An internal function called by the tmleMSM function to calculate the variance-covariance matrix of the parameter estimates based on the influence curve of the specified MSM.
tmle-package
(Package: tmle) :
Targeted Maximum Likelihood Estimation with Super Learning
Targeted maximum likelihood estimation of marginal treatment effect of a binary point treatment on a continuous or binary outcome, adjusting for baseline covariates. Missingness in the outcome is accounted for in the estimation procedure. The population mean outcome is calculated when there is missingness and no treatment. Controlled direct effect estimation is available, and MSM parameter estimation for binary point treatment effects. Optional data-adaptive estimation of Q and g portions of the likelihood using the SuperLearner package is strongly encouraged.
estimateG
(Package: tmle) :
Estimate Treatment or Missingness Mechanism
An internal function called by the tmle function to obtain an estimate of conditional treatment assignment probabiliites P(A=1|W), and conditional probabilites for missingness, P(Delta=1|A,W). The estimate can be based on user-supplied values, a user-supplied regression formula, or a data-adaptive super learner fit. If the SuperLearner package is not available, and there are no user-specifications, estimation is carried out using main terms regression with glm. These main terms-based estimates may yield poor results.
An internal function called by the tmle function to calculate the population mean effect when there is missingness in the data, but no treatment assignment. When observations are in treatment and control groups, estimates the additive treatment effect, and if the outcome is binary, also the relative risk and odds ratio parameters. P-values and 95% confidence intervals are also calculated (on the log scale for RR and OR).
An internal function called by the tmle function to obtain an initial estimate of the Q portion of the likelihood based on user-supplied matrix values for predicted values of (counterfactual outcomes) Q(0,W),Q(1,W), or a user-supplied regression formula, or based on a data-adaptively selected SuperLearner fit. In the absence of user-supplied values, a user-supplied regression formula takes precedence over data-adaptive super-learning.
SL.glm.interaction
(Package: tmle) :
Wrapper function for SuperLearner prediction algorithm
A function in the default SuperLearner library used by tmle. This wrapper is built into SuperLearner 2.0-6, and is defined here for backwards-compatibility with older versions of SuperLearner. This function may be incorporated into any user-supplied SuperLearner library, but will not ordinarily be called directly by the user.
tmle
(Package: tmle) :
Targeted Maximum Likelihood Estimation
Targeted maximum likelihood estimation of parameters of a marginal structural model, and of marginal treatment effects of a binary point treatment on an outcome. In addition to the additive treatment effect, risk ratio and odds ratio estimates are reported for binary outcomes. The tmle function is generally called with arguments (Y,A,W), where Y is a continuous or binary outcome variable, A is a binary treatment variable, (A=1 for treatment, A=0 for control), and W is a matrix or dataframe of baseline covariates. The population mean outcome is calculated when there is no variation in A. If values of binary mediating variable Z are supplied, estimates are returned at each level of Z. Missingness in the outcome is accounted for in the estimation procedure if missingness indicator Delta is 0 for some observations. Repeated measures can be identified using the id argument.
providing 
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