tooluniverse-statistical-modeling
Perform statistical modeling and regression analysis on biomedical datasets. Supports linear regression, logistic regression (binary/ordinal/multinomial), mixed-effects models, Cox proportional hazards survival analysis, Kaplan-Meier estimation, and comprehensive model diagnostics. Extracts odds ratios, hazard ratios, confidence intervals, p-values, and effect sizes. Designed to solve BixBench statistical reasoning questions involving clinical/experimental data. Use when asked to fit regression models, compute odds ratios, perform survival analysis, run statistical tests, or interpret model co
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-statistical-modeling --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Statistical Modeling for Biomedical Data Analysis Comprehensive statistical modeling skill for fitting regression models, survival models, and mixed-effects models to biomedical data. Produces publication-quality statistical summaries with odds ratios, hazard ratios, confidence intervals, and p-values. ## 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. ## Quick Start ### Binary Logistic Regression ```python import statsmodels.formula.api as smf import numpy as np # Fit logistic regression model = smf.logit('disease ~ exposure + age + sex', data=df).fit(disp=0) # Extract odds ratios odds_ratios = np.exp(model.params) conf_int = np.exp(model.conf_int()) print(f"Odds Ratio for exposur
- Features
- Quick Start
- Binary Logistic Regression
- Cox Proportional Hazards
- Model Selection Decision Tree
- When to Use
- Workflow
- Phase 0: Data Validation
- Phase 1: Model Fitting
- Phase 2: Model Diagnostics
- Phase 3: Interpretation
- Common BixBench Patterns
- Pattern 1: Odds Ratio from Ordinal Regression
- Pattern 2: Percentage Reduction in Odds
What does the tooluniverse-statistical-modeling skill do?
Perform statistical modeling and regression analysis on biomedical datasets. Supports linear regression, logistic regression (binary/ordinal/multinomial), mixed-effects models, Cox proportional hazards survival analysis, Kaplan-Meier estimation, and comprehensive model diagnostics. Extracts odds ratios, hazard ratios, confidence intervals, p-values, and effect sizes. Designed to solve BixBench statistical reasoning questions involving clinical/experimental data. Use when asked to fit regression models, compute odds ratios, perform survival analysis, run statistical tests, or interpret model co
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-statistical-modeling --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.