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dual-n-back-training-planner

v1.0.0

Published

Zero-dependency helpers for planning Dual N-Back practice and summarizing user-provided results.

Readme

Dual N-Back Training Planner

A small, zero-dependency JavaScript package for creating a practice calendar and summarizing user-provided Dual N-Back session results.

This is an independent open-source utility. It does not contain the source code of the Dual N-Back web application, collect data, make network requests, or claim that N-Back practice increases IQ.

  • Website: https://dualnbackapp.com/
  • Online trainer: https://dualnbackapp.com/trainer/
  • Public notebook: https://www.kaggle.com/code/yanlunin/dual-n-back
  • Launchpad project: https://launchpad.net/dual-n-back-planner

Install

npm install dual-n-back-training-planner

Example

const {
  createTrainingPlan,
  summarizeResults,
  suggestNextLevel
} = require('dual-n-back-training-planner');

const plan = createTrainingPlan({
  startDate: '2026-09-07',
  weeks: 4,
  sessionsPerWeek: 3,
  sessionMinutes: 20,
  startLevel: 2
});

const results = [
  { accuracy: 72 },
  { correct: 16, total: 20 },
  { accuracy: 86 }
];

console.log(plan.sessions);
console.log(summarizeResults(results));
console.log(suggestNextLevel(results, 2));

API

createTrainingPlan(options)

Creates a deterministic UTC-date calendar. Options:

  • startDate: a Date or date string; defaults to today.
  • weeks: 1–52; defaults to 4.
  • sessionsPerWeek: 1–7; defaults to 3.
  • sessionMinutes: 1–120; defaults to 20.
  • startLevel: 1–10; defaults to 2.

summarizeResults(results)

Returns total sessions, average accuracy, best accuracy, latest accuracy, and the average of the latest three sessions. Each result can contain either accuracy (0–100) or correct and total values.

suggestNextLevel(results, currentLevel, options)

Returns a transparent increase, hold, or decrease suggestion. Default thresholds are 85 for increasing and 60 for decreasing; both can be customized. This is a convenience rule, not medical guidance.

Privacy

All calculations run locally. The package has no dependencies, telemetry, storage, or network access.

License

MIT