Create arrays of random length, sampled from a specified distribution

several.js

/**
 * Generate an array from random length between `min` and `max`, calling `fct`
 * to populate each item.
 *
 * @example Creates an array of random length sampled from a Poisson distribution of timestamp-text pair objects.
 * several(
 *    () => ({
 *      timestamp: f.date.recent(),
 *      text: f.lorem.sentence(),
 *    }),
 *    () => poisson(3)
 *  )
 *
 * @param {function} fct - Function to apply to each element
 * @param {function} dist - Distribution from which to sample, defaults to uniform [0–10]
 * @return {Array}
 */
function several(fct, dist = () => Math.random() * 10) {
  const rand = dist();
  const length = Math.max(Math.floor(rand), 0);
  // console.log(length);
  const coll = [];
  for (let i = 0; i < length; i++) {
    coll.push(fct());
  }
  return coll;
}

// https://github.com/robbrit/randgen/blob/master/lib/randgen.js#L21-L49
// Generate normally-distributed random nubmers
// Algorithm adapted from:
// http://c-faq.com/lib/gaussian.html
function normal(mean = 0.0, stdev = 1.0) {
  var u1, u2, v1, v2, s;
  if (normal.v2 === undefined) {
    do {
      u1 = Math.random();
      u2 = Math.random();

      v1 = 2 * u1 - 1;
      v2 = 2 * u2 - 1;
      s = v1 * v1 + v2 * v2;
    } while (s === 0 || s >= 1);

    normal.v2 = v2 * Math.sqrt(-2 * Math.log(s) / s);
    return stdev * v1 * Math.sqrt(-2 * Math.log(s) / s) + mean;
  }

  v2 = normal.v2;
  normal.v2 = undefined;
  return stdev * v2 + mean;
}

// https://github.com/robbrit/randgen/blob/master/lib/randgen.js#L67-L81
function poisson(lambda = 1) {
  var l = Math.exp(-lambda),
    k = 0,
    p = 1.0;
  do {
    k++;
    p *= Math.random();
  } while (p > l);

  return k - 1;
}

const coll = [];
for (let i = 0; i < length; i++) {
  coll.push(fct());
}
return coll;

can be simplified to

return Array.from(new Array(length), fct);