Collection of online algorithms for data exploration and analysis implemented in JavaScript. Online algorithms process inputs piece by piece. That means you can process data without storing it in memory. More about online algorithms: https://en.wikipedia.org/wiki/Online_algorithm
Most algorithms of online-stats also support more usual batch mode (i.e. mean([1,2,3,4]))
npm i -S online-stats
const Stats = require('online-stats') // { Mean, Median, Max, Min, ... }
To process data sequentially we need functions to have internal state. That's why there's one extra step - functions initialization
const mean = Stats.Mean() // -> function mean
const median = Stats.Median() // -> function median
...
When functions are initialized, just call them passing a value (for example: mean(x)).
Result is returned. To get a final result just call a function without any params: const result = mean()
Check how the online-stats package is used for online data profiling on StatSim Profile
const mean = Stats.Mean()
mean(1) // -> 1
mean(2) // -> 1.5
mean(9) // -> 4
console.log(mean()) // -> 4const v = Stats.Variance({ddof: 1}) // 0 (default) - population variance, 1 - sample variance
v(1) // -> 0
v(2) // -> 0.5
v(9) // -> 19
console.log(v()) // -> 19const median = Stats.Median()
median(1) // -> 1
median(2) // -> 1.5
median(9) // -> 2
console.log(median()) // -> 2const min = Stats.Min()
min(2) // -> 2
min(6) // -> 2
min(1) // -> 1
console.log(min()) // -> 1const max = Stats.Max()
max(2) // -> 2
max(6) // -> 6
max(1) // -> 6
console.log(max()) // -> 6const std = Stats.Std({ddof: 1}) // 0 (default) - population std, 1 - sample std (unbiased)
std(1) // -> 0
std(2) // ~> 0.7071
std(9) // ~> 4.3589
console.log(std()) // ~> 4.3589const a = [1, 3, 2, 5, 8, 7, 12, 2, 4]
const b = [8, 6, 9, 4, 3, 3, 2, 7, 7]
const cov = Stats.Covariance({ddof: 1})
a.forEach((ax, i) => { cov(ax, b[i]) })
console.log(cov()) // -> -8.069const hist = Stats.Histogram(20)
hist(2)
hist(6)
hist(1)
console.log(hist())const autocov = Stats.AutoCov(5)
;[1, 2, 3, 4, 5, 6, 7].forEach(v => { autocov(v) })
console.log(autocov())const autocor = Stats.AutoCor(5)
;[1, 2, 3, 4, 5, 6, 7].forEach(v => { autocor(v) })
console.log(autocor())const lr = Stats.LinReg()
const f = x => 0.5 * x + 2
;[1, 2, 3, 4, 5, 6].forEach(v => { lr(v, f(v)) })
console.log(lr([7])) // Predict
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