This is not a uniform distribution:

  const randIntRange = (i, j, engine = defaultEngine) => {
    const min = Math.ceil(i)
    const max = Math.floor(j)

    return Math.floor(engine() * (max - min + 1)) + min
  }

If you assume engine() outputs, say, 64 bits of entropy, any number range that is not an even factor of 2^64 will exhibit intermittent bias where the numbers are rounded.

The only correct way to do this is to do rejection sampling: generate an integer x with log2(max - min) bits and if x >= max - min retry until you do:

  function randBelow(n) {
    const nbits = Math.ceil(Math.log2(n));
    while (true) {
       const x = getRandBits(nbits);
       if (x < n) { return x; }
    }
  }

  function randIntHalfOpenRange(min, max) {
    return min + randBelow(max - min);
  }

  function randIntRange(min, max) {
    return randIntHalfOpenRange(min, max + 1);
  }

The primitive you want for building distributions on is not to generate a floating point number between 0.0 and 1.0, but to generate an integer of at most x bits.