The most RANDOM module on npm! 😜

PHP implementation of statistical probability distributions: normal distribution, beta distribution, gamma distribution and more.

:rocket: A library designed to facilitate work with probability, statistics and stochastic calculus

Gauss Naive Bayes in Python From Scratch.

An algorithm based on Java implementation, can automatically check the set of outliers in a set of data, eliminate these outliers, and finally get normal data.基于java实现的能够自动检查出一组数据中的异常值的集合，剔除这些异常集，得到正常数据。

17 standard normality tests in a single wrapper

A text analyzer to tell the sentiment in a given text

Shiny app - Bayesian inference for normal mean (known variance)

Package provides the javascript implementation of various statistics and distribution

Compute mean and variance of the univariate truncated normal distribution (works far from the peak).

Desktop app that estimates the probability of price movements for stocks

Applying A/B test to help determing if company should launch the new page

Monte Carlo simulator in Python.

Statistics library for Dart

Random data generator from scratch. (Using numpy for simple mathematical functions only)

Statistics library for Dart

C++ and Julia Normal distribution generated with uniform distribution

Examples of normal distribution with python

Background for Hypothesis testing / Bayesian Inference with code examples

Statistics is the heart of Analytics, and doing that using python requires a number of libraries and functions which makes it easy to apply the statistical concepts on machine learning models.

Schedules a set of simulated processes in a single processor system and a multi-processor system using the Shortest Job First (SJF) scheduling algorithm.

This http traffic generator uses a Normal distribution to limit the connection per second

golang normal distributions

Statistical analysis comparing metal accumulation levels in three macroinvertebrate groups

This is a project I did in the Spring of 2017 for a graduate course in Statistical Computing. I was asked to find the descriptive statistics of 1000 simulations of a distribution, specifically to find the mean, median, midrange, and interquartile range of the simulations. I was asked to find these statistics for 1000 simulations of sample sizes of 10, 30, and 100. I was aslo asked to find each of these sample sizes for Normal, Uniform, and Exponential distributions.

Sql Server Normal Distribution, Gauss or Bell Curve and Cumulative normal distribution

A JavaScript Project to form a Normal Probability Distribution out of a provided Sample.

Fire Detection and Alarm Topology ( based in Neural Network ) in Java ME

Naive Bayes classifier