Maybe a bit of a hail mary but I want to take an array and calculate the statistical cumulative distribution function from data array of x values. https://www.npmjs.com/package/cumulative-distribution-function?activeTab=readme

would be perfect for my needs but i’m not sure i can import functions like this to pavlovia.

any advice of a function or how to use the above written function would be super appreciated!

couldn’t you just copy the .js code that defines the needed function from here and paste it into the code component, where you use it in your experiment? Not sure if it works like that.

Ive just see that you were looking for the ecdf (have just thought about standard cdf, thinking your sample emerges from a known normal distribution).

Than you can really just remove the exports statement (node.js specific) and use this in a JS Only Before Experiment Code Component:

ecdfArray = function(data) {
"use strict";
var f, sorted, xs, ps, i, j, l, xx;
if (Array.isArray(data) && (data.length > 0)) {
for (i = 0, l = data.length; i < l; ++i) {
if (typeof(data[i]) !== 'number') {
throw new TypeError("cdf data must be an array of finite numbers, got:" + typeof(data[i]) + " at " + i);
}
if (!isFinite(data[i])) {
throw new TypeError("cdf data must be an array of finite numbers, got:" + data[i] + " at " + i);
}
}
sorted = data.slice().sort(function(a, b) {
return +a - b;
});
xs = [];
ps = [];
j = 0;
l = sorted.length;
xs[0] = sorted[0];
ps[0] = 1 / l;
for (i = 1; i < l; ++i) {
xx = sorted[i];
if (xx === xs[j]) {
ps[j] = (1 + i) / l;
} else {
j++;
xs[j] = xx;
ps[j] = (1 + i) / l;
}
}
f = function(x) {
if (typeof(x) !== 'number') throw new TypeError('cdf function input must be a number, got:' + typeof(x));
if (Number.isNaN(x)) return Number.NaN;
var left = 0,
right = xs.length - 1,
mid, midval, iteration;
if (x < xs[0]) return 0;
if (x >= xs[xs.length - 1]) return 1;
iteration = 0;
while ((right - left) > 1) {
mid = Math.floor((left + right) / 2);
midval = xs[mid];
if (x > midval)
left = mid;
else if (x < midval)
right = mid;
else if (x === midval) {
left = mid;
right = mid;
}
++iteration;
if (iteration>40) throw new Error("cdf function exceeded 40 bisection iterations, aborting bisection loop");
}
return ps[left];
};
f.xs = function() {
return xs;
};
f.ps = function() {
return ps;
};
} else {
// missing or zero length data
throw new TypeError("cdf data must be an array of finite numbers, got: missing or empty array");
}
return f;
};

This should work in the experiment like this:
test = ecdfArray([1,2,3,4,5,5,6])
test(5)
→ 0.857142857142857