5d2096fb5d
Implements a comprehensive suite of AI SDK v6 tools across multiple categories: - Research (5): page-brief, compare-pages, source-credibility, claim-checklist, timeline-from-text - Web (10): fetch-text, links-catalog, extract-meta, extract-json-ld, redirect-trace, sitemap-read, rss-read, table-extract, robots-policy, url-normalize - Data (15): csv-parse, csv-stringify, json-repair, json-schema-validate, yaml-parse, yaml-stringify, text-chunk, normalize-whitespace, dedupe-by-key, pivot, rows-filter, rows-sort, rows-group-aggregate, rows-join, schema-infer - Doc (12): toc-generate, glossary-build, faq-from-text, executive-brief, decision-record-adr, prd-outline, acceptance-criteria, style-rewrite - Eng (12): diff-text-unified, env-var-docs-generate, dependency-audit-lite, conventional-commit-suggest, markdown-lint-basic, test-case-generate, stacktrace-parse, release-notes, changelog-entry, release-checklist - Security (7): redact-secrets, secret-scan-text, url-risk-heuristic, csp-compose, hardening-checklist-web, access-control-matrix, data-classification-heuristic - Stats (9): effect-size-suite, bootstrap-ci, permutation-test, multiple-testing-adjust, linear-regression-ols, logistic-regression, time-series-decompose-lite, anomaly-detect-mad - Ops (7): slo-draft, runbook-draft, postmortem-draft, postmortem-action-extractor, error-log-triage, coverage-tracker, monitoring-gap-analysis - Agent (15): prompt-to-workflow-skeleton, workflow-validate-io, workflow-explain, workflow-cost-estimate, tool-call-accuracy-score, eval-fixture-build, guardrail-policy-draft, workflow-auto-repair, tool-selection-plan, novelty-score-workflow, workflow-variant-generate, config-normalize, recipe-* - Utility (8): base64-encode, base64-decode, hash-text, regex-extract, template-render, date-parse, json-path-query, url-parse - HTML (3): html-sanitize, html-to-markdown, markdown-to-html All tools follow AI SDK v6 pattern with tool() and jsonSchema<T>(). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
115 lines
2.9 KiB
Markdown
115 lines
2.9 KiB
Markdown
# @tpmjs/tools-multiple-testing-adjust
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Adjust p-values for multiple testing using Bonferroni, Benjamini-Hochberg (BH), or Holm methods to control family-wise error rate or false discovery rate.
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## Installation
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```bash
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npm install @tpmjs/tools-multiple-testing-adjust
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```
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## Usage
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```typescript
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import { multipleTestingAdjustTool } from '@tpmjs/tools-multiple-testing-adjust';
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// Use with AI SDK
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const result = await multipleTestingAdjustTool.execute({
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pValues: [0.001, 0.02, 0.03, 0.15, 0.8],
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method: 'bh', // 'bonferroni', 'bh', or 'holm'
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alpha: 0.05,
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});
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console.log(result);
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// {
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// adjusted: [0.005, 0.05, 0.05, 0.1875, 0.8],
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// significant: [0, 1, 2], // Indices of significant tests
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// method: 'bh',
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// alpha: 0.05,
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// metadata: {
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// totalTests: 5,
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// significantCount: 3,
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// originalSignificant: 3
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// }
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// }
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```
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## Parameters
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- **pValues** (number[], required): Array of p-values to adjust (each between 0 and 1)
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- **method** (string, optional): Adjustment method - 'bonferroni', 'bh', or 'holm' (default: 'bonferroni')
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- **alpha** (number, optional): Significance level (default: 0.05)
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## Returns
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```typescript
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{
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adjusted: number[]; // Adjusted p-values in original order
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significant: number[]; // Indices of tests that remain significant
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method: string; // Method used
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alpha: number; // Significance level
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metadata: {
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totalTests: number;
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significantCount: number; // Count after adjustment
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originalSignificant: number; // Count before adjustment
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}
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}
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```
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## Methods
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### Bonferroni
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- Most conservative method
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- Controls family-wise error rate (FWER)
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- Multiplies each p-value by the number of tests
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- Use when you need strict control over false positives
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### Benjamini-Hochberg (BH)
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- Controls false discovery rate (FDR)
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- Less conservative than Bonferroni
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- Better power for large numbers of tests
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- Recommended for exploratory analyses
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### Holm
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- Step-down procedure
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- Controls FWER like Bonferroni but more powerful
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- Good middle ground between Bonferroni and BH
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- Use when you want FWER control with better power
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## When to use
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- When performing multiple hypothesis tests simultaneously
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- To avoid inflated Type I error rates
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- In genomics, neuroimaging, A/B testing with multiple variants
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- Any study with multiple comparisons
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## Example: Comparing methods
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```typescript
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const pValues = [0.001, 0.01, 0.02, 0.03, 0.05, 0.1];
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// Bonferroni (most strict)
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const bonf = await multipleTestingAdjustTool.execute({
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pValues,
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method: 'bonferroni',
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});
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// significant: [0] - only the smallest p-value survives
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// Holm (moderate)
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const holm = await multipleTestingAdjustTool.execute({
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pValues,
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method: 'holm',
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});
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// significant: [0, 1] - two tests survive
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// BH (least strict)
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const bh = await multipleTestingAdjustTool.execute({
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pValues,
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method: 'bh',
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});
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// significant: [0, 1, 2, 3] - four tests survive
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```
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## License
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MIT
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