statistical-computing
Computational tools and algorithms for statistical analysis. Covers simulation, resampling methods (bootstrap, permutation tests), Monte Carlo methods, random number generation, numerical optimization (Newton-Raphson, EM algorithm), cross-validation, and reproducible analysis workflows. Emphasizes the bootstrap revolution and the shift from formula-based to computation-based inference. Use when implementing statistical procedures, running simulations, bootstrapping confidence intervals, performing cross-validation, or building reproducible analysis pipelines.
Profile →npx skills add majiayu000/claude-skill-registry --skill statistical-computing --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 Computing Statistical computing transformed the discipline. Before 1970, inference depended on mathematical formulas and tables. The bootstrap (Efron, 1979), permutation tests, and Monte Carlo methods showed that a computer could replace analytical derivations with brute-force resampling -- and often provide more accurate answers with fewer assumptions. This skill covers the computational toolkit that modern statistics depends on. **Agent affinity:** efron (bootstrap, computational methods), box (simulation for model checking), george (simulation-based pedagogy) **Concept IDs:** stat-probability-foundations, stat-hypothesis-testing, stat-descriptive-statistics ## The Bootstrap ### The idea Given a sample of n observations, the bootstrap generates new "samples" by resampling with replacement from the original data. Each bootstrap sample has size n. The distribution of a statistic across many bootstrap samples approximates the sampling distribution of that statistic. ### Algorithm (nonparametric bootstrap) 1. Observe data x_1, x_2, ..., x_n. 2. For b = 1, 2, ..., B (typically B = 1000 to 10000): a. Draw a sample of size n with replacement from the original data. b. Comp
- The Bootstrap
- The idea
- Algorithm (nonparametric bootstrap)
- Bootstrap confidence intervals
- When the bootstrap fails
- Permutation Tests
- Algorithm
- Advantages
- Limitations
- Monte Carlo Simulation
- For probability estimation
- For statistical properties
- Random number generation
- Cross-Validation
What does the statistical-computing skill do?
Computational tools and algorithms for statistical analysis. Covers simulation, resampling methods (bootstrap, permutation tests), Monte Carlo methods, random number generation, numerical optimization (Newton-Raphson, EM algorithm), cross-validation, and reproducible analysis workflows. Emphasizes the bootstrap revolution and the shift from formula-based to computation-based inference. Use when implementing statistical procedures, running simulations, bootstrapping confidence intervals, performing cross-validation, or building reproducible analysis pipelines.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill statistical-computing --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 majiayu000/claude-skill-registry, a repository with 534 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.