tmlenet: Targeted Maximum Likelihood Estimation for Network Data

Estimation of average causal effects for single time point interventions in network-dependent data (e.g., in the presence of spillover and/or interference). Supports arbitrary interventions (static or stochastic). Implemented estimation algorithms are the targeted maximum likelihood estimation (TMLE), the inverse-probability-of-treatment (IPTW) estimator and the parametric G-computation formula estimator. Asymptotically correct influence-curve-based confidence intervals are constructed for the TMLE and IPTW. The data are assumed to consist of rows of unit-specific observations, each row i represented by variables (F.i,W.i,A.i,Y.i), where F.i is a vector of friend IDs of unit i (i's network), W.i is a vector of i's baseline covariates, A.i is i's exposure (can be binary, categorical or continuous) and Y.i is i's binary outcome. Exposure A.i depends on (multivariate) user-specified baseline summary measure(s) sW.i, where sW.i is any function of i's baseline covariates W.i and the baseline covariates of i's friends in F.i. Outcome Y.i depends on sW.i and (multivariate) user-specified summary measure(s) sA.i, where sA.i is any function of i's baseline covariates and exposure (W.i,A.i) and the baseline covariates and exposures of i's friends. The summary measures are defined with functions def.sW and def.sA. See ?'tmlenet-package' for a general overview.

Author
Oleg Sofrygin [aut, cre], Mark J. van der Laan [aut]
Date of publication
2015-09-28 09:26:59
Maintainer
Oleg Sofrygin <oleg.sofrygin@gmail.com>
License
GPL-2
Version
0.1.0
URLs

View on CRAN

Man pages

BinDat
R6 class for storing the design matrix and binary outcome for...
BinOutModel
R6 class for fitting and making predictions for a single...
CategorSummaryModel
R6 class for fitting and predicting joint probability for a...
ContinSummaryModel
R6 class for fitting and predicting joint probability for a...
DatNet
R6 class for storing and managing already evaluated summary...
DatNet.sWsA
R6 class for storing and managing the combined summary...
DefineSummariesClass
R6 class for parsing and evaluating user-specified summary...
Define_sVar
R6 class for parsing and evaluating node R expressions.
def.sW
Define Summary Measures sA and sW
df_netKmax2
An example of a row-dependent dataset with known network of...
df_netKmax6
An example of a row-dependent dataset with known network of...
eval.summaries
Evaluate Summary Measures sA and sW
mcEvalPsi
R6 class for Monte-Carlo evaluation of various substitution...
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),...
SummariesModel
R6 class for fitting and predicting model P(sA|sW) under...
tmlenet
Estimate Average Network Effects For Arbitrary (Stochastic)...
tmlenet_options
Setting Options for 'tmlenet'
tmlenet-package
Targeted Maximum Likelihood Estimation for Network Data

Files in this package

tmlenet
tmlenet/tests
tmlenet/tests/examples
tmlenet/tests/examples/2_defsWsA_examples.R
tmlenet/tests/examples/1_tmlenet_example.R
tmlenet/tests/examples/3_eval.summaries_examples.R
tmlenet/tests/RUnit
tmlenet/tests/RUnit/RUnit_tests_00a.R
tmlenet/tests/RUnit/RUnit_tests_03_iidcont_sA_tests.R
tmlenet/tests/RUnit/RUnit_tests_00b_sWsAparser.R
tmlenet/tests/RUnit/RUnit_tests_05_categorical_sA.R
tmlenet/tests/RUnit/RUnit_tests_01_tmlenet_errors.R
tmlenet/tests/RUnit/RUnit_tests_01_tmlenet_example.R
tmlenet/tests/RUnit/RUnit_tests_04_netcont_sA_tests.R
tmlenet/tests/RUnit/RUnit_tests_02_fit_iptw_density.R
tmlenet/tests/RUnit/RUnit_tests_06_netcont_sA_test_hinputfit.R
tmlenet/tests/datgen_nets
tmlenet/tests/datgen_nets/sim3_datgen_k6.R
tmlenet/tests/test-all.R
tmlenet/src
tmlenet/src/RcppExports.cpp
tmlenet/src/glm_binom_family.cpp
tmlenet/NAMESPACE
tmlenet/NEWS
tmlenet/data
tmlenet/data/df_netKmax6.rda
tmlenet/data/NetInd_mat_Kmax6.rda
tmlenet/data/df_netKmax2.rda
tmlenet/R
tmlenet/R/DatNetClass.R
tmlenet/R/mcEvalPsiClass.R
tmlenet/R/tmlenet-package.R
tmlenet/R/Inference.R
tmlenet/R/RcppExports.R
tmlenet/R/deprecated.R
tmlenet/R/BinOutModelClass.R
tmlenet/R/dhist.r
tmlenet/R/modelhdensity.R
tmlenet/R/parserfunctions_R6.r
tmlenet/R/tmlenet.R
tmlenet/R/DefineSummariesClass.R
tmlenet/R/zzz.R
tmlenet/R/SummariesModelClass.R
tmlenet/README.md
tmlenet/MD5
tmlenet/DESCRIPTION
tmlenet/man
tmlenet/man/BinDat.Rd
tmlenet/man/BinOutModel.Rd
tmlenet/man/df_netKmax2.Rd
tmlenet/man/DatNet.sWsA.Rd
tmlenet/man/eval.summaries.Rd
tmlenet/man/Define_sVar.Rd
tmlenet/man/NetInd_mat_Kmax6.Rd
tmlenet/man/tmlenet_options.Rd
tmlenet/man/tmlenet-package.Rd
tmlenet/man/tmlenet.Rd
tmlenet/man/ContinSummaryModel.Rd
tmlenet/man/DefineSummariesClass.Rd
tmlenet/man/CategorSummaryModel.Rd
tmlenet/man/mcEvalPsi.Rd
tmlenet/man/SummariesModel.Rd
tmlenet/man/RegressionClass.Rd
tmlenet/man/def.sW.Rd
tmlenet/man/DatNet.Rd
tmlenet/man/df_netKmax6.Rd
tmlenet/man/print_tmlenet_opts.Rd