CBPS.fit: CBPS.fit determines the proper routine (what kind of...
In CBPS: Covariate Balancing Propensity Score

CBPS.fit determines the proper routine (what kind of treatment) and calls the approporiate function. It also pre- and post-processes the data

CBPS.fit(
  treat,
  X,
  baselineX,
  diffX,
  ATT,
  method,
  iterations,
  standardize,
  twostep,
  sample.weights = sample.weights,
  ...
)

`treat`	A vector of treatment assignments. Binary or multi-valued treatments should be factors. Continuous treatments should be numeric.
`X`	A covariate matrix.
`baselineX`	Similar to `baseline.formula`, but in matrix form.
`diffX`	Similar to `diff.formula`, but in matrix form.
`ATT`	Default is 1, which finds the average treatment effect on the treated interpreting the second level of the treatment factor as the treatment. Set to 2 to find the ATT interpreting the first level of the treatment factor as the treatment. Set to 0 to find the average treatment effect. For non-binary treatments, only the ATE is available.
`method`	Choose "over" to fit an over-identified model that combines the propensity score and covariate balancing conditions; choose "exact" to fit a model that only contains the covariate balancing conditions.
`iterations`	An optional parameter for the maximum number of iterations for the optimization. Default is 1000.
`standardize`	Default is `TRUE`, which normalizes weights to sum to 1 within each treatment group. For continuous treatments, normalizes weights to sum up to 1 for the entire sample. Set to `FALSE` to return Horvitz-Thompson weights.
`twostep`	Default is `TRUE` for a two-step estimator, which will run substantially faster than continuous-updating. Set to `FALSE` to use the continuous-updating estimator described by Imai and Ratkovic (2014).
`sample.weights`	Survey sampling weights for the observations, if applicable. When left NULL, defaults to a sampling weight of 1 for each observation.
`...`	Other parameters to be passed through to `optim()`.