Man pages for osofr/tmlenet
Targeted Maximum Likelihood Estimation for Network Data

BinDatR6 class for storing the design matrix and binary outcome for...
BinOutModelR6 class for fitting and making predictions for a single...
CategorSummaryModelR6 class for fitting and predicting joint probability for a...
ContinSummaryModelR6 class for fitting and predicting joint probability for a...
DatNetR6 class for storing and managing already evaluated summary...
DatNet.sWsAR6 class for storing and managing the combined summary...
datO_inputAn example simulated dataset used for testing
DefineSummariesClassR6 class for parsing and evaluating user-specified summary...
def_sWDefine Summary Measures sA and sW
df_netKmax2An example of a row-dependent dataset with known network of...
df_netKmax6An example of a row-dependent dataset with known network of...
eval.summariesEvaluate Summary Measures sA and sW
glmR6glmR6
logisfitR6logisfitR6
mcEvalPsiR6 class for Monte-Carlo evaluation of various substitution...
NetInd_mat_Kmax6An example of a network ID matrix
OdataDTR6 class for storing and managing the user-input data.
print_tmlenet_optsPrint Current Option Settings for 'tmlenet'
RegressionClassR6 class that defines regression models evaluating P(sA|sW),...
speedglmR6speedglmR6
SummariesModelR6 class for fitting and predicting model P(sA|sW) under...
tmlenetEstimate Average Network Effects For Arbitrary (Stochastic)...
tmlenet_optionsSetting Options for 'tmlenet'
tmlenet-packageTargeted Maximum Likelihood Estimation for Network Data
osofr/tmlenet documentation built on May 24, 2019, 4:58 p.m.