Targeted Maximum Likelihood Estimation for Network Data

Documentation for package ‘tmlenet’ version 0.1.0

Help Pages

tmlenet-package	Targeted Maximum Likelihood Estimation for Network Data
+.DefineSummariesClass	Define Summary Measures sA and sW
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.
def.sA	Define Summary Measures sA and sW
def.sW	Define Summary Measures sA and sW
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.
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.
NetInd_mat_Kmax6	An example of a network ID matrix
print_tmlenet_opts	Print Current Option Settings for 'tmlenet'
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
tmlenet	Estimate Average Network Effects For Arbitrary (Stochastic) Interventions
tmlenet_options	Setting Options for 'tmlenet'