How to use the jstat.jStat.exponential function in jstat

// Source:
// https://en.wikipedia.org/wiki/Beta_distribution#Transformations
function PERT(min, max, mode = (min + max)/2, lambda = 4) {
  const width = max - min
  const a = 1 + lambda * ((mode - min)/width)
  const b = 1 + lambda * ((max - mode)/width)
  const p = jStat.beta.sample(a, b)
  return min + p*width
}

export const Distributions = {
  beta: jStat.beta.sample,
  centralF: jStat.centralF.sample,
  cauchy: jStat.cauchy.sample,
  chisquare: jStat.chisquare.sample,
  exponential: jStat.exponential.sample,
  invgamma: jStat.invgamma.sample,
  lognormal: jStat.lognormal.sample,
  normal: jStat.normal.sample,
  studentt: jStat.studentt.sample,
  weibull: jStat.weibull.sample,
  uniform: jStat.uniform.sample,
  gamma: jStat.gamma.sample,
  triangular,
  doubleTriangular,
  PERT,
  bernoulli: bernoulli,
  if: bernoulli,
  test: bernoulli,
  binomial: binomial,
  poisson: poisson,
  negBinomial: negBinomial

import math from 'mathjs'
import {jStat} from 'jstat'
var Finance = require('financejs')
const finance = new Finance()

const jStatDistributions = {
  beta: jStat.beta.sample,
  centralF: jStat.centralF.sample,
  cauchy: jStat.cauchy.sample,
  chisquare: jStat.chisquare.sample,
  exponential: jStat.exponential.sample,
  invgamma: jStat.invgamma.sample,
  lognormal: jStat.lognormal.sample,
  normal: jStat.normal.sample,
  studentt: jStat.studentt.sample,
  weibull: jStat.weibull.sample,
  uniform: jStat.uniform.sample,
  gamma: jStat.gamma.sample
}

const financeFunctions = {
  PV: finance.PV,
  FV: finance.FV,
  NPV: finance.NPV,
  //IRR: finance.IRR, Too slow.
  PP: finance.PP,
  ROI: finance.ROI,

private draw(dist: Distribution): number {
    switch (dist.tag) {
      case 'constant':
        return dist.value;
      case 'exp':
        const expDraw = jStat.exponential.sample(dist.rate) as number;
        return expDraw > 0 ? expDraw : 0;
      case 'gamma':
        const grammDraw = jStat.gamma.sample(dist.shape, dist.scale) as number;
        return grammDraw > 0 ? grammDraw : 0;
      case 'normal':
        const normalDraw = jStat.normal.sample(dist.mean, dist.std) as number;
        return normalDraw > 0 ? normalDraw : 0;
      default:
        throw new Error('Unsupported distribution');
    }
  }