	numpydoc	`—`	indirect
PyPI	packaging	`—`	indirect
PyPI	pandas-stubs	`—`	indirect
PyPI	patsy	`—`	indirect
PyPI	pillow	`—`	indirect
PyPI	pyarrow	`—`	indirect
PyPI	pytest	`—`	indirect
PyPI	pytest-cov	`—`	indirect
PyPI	pytest-randomly	`—`	indirect
PyPI	pytest-xdist	`—`	indirect
PyPI	pyupgrade	`—`	indirect
PyPI	ruff	`—`	indirect
PyPI	scipy-stubs	`—`	indirect
PyPI	seaborn	`—`	indirect
PyPI	setuptools	`—`	indirect
PyPI	setuptools-scm	`—`	indirect
PyPI	soupsieve	`—`	indirect
PyPI	sphinx	`—`	indirect
PyPI	sphinx-autodoc-typehints	`—`	indirect
PyPI	sphinx-immaterial	`—`	indirect
PyPI	sphinxcontrib-spelling	`—`	indirect
PyPI	tornado	`—`	indirect
PyPI	urllib3	`—`	indirect
PyPI	wheel	`—`	indirect
PyPI	wrapt	`—`	indirect
PyPI	xarray	`—`	indirect
PyPI	zipp	`—`	indirect

Dependency advisories 0

Installing pypi:linearmodels@7.0 pulls in 15 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.

Software health index

Score profile

Ownership

Package ecosystems

Metrics by category

Key facts

Data collection warnings

More detail

Feedback

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