npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2026 – Pkg Stats / Ryan Hefner

@ferimer/node-random

v0.0.4

Published

Random generator with PKCS#11 devices if available

Readme

Pruebas entropía:

->   ent data_linux.dat Entropy = 7.992196 bits per byte.

Optimum compression would reduce the size of this 100000 byte file by 0 percent.

Chi square distribution for 100000 samples is 690.69, and randomly would exceed this value less than 0.01 percent of the times.

Arithmetic mean value of data bytes is 126.8143 (127.5 = random). Monte Carlo value for Pi is 3.159726389 (error 0.58 percent). Serial correlation coefficient is 0.001111 (totally uncorrelated = 0.0).     frsela  ~  Datos   … ctos  nodeLibs  node-random  ⎈fm-k8s-admin@fm-k8s-01   ->   ent data_yubikey_5.dat Entropy = 7.998167 bits per byte.

Optimum compression would reduce the size of this 100000 byte file by 0 percent.

Chi square distribution for 100000 samples is 254.01, and randomly would exceed this value 50.58 percent of the times.

Arithmetic mean value of data bytes is 126.9385 (127.5 = random). Monte Carlo value for Pi is 3.149885995 (error 0.26 percent). Serial correlation coefficient is 0.000188 (totally uncorrelated = 0.0).     frsela  ~  Datos   … ctos  nodeLibs  node-random  ⎈fm-k8s-admin@fm-k8s-01   ->   ent data_nitrokey_hsm.dat Entropy = 7.998340 bits per byte.

Optimum compression would reduce the size of this 100000 byte file by 0 percent.

Chi square distribution for 100000 samples is 231.06, and randomly would exceed this value 85.67 percent of the times.

Arithmetic mean value of data bytes is 127.3643 (127.5 = random). Monte Carlo value for Pi is 3.138125525 (error 0.11 percent). Serial correlation coefficient is 0.004002 (totally uncorrelated = 0.0).

Tiempos 100k:

Linux:

real 0m13,475s user 0m0,755s sys 0m1,008s

Yubikey:

real 0m13,475s user 0m0,755s sys 0m1,008s

Nitrokey:

real 1m50,325s user 0m0,417s sys 0m0,289s