CBMSM.fit: CBMSM.fit
In CBPS: Covariate Balancing Propensity Score

CBMSM.fit

CBMSM.fit(
  treat,
  X,
  id,
  time,
  MultiBin.fit,
  twostep,
  msm.variance,
  time.vary,
  init,
  ...
)

`treat`	A vector of treatment assignments. For N observations over T time periods, the length of treat should be N*T.
`X`	A covariate matrix. For N observations over T time periods, X should have N*T rows.
`id`	A vector which identifies the unit associated with each row of treat and X.
`time`	A vector which identifies the time period associated with each row of treat and X.
`MultiBin.fit`	A parameter for whether the multiple binary treatments occur concurrently (`FALSE`) or over consecutive time periods (`TRUE`) as in a marginal structural model. Setting type = "MultiBin" when calling `CBMSM` will set MultiBin.fit to `TRUE` when CBMSM.fit is called.
`twostep`	Set to `TRUE` to use a two-step estimator, which will run substantially faster than continuous-updating. Default is `FALSE`, which uses the continuous-updating estimator described by Imai and Ratkovic (2014).
`msm.variance`	Default is `FALSE`, which uses the low-rank approximation of the variance described in Imai and Ratkovic (2014). Set to `TRUE` to use the full variance matrix.
`time.vary`	Default is `FALSE`, which uses the same coefficients across time period. Set to `TRUE` to fit one set per time period.
`init`	Default is `"opt"`, which uses CBPS and logistic regression starting values, and chooses the one that achieves the best balance. Other options are "glm" and "CBPS"
`...`	Other parameters to be passed through to `optim()`