Formula to detect the grade level of text according to the Flesch–Kincaid grade level.

• What is this?
• When should I use this?
• Install
• Use
• API
  • fleschKincaid(counts)
• Types
• Compatibility
• Related
• Contribute
• Security
• License

This package exposes an algorithm to detect ease of reading of English texts.

You’re probably dealing with natural language, and know you need this, if you’re here!

This algorithm is based on syllables, whereas some others are not, which means it’s tougher to get right and slower to calculate. See syllable for detecting syllables.

This package is ESM only. In Node.js (version 14.14+, 16.0+), install with npm:

npm install flesch-kincaid

In Deno with esm.sh:

import {fleschKincaid} from 'https://esm.sh/flesch-kincaid@2'

In browsers with esm.sh:

<script type="module">
  import {fleschKincaid} from 'https://esm.sh/flesch-kincaid@2?bundle'
</script>

import {fleschKincaid} from 'flesch-kincaid'

// For “The Australian platypus is seemingly a hybrid of a mammal and reptilian
// creature.” (1 sentence, 13 words, 26 syllables).
fleschKincaid({sentence: 1, word: 13, syllable: 26})
// => 13.08

This package exports the identifier fleschKincaid. There is no default export.

Given an object containing the number of words (word), the number of sentences (sentence), and the number of syllables (syllable) in a document, returns the U.S. grade level associated with the document.

Counts from input document.

Number of sentences (number, required).

Number of words (number, required).

Number of syllables (number, required).

Grade level associated with the document (number).

👉 Note: values can theoretically start at -3.40 and end at Infinity.

This package is fully typed with TypeScript. It exports the additional type Counts.

This package is at least compatible with all maintained versions of Node.js. As of now, that is Node.js 14.14+ and 16.0+. It also works in Deno and modern browsers.

• automated-readability — uses character count instead of error-prone syllable parser
• coleman-liau — uses letter count instead of an error-prone syllable parser
• dale-chall-formula — uses a dictionary; suited for higher reading levels
• flesch — uses syllable count
• gunning-fog — uses syllable count, needs POS-tagging and NER
• smog-formula — like gunning-fog-index, without needing advanced NLP
• spache-formula — uses a dictionary, suited for lower reading levels

Yes please! See How to Contribute to Open Source.

This package is safe.

MIT © Titus Wormer