L0 
A data.frame featuring covariates measured at baseline.

L1 
A data.frame featuring timevarying covariates measured at
the first timepoint.

L2 
A vector outcome of interest

A0 
A vector treatment delivered at baseline.

A1 
A vector treatment deliver after L1 is measured.

abar 
A vector of length 2 indicating the treatment assignment
that is of interest.

stratify 
A boolean indicating whether to pool across treatment
nodes or to estimate outcome regression separately in each category. Should be
kept TRUE until I have more time to think about how to pool across
treatment arms?

SL.Q 
A vector or list specifying the SuperLearner library
to be used to estimate the outcome regressions at each time point. See SuperLearner
package for details.

SL.g 
A vector or list specifying the SuperLearner library
to be used to estimate the conditional probability of treatment at each time point. See SuperLearner
package for details.

SL.Qr 
A vector or list specifying the SuperLearner library
to be used to estimate the reduceddimension regression to protect against misspecification of the
outcome regressions. See SuperLearner package for details.

SL.gr 
A vector or list specifying the SuperLearner library
to be used to estimate the reduceddimension regression to protect against misspecification of the
conditional treatment probabilities. See SuperLearner package for details.

glm.Q 
A character specifying the righthand side of the glm
formula used to estimate the outcome regressions at each time point. Only used if SL.Q = NULL .

glm.g 
A character specifying the righthand side of the glm
formula used to estimate the conditional probability of treatment at each time point.
Only used if SL.g = NULL .

guard 
A vector of characters , either "Q" , "g" , both, or neither (NULL ).
Indicates whether to guard against misspecification of outcome or treatment regressions or both. Currently only works
with c("Q","g") .

universal 
A boolean indicating whether to perform TMLE step using locally least favorable
parametric submodels (if FALSE ) or universally least favorable submodels (if TRUE )

universalStepSize 
A numeric indicating the step size for the recursive calculation of
universally least favorable submodel. Default is 0.005 .

return.models 
A boolean indicating whether the models for Q, g, Qr, and gr should be
returned with the output.

maxIter 
A numeric indicating the maximum number of TMLE iterations before stopping.

tolIF 
A numeric stopping criteria for the TMLE updates based on the empirical average of the
estimated influence curve.

tolg 
A numeric indicating the truncation level for conditional treatment probabilities.

tolQ 
A numeric indicating the truncation level for transformed outcome regressions.

verbose 
A boolean indicating whether messages should be printed to indicate progress.

SL.Q.options 
A list of additional arguments passed to SuperLearner for outcome
regression fits.

SL.g.options 
A list of additional arguments passed to SuperLearner for condtional treatment
probability fits.

glm.Q.options 
A list of additional arguments passed to glm for outcome
regression fits.

return.ltmle 
A boolean indicating whether to compute the LTMLE estimate using a similar
iterative updating scheme.

only.ltmle 
Only return ltmle (for bootstrapping)

return.naive 
A boolean indicating whether to return the naive plugin estimate.

... 
Other arguments (not currently used)
