ts-seedrandom

Seeded random number generators for TypeScript.

Installation

npm install ts-seedrandom

Usage

Each generator includes the following methods:

• quick - Default method used. Provides 32 bits of randomness in a float. Can either be called by calling generator instance directly (ex. generator()) or by name (ex. generator.quick()).
• double - Provides 56 bits of randomness.
• int32 - Providers a 32 bit (signed) integer.
• state - Provides internal generator state. Used for saving and restoring states.

import { prngAlea } from 'ts-seedrandom';

const aleaGenerator = prngAlea('seed');
const firstValue = aleaGenerator();
const secondValue = aleaGenerator();

You also have the option of saving and restoring state of your generator.

import { prngAlea } from 'ts-seedrandom';

const aleaGenerator = prngAlea('seed');
const firstValue = aleaGenerator();

// Return internal generator state, which you can use in other generator instances
const state = aleaGenerator.state();

// This generator starts from the same state as first generator, but runs independently
const secondAleaGenerator = prngAlea('seed', state);

Available Algorithms

The following PRNG algorithms are available:

1. prngAlea: Alea algorithm
2. prngArc4: ARC4 algorithm
3. prngTychei: Tyche-i algorithm
4. prngXor128: XorShift128 algorithm
5. prngXor4096: XorShift4096 algorithm
6. prngXorshift7: XorShift7 algorithm
7. prngXorwow: Xorwow algorithm
8. prngMulberry32: Mulberry 32 algorithm
9. prngXoshiro128Plus: Xoshiro128+ algorithm (simple, fast)
10. prngXoshiro128PlusPlus: Xoshiro128++ algorithm (higher statistical quality)
11. prngSplitMix64: SplitMix64 algorithm
12. prngPcg32: PCG32 algorithm

You can import and use any of these algorithms in the same way as demonstrated in the usage examples above.

Algorithm comparison

Name	State Size	Speed 🏎️	Quality 📊	Period	Notes
prngAlea	~96 bits	🟢 Fast	🔸 OK	~2³²	Simple and widely used in JS
prngArc4	2048 bits	🟡 Medium	🔸 OK	~2¹⁷⁰⁰	Legacy cipher, good entropy, slowish
prngTychei	128 bits	🟢 Fast	🟡 Medium	~2⁶⁴	Inspired by Marsaglia, fast and small
prngXor128	128 bits	🟢 Fast	🔸 OK	~2¹²⁸−1	Basic Xorshift, outdated
prngXor4096	4096 bits	🔴 Slow	🟡 Medium	~2⁴⁰⁹⁶−1	Very large state
prngXorshift7	224 bits	🟡 Medium	🟡 Medium	~2²²⁴	Passes more tests than basic Xorshift
prngXorwow	160 bits	🟢 Fast	🟡 Medium	~2¹⁹²	Used in older CUDA RNGs
prngMulberry32	32 bits	🟢 Very fast	🔸 OK	~2³²	Very small, ultra fast
prngXoshiro128Plus	128 bits	🟢 Fast	🟡 Good	~2¹²⁸−1	Simple, good for games
prngXoshiro128PlusPlus	128 bits	🟢 Fast	🟢 Better	~2¹²⁸−1	Improved output function
prngSplitMix64	64 bits	🟢 Very fast	🟢 High	~2⁶⁴	Great for seeding other PRNGs
prngPcg32	64 bits	🟢 Fast	🟢 High	~2⁶⁴	Compact, modern, good for simulation use

Legend

• Speed:

  • 🟢 Fast: Suitable for games, animation, UI
  • 🟡 Medium: Acceptable overhead
  • 🔴 Slow: Use only when large state is essential

• Quality:

  • 🔸 OK: May fail strict tests (e.g., PractRand)
  • 🟡 Medium: Passes basic uniformity/randomness
  • 🟢 High: Strong statistical quality