Open source alternatives to statsmodels
24 open source AI Skills apps to use instead, best rated first.
Top picks
- G
github-ops
Launchgithub-ops is an agent skill from the ECC collection by affaan-m, listed on skills.sh. It manages a GitHub repository with a focus on community health, CI reliability and the experience of contributors. It covers triaging issues by classify
Open source
- O
opensource-pipeline
Launchopensource-pipeline is an agent skill from the ECC collection by affaan-m, listed on skills.sh. It prepares a private project for safe public release through a three-stage pipeline. The fork stage strips secrets from a copy. The sanitise st
Open source
- A
andrej-karpathy-skills
Launchandrej-karpathy-skills is a single CLAUDE.md file from the multica-ai project that is meant to improve how Claude Code behaves on coding tasks. It is derived from Andrej Karpathy's public observations about LLM coding pitfalls. The file dis
Open source
- M
microsoft/markitdown
Launchmarkitdown-mcp is the MCP server package for MarkItDown, Microsoft's tool for converting many file formats to Markdown for language models. It is a lightweight server that runs over stdio, Streamable HTTP or SSE, and exposes one tool, conve
Open source
- O
od-contribute
Launchod-contribute is an agent skill in the OpenDesign project that provides a guided, one-click contribution flow to the OpenDesign repository, written so that people without a coding background can send a real pull request. It is locked to the
Open source
- G
github-deep-research
Launchgithub-deep-research is an agent skill from the DeerFlow project that researches a single GitHub repository in several rounds and writes a report in Markdown. It is for requests that need more than a README summary, such as a comprehensive
Open source
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About statsmodels
statsmodels
Fits and diagnoses Python statistical models including OLS, GLM
statsmodels is an agent skill for fitting and diagnosing statistical models in Python with the statsmodels library. It covers regression, time series and econometric models, including OLS, GLM, discrete and mixed models, ARIMA and SARIMAX. It also covers coefficient inference, marginal effects and model diagnostics. The skill is clear about what a fit shows. A successful fit establishes that the numerical procedure ran, but causal identification, calibrated uncertainty and model adequacy depend on a defensible study design and assumptions. The skill was reviewed against statsmodels 0.15.0, released on 27 August 2026. Native checks used Python 3.13, NumPy 2.5.3, SciPy 1.18.1, pandas 3.0.6, matplotlib 3.11.2 and scikit-learn 1.9.1, in a dedicated environment. It recommends the stable high-level imports from statsmodels.api and statsmodels.formula.api, with direct module imports where newer or specialised classes are needed. In version 0.15, the skill advises using result_object=True with named fields for the ADF and KPSS tests, and rng= where older versions accepted seed or random_state.
- Price
- Free
- Made by
- k-dense-ai
- Category
- AI Skills
- Website
- github.com/k-dense-ai/scientific-agent-skills/statsmodels