cfa.inner: cfa.inner
In ccfa: Continuous Counterfactual Analysis

calls function to compute counterfactuals

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cfa.inner(tvals, yvals, data, yname, tname, xnames = NULL,
  method = "dr", link = "logit", tau = seq(0.01, 0.99, 0.01),
  condDistobj = NULL, se = TRUE, iters = 100, cl = 1)

`tvals`	the values of the "treatment" to compute parameters of interest for
`yvals`	the values to compute the counterfactual distribution for
`data`	the data.frame where y, t, and x are
`yname`	the name of the outcome (y) variable
`tname`	the name of the treatment (t) variable
`xnames`	the names of additional control variables to include
`method`	either "dr" or "qr" for distribution regression or quantile regression
`link`	if using distribution regression, any link function that works with the binomial family (e.g. logit (the default), probit, cloglog)
`tau`	if using quantile regression, which values of tau to estimate the conditional quantiles
`condDistobj`	optional conditional distribution object that has been previously computed
`se`	whether or not to compute standard errors using the bootstrap
`iters`	how many bootstrap iterations to use
`cl`	how many clusters to use for parallel computation of standard errors