hurdle.IV.sim: Simulate a dataset
In jackiemauro/hurdleIV:

Simulates a dataset from either Cragg or Lognormal distributions. Allows you to specify the data generating process, though it still can't handle more than one endogenous regression

hurdle.IV.sim(formula = F, pi = c(1, -1, 3), gamma = c(-0.2, 0.8, 0.07),
  beta = c(0.05, 0.06, 0.02), endog_reg = list(), exog_mean = 1,
  exog_sd = 1, z_mean = 3, z_sd = 1, endog_sd = 5, y_sd = 2,
  rho = 0.2, tau0 = 0.3, tau1 = 0.1, n = 10000, options = list(silent
  = F, cragg_errors = 4), type = "lognormal")

`formula`	For now, must be False. In the future, will allow you to leave out exogenous variables you use in first stage regressions from second stage regressions.
`pi`	a vector of the coefficients on your first stage regression. Should be in the order: intercept, exogenous variables, instruments. Defaults to (1,-1,3).
`gamma`	a vector of the coefficients on your second stage probit regression. Should be in the order: intercept, exogenous variables, endogenous variable. Defaults to (-.2,.8,.07)
`beta`	a vector of the coefficients on your second stage linear regression. Should be in the order: intercept, exogenous variables, endogenous variable. Defaults to (.05,.06,.02)
`endog_reg`	for now, leave as an empty list. Will create one endogenous regression which includes the instrument and all exogenous variables
`exog_mean`	the mean(s) of your exogenous variable(s). Defaults to 1
`exog_sd`	the standard deviation(s) of your exogenous variable(s). Defaults to 1.
`z_mean`	the mean(s) of your instrument(s). Defaults to 3.
`z_sd`	the standard deviation(s) of your instrument(s). Defaults to 1.
`y_sd`	the standard deviation of your second stage linear regression. Defaults to 2.
`rho`	the covariance term between second stage linear and probit regressions. Defaults to 0.2.
`tau0`	the covariance term between the second stage probit and the first stage regression. Defaults to 0.3.
`tau1`	the covariance term between the second stage linear regression and the first stage regression. Defaults to 0.1.
`n`	the number of observations. Defaults to 10,000.
`options`	Options are: silent = F (will print histograms and percent censored), cragg_errors = 1,2,3,4 cragg error type 4 will generate x2 and y0 first, then generate y1 from a conditional distribution on the first two and on itself being positive. See documentation for cragg_errs for more detail.
`type`	defaults to lognormal. Enter "cragg" for cragg model.