Return a normally-distributed random variate.
TypeScript definitions for random-normal
Statistical routines and probability distributions.
Generate pseudorandom numbers drawn from a normal distribution.
Normally distributed pseudorandom numbers.
Measures patterns of attribute values associated with features. Reveals whether similar values tend to occur near each other, or whether high or low values are interspersed
Seedable random number generator supporting many common distributions.
Generate random numbers from various distributions.
Fastest random ID and random string generation for Node.js
TypeScript definitions for d3-random
Normal distribution quantile function.
URL and cookie safe UIDs
Use the random function in CSS
Normal distribution probability density function (PDF).
An alias package for `crypto.randomBytes` in Node.js and/or browsers
Generate a cryptographically strong random string
Random utility functions for ethers.
Normal distribution cumulative distribution function (CDF).
Javascript Implementation of Statistical R standalone library rnmath
A Pulumi package to safely use randomness in Pulumi programs.
A small implementation of `crypto.getRandomValues` for React Native. This is useful to polyfill for libraries like [uuid](https://www.npmjs.com/package/uuid) that depend on it.
Generate a random integer
Provides functions for detecting if the host environment supports the WebCrypto API
Extremely fast double-ended queue implementation
Random number generator from a multivariate normal distribution.
Generates Normally Distributed, Random Numbers
Normal(Gaussian) random number generator.
Generate random numbers from a Normal/Gaussian distribution
Simple Random Number Generator including Beta, Cauchy, Chi square, Exponential, Gamma, Inverse Gamma, Laplace (double exponential), Normal, Student t, Uniform, and Weibull. Ported from John D. Cook's C# Code.
random variables for a wide variety of probability distributions such as: Binomial, Beta, Chi-Squared, Normal, Pareto, Poisson, etc.
This library extends class Random in the Ruby standard library. The Random class in the Ruby standard library supports only random sampling from discrete/continuous uniform distribution. This library provides random sampling methods from many kinds of probability distributions such as normal, gamma, beta, chi_square, t, F, binomial, Poisson, and many other distributions.
Xelor was built for systems that require random bytes for processes faster than one second. Because normal random generation is based off of time as a seed, if there exists multiple calls towards SecureRandom or Rand within one second, the same number will be produced. This can be resolved on unix or linux based systems by making a system call to read /dev/urandom.
A Mersenne-Twister random number generator (RNG) packed up as a class. This allows multiple RNG streams to be active at the same time (which Ruby's normal rand/srand does not allow). The Mersenne-Twister is implemented with fast C code for speed.
FSelector is a Ruby gem that aims to integrate various feature selection algorithms and related functions into one single package. Welcome to contact me (need47@gmail.com) if you'd like to contribute your own algorithms or report a bug. FSelector allows user to perform feature selection by using either a single algorithm or an ensemble of multiple algorithms, and other common tasks including normalization and discretization on continuous data, as well as replace missing feature values with certain criterion. FSelector acts on a full-feature data set in either CSV, LibSVM or WEKA file format and outputs a reduced data set with only selected subset of features, which can later be used as the input for various machine learning softwares such as LibSVM and WEKA. FSelector, as a collection of filter methods, does not implement any classifier like support vector machines or random forest.
A simple, text-based crowdfunding simulator. Run a group of projects through a series of funding rounds, in which they either receive or lose funds, or are skipped. They also receive a random pledge. Grant projects never lose funds. Match projects have all future funding matched after they reach half-funding. Statistics are printed to the console at the end of the simulation. The normal projects can be specified in a '.csv' file that is given as a command line argument when loading the program, or the default projects can be used. The format for 'csv' entries is Project Name,Goal,Initial_funding with a comma and no spaces between entries and underscores in place of commas within larger numbers (e.g. Your Project,10_000,0). The option is given to save a list of underfunded projects upon exiting the program. The list is saved in 'underfunded.txt' in the top-level folder of the application. Created as a bonus project while completing the Pragmatic Studio Ruby Programming course.
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