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>
2.9 KiB
2.9 KiB
@tpmjs/tools-multiple-testing-adjust
Adjust p-values for multiple testing using Bonferroni, Benjamini-Hochberg (BH), or Holm methods to control family-wise error rate or false discovery rate.
Installation
npm install @tpmjs/tools-multiple-testing-adjust
Usage
import { multipleTestingAdjustTool } from '@tpmjs/tools-multiple-testing-adjust';
// Use with AI SDK
const result = await multipleTestingAdjustTool.execute({
pValues: [0.001, 0.02, 0.03, 0.15, 0.8],
method: 'bh', // 'bonferroni', 'bh', or 'holm'
alpha: 0.05,
});
console.log(result);
// {
// adjusted: [0.005, 0.05, 0.05, 0.1875, 0.8],
// significant: [0, 1, 2], // Indices of significant tests
// method: 'bh',
// alpha: 0.05,
// metadata: {
// totalTests: 5,
// significantCount: 3,
// originalSignificant: 3
// }
// }
Parameters
- pValues (number[], required): Array of p-values to adjust (each between 0 and 1)
- method (string, optional): Adjustment method - 'bonferroni', 'bh', or 'holm' (default: 'bonferroni')
- alpha (number, optional): Significance level (default: 0.05)
Returns
{
adjusted: number[]; // Adjusted p-values in original order
significant: number[]; // Indices of tests that remain significant
method: string; // Method used
alpha: number; // Significance level
metadata: {
totalTests: number;
significantCount: number; // Count after adjustment
originalSignificant: number; // Count before adjustment
}
}
Methods
Bonferroni
- Most conservative method
- Controls family-wise error rate (FWER)
- Multiplies each p-value by the number of tests
- Use when you need strict control over false positives
Benjamini-Hochberg (BH)
- Controls false discovery rate (FDR)
- Less conservative than Bonferroni
- Better power for large numbers of tests
- Recommended for exploratory analyses
Holm
- Step-down procedure
- Controls FWER like Bonferroni but more powerful
- Good middle ground between Bonferroni and BH
- Use when you want FWER control with better power
When to use
- When performing multiple hypothesis tests simultaneously
- To avoid inflated Type I error rates
- In genomics, neuroimaging, A/B testing with multiple variants
- Any study with multiple comparisons
Example: Comparing methods
const pValues = [0.001, 0.01, 0.02, 0.03, 0.05, 0.1];
// Bonferroni (most strict)
const bonf = await multipleTestingAdjustTool.execute({
pValues,
method: 'bonferroni',
});
// significant: [0] - only the smallest p-value survives
// Holm (moderate)
const holm = await multipleTestingAdjustTool.execute({
pValues,
method: 'holm',
});
// significant: [0, 1] - two tests survive
// BH (least strict)
const bh = await multipleTestingAdjustTool.execute({
pValues,
method: 'bh',
});
// significant: [0, 1, 2, 3] - four tests survive
License
MIT