code: function dt(df, logp) {
        var dtl;
  
        logp = logp === true;
        dtl = dtlog(df);
        if (df === Infinity) { return normal.dnorm(0, 1, logp); }
        return logp ? dtl : function(x) { return Math.exp(dtl(x)); };
     }
docs: density function dt(df) returns a function for calculating cumulative distribution 
  Evaluates the t density function at `x`:
  $$\textrm{dt}(\textrm{df})(x) = \frac{1}{\sqrt{\textrm{df}}\,\textrm{B}(1/2, \textrm{df}/2)} \left(1+\frac{x^2}{\textrm{df}} \right)^{-\frac{\textrm{df} + 1}{2}}$$
  
  Expects $\textrm{df} > 0$.
  
  `logp` defaults to `false`; if `logp` is `true`, returns the
  logarithm of the result.
  
  @fullName dpois(lambda, logp)(x)
  @fullName dt(df, logp)(x)
  @memberof t