code: function pt(df, lowerTail, logp) {
        var ptl;
  
        lowerTail = lowerTail !== false;
        logp = logp === true;
        ptl = ptlog(df, lowerTail);
        if (df === Infinity) { return normal.pnorm(0, 1, lowerTail, logp); }
        return logp ? ptl : function(x) { return Math.exp(ptl(x)); };
     }
docs: cumulative distribution function 
  Evaluates the lower-tail cdf at `x` for the t distribution:
  $$\textrm{pt}(\textrm{df})(x) = \int_{-\infty}^{x} \frac{1}{\sqrt{\textrm{df}}\,\textrm{B}(1/2, \textrm{df}/2)} \left(1+\frac{x^2}{\textrm{df}} \right)^{-\frac{\textrm{df} + 1}{2}}$$
  
  Expects $\textrm{df} > 0$.
  
  `lowerTail` defaults to `true`; if `lowerTail` is `false`, returns
  the upper tail probability instead.
  
  `logp` defaults to `false`; if `logp` is `true`, returns the logarithm
  of the result.
  @fullName pt(df, lowerTail, logp)(x)
  @memberof t