stats-lite
A fairly light statistical package. Works with numeric arrays, and will automatically filter out non-numeric values and attempt to convert string numeric values.
Example
Live Demo using Browserify!
var stats = require("stats-lite")
var dice = require("dice")
var rolls = []
for (var i = 0; i < 3000; i++) {
rolls.push(dice.sum(dice.roll("2d6")))
}
console.log("sum: %s", stats.sum(rolls))
console.log("mean: %s", stats.mean(rolls))
console.log("median: %s", stats.median(rolls))
console.log("mode: %s", stats.mode(rolls))
console.log("variance: %s", stats.variance(rolls))
console.log("standard deviation: %s", stats.stdev(rolls))
console.log("85th percentile: %s", stats.percentile(rolls, 0.85))
console.log("histogram:", stats.histogram(rolls))
Compatibility Notice: Version 2.0.0+ of this library use features that require Node.js v4.0.0 and above
API
All of the exported functions take vals
which is an array of numeric values. Non-numeric values will be removed, and string numbers will be converted to Numbers.
NOTE: This will impact some operations, e.g. mean([null, 1, 2, 3])
will be calculated as mean([1, 2, 3])
, (e.g. 6 / 3 = 2
, NOT 6 / 4 = 1.5
)
numbers(vals)
Accepts an array of values and returns an array consisting of only numeric values from the source array. Converts what it can and filters out anything else. e.g.
numbers(["cat", 1, "22.9", 9])
sum(vals)
Sum the values in the array.
mean(vals)
Calculate the mean average value of vals
.
median(vals)
Calculate the median average value of vals
.
mode(vals)
Calculate the mode average value of vals
.
If vals
is multi-modal (contains multiple modes), mode(vals)
will return a ES6 Set of the modes.
variance(vals)
Calculate the variance from the mean.
stdev(vals)
Calculate the standard deviation of the values from the mean.
percentile(vals, ptile)
Calculate the value representing the desired percentile (0 < ptile <= 1)
. Uses the Estimation method to interpolate non-member percentiles.
histogram(vals[, bins])
Build a histogram representing the distribution of the data in the provided number of bins
. If bins
is not set, it will choose one based on Math.sqrt(vals.length)
. Data will look like:
histogram {
values: [ 86, 159, 253, 335, 907, 405, 339, 270, 146, 100 ],
bins: 10,
binWidth: 1.05,
binLimits: [ 1.75, 12.25 ]
}
Where values
are the bins and the counts of the original values falling in that range. The ranges can be calculated from the binWidth
and binLimits
. For example, the first bin values[0]
in this example is from 1.75 < value <= 2.8
. The third bin values[2]
would be 1.75 + (1.05 * 2) < value <= 1.75 + (1.05 * 3)
or 3.85 < value <= 4.9
.
LICENSE
MIT