w-distributions

w-distributions

Distributions for Uniform, Normal, Binomial and Studentt, fork from distributions.

Documentation

To view documentation or get support, visit docs.

Example

To view some examples for more understanding, visit examples:

distributions: web [source code]

Installation

Using npm(ES6 module):

Note: w-distributions is mainly dependent on cephes.

npm i w-distributions

Example:

import wd from 'w-distributions'

async function test() {
    let r

    let normal = await wd.Normal(1,2) //mean=1,std deviation=2

    r = normal.pdf(1)
    console.log(r)
    // => 0.19947114020071632

    r = normal.cdf(1)
    console.log(r)
    // => 0.5

    r = normal.inv(1)
    console.log(r)
    // => Infiniy

    r = normal.mean()
    console.log(r)
    // => 1

    r = normal.median()
    console.log(r)
    // => 1

    r = normal.variance()
    console.log(r)
    // => 4

    let studentt = await wd.Studentt(34) //degrees of freedom=34

    r = studentt.inv(0.95) //one or two sided test p-values=0.95
    console.log(r)
    // => 1.6909242551868549

    studentt = await wd.Studentt(4) //degrees of freedom=4

    r = studentt.inv(0.05) //one or two sided test p-values=0.05
    console.log(r)
    // => -2.1318467863266504

}
test()
    .catch((err) => {
        console.log(err)
    })

In a browser(UMD module):

Note: w-distributions is not dependent on any package, has included cephes.

Note: It does not support IE11, because cephes using WebAssembly in the browser.

[Necessary] Add script for w-distributions.

<script src="https://cdn.jsdelivr.net/npm/w-distributions@1.0.2/dist/w-distributions.umd.js"></script>

Example:

Link: [dev source code]

<script>

    let wd = window['w-distributions']
    // console.log('wd',wd)

    async function test() {
        let r

        let normal = await wd.Normal(1, 2) //mean=1,std deviation=2

        r = normal.pdf(1)
        console.log(r)
        // => 0.19947114020071632

        r = normal.cdf(1)
        console.log(r)
        // => 0.5

        r = normal.inv(1)
        console.log(r)
        // => Infiniy

        r = normal.mean()
        console.log(r)
        // => 1

        r = normal.median()
        console.log(r)
        // => 1

        r = normal.variance()
        console.log(r)
        // => 4

        let studentt = await wd.Studentt(34) //degrees of freedom=34

        r = studentt.inv(0.95) //one or two sided test p-values=0.95
        console.log(r)
        // => 1.6909242551868549

        studentt = await wd.Studentt(4) //degrees of freedom=4

        r = studentt.inv(0.05) //one or two sided test p-values=0.05
        console.log(r)
        // => -2.1318467863266504

    }
    test()
        .catch((err) => {
            console.log(err)
        })

</script>