code: function colLogLikelihood(a, b, ab, N) {
    const p = b / N,
          p1 = ab / a,
          p2 = (b - ab) / (N - a);
  
    const summand1 = ab * Math.log(p) + (a - ab) * Math.log(1 - p),
          summand2 = (b - ab) * Math.log(p) + (N - a - b + ab) * Math.log(1 - p);
  
    let summand3 = 0;
    if (a !== ab)
      summand3 = ab * Math.log(p1) + (a - ab) * Math.log(1 - p1);
  
    let summand4 = 0;
    if (b !== ab)
      summand4 = (b - ab) * Math.log(p2) + (N - a - b + ab) * Math.log(1 - p2);
  
    const likelihood = summand1 + summand2 - summand3 - summand4;
  
    return (-2 * likelihood);
  }
docs: A function that wil just compute log-likelihood estimate, in the original
  paper, it's described in algorithm 6 and 7.
  
  Note: this SHOULD be the original Dunning log-likelihood values.
  
  @param {number} a  - Count of <a>.
  @param {number} b  - Count of <b>.
  @param {number} ab - Count of <ab>.
  @param {number} N  - Number of elements in the distribution.
  @return {number}   - The log-likelihood.