Generate a random month.
this app return an random month
Node.js library for parsing crontab instructions
Generate random numbers from various distributions.
TypeScript definitions for d3-random
Fastest random ID and random string generation for Node.js
URL and cookie safe UIDs
Use the random function in CSS
An alias package for `crypto.randomBytes` in Node.js and/or browsers
Generate a cryptographically strong random string
JavaScript date/time utilities for Vega.
Random utility functions for ethers.
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.
Provides functions for detecting if the host environment supports the WebCrypto API
Generate a random integer
Statistical routines and probability distributions.
math-random is an isomorphic, drop-in replacement for `Math.random` that uses cryptographically secure random number generation, where available
Calculates Easter for a given year
Generate random numbers with a seed, useful for reproducible tests
GRC's UHE PRNG in node (Ultra-High Entropy Pseudo-Random Number Generator by Gibson Research Corporation)
Temporary file and directory creator
Revised Bengali Calendar
Typescript port of an accurate python Hijri-Gregorian dates converter based on the Umm al-Qura calendar: https://github.com/mhalshehri/hijri-converter
Using this gem one can generate randomized weather based upon the rules found in the 1983 boxed set. Currently not all features of those rules are applied. Future plans include the ability to choose different months and weather chances instead of only those from Greyhawk.
This Ruby gem leverages Machine Learning(ML) techniques to make predictions(forecasts) and classifications in various applications. It provides capabilities such as predicting next month's billing, forecasting upcoming sales orders, identifying patient's potential findings(like Diabetes), determining user approval status, classifying text, generating similarity scores, and making recommendations. It uses Python3 under the hood, powered by popular machine learning techniques including NLP(Natural Language Processing), Decision Tree, K-Nearest Neighbors and Logistic Regression, Random Forest and Linear Regression algorithms.