BinDat                  R6 class for storing the design matrix and
                        binary outcome for a single logistic regression
BinOutModel             R6 class for fitting and making predictions for
                        a single logistic regression with binary
                        outcome B, P(B | PredVars)
CategorSummaryModel     R6 class for fitting and predicting joint
                        probability for a univariate categorical
                        summary measure sA[j]
ContinSummaryModel      R6 class for fitting and predicting joint
                        probability for a univariate continuous summary
                        measure sA[j]
DatNet                  R6 class for storing and managing already
                        evaluated summary measures 'sW' or 'sA' (but
                        not both at the same time).
DatNet.sWsA             R6 class for storing and managing the combined
                        summary measures 'sW' & 'sA' from DatNet
                        classes.
DefineSummariesClass    R6 class for parsing and evaluating
                        user-specified summary measures (in
                        'exprs_list')
Define_sVar             R6 class for parsing and evaluating node R
                        expressions.
NetInd_mat_Kmax6        An example of a network ID matrix
RegressionClass         R6 class that defines regression models
                        evaluating P(sA|sW), for summary measures
                        (sW,sA)
SummariesModel          R6 class for fitting and predicting model
                        P(sA|sW) under g.star or g.0
def.sW                  Define Summary Measures sA and sW
df_netKmax2             An example of a row-dependent dataset with
                        known network of at most 2 friends.
df_netKmax6             An example of a row-dependent dataset with
                        known network of at most 6 friends.
eval.summaries          Evaluate Summary Measures sA and sW
mcEvalPsi               R6 class for Monte-Carlo evaluation of various
                        substitution estimators for exposures generated
                        under the user-specified stochastic
                        intervention function.
print_tmlenet_opts      Print Current Option Settings for 'tmlenet'
tmlenet                 Estimate Average Network Effects For Arbitrary
                        (Stochastic) Interventions
tmlenet-package         Targeted Maximum Likelihood Estimation for
                        Network Data
tmlenet_options         Setting Options for 'tmlenet'
