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tooluniverse-statistical-modeling
维护者 FreedomIntelligence · 最近更新 2026年4月1日
tooluniverse-statistical-modeling:执行 statistical modeling 、 回归 analysis on biomedical 数据集s。 支持 linear 回归,logistic 回归 (binary/ordinal/multinomial),mixed-effects models,Cox proportional hazards survival analysis,Kaplan-Meier estimation,、 comprehensive model diagnostics。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-statistical-modeling
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Comprehensive statistical modeling skill ,用于 fitting 回归 models,survival models,、 mixed-effects models to biomedical data. Produces publication-quality statistical summaries ,支持 odds ratios,hazard ratios,confidence intervals,、 p-values。
- model = smf.logit('disease ~ exposure + age + sex',data=df).fit(disp=0)。
原始文档
SKILL.md 摘录
Features
✅ Linear Regression - OLS for continuous outcomes with diagnostic tests ✅ Logistic Regression - Binary, ordinal, and multinomial models with odds ratios ✅ Survival Analysis - Cox proportional hazards and Kaplan-Meier curves ✅ Mixed-Effects Models - LMM/GLMM for hierarchical/repeated measures data ✅ ANOVA - One-way/two-way ANOVA, per-feature ANOVA for omics data ✅ Model Diagnostics - Assumption checking, fit statistics, residual analysis ✅ Statistical Tests - t-tests, chi-square, Mann-Whitney, Kruskal-Wallis, etc.
Binary Logistic Regression
import statsmodels.formula.api as smf
import numpy as np
## Extract odds ratios
odds_ratios = np.exp(model.params)
conf_int = np.exp(model.conf_int())
print(f"Odds Ratio for exposure: {odds_ratios['exposure']:.4f}")
print(f"95% CI: ({conf_int.loc['exposure', 0]:.4f}, {conf_int.loc['exposure', 1]:.4f})")
print(f"P-value: {model.pvalues['exposure']:.6f}")
适用场景
- 适合在asked to fit 回归 models,compute odds ratios,perform survival analysis,run statistical tests,或 interpret model coefficients ,面向 provided data时使用。
不适用场景
- Do not rely on this catalog entry alone ,用于 installation 或 maintenance details。
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