DESCRIPTION

Package: tmlenet
Title: Targeted Maximum Likelihood Estimation for Network Data
Version: 0.1.9
Authors@R: c(
    person("Oleg", "Sofrygin", role=c("aut", "cre"), email="oleg.sofrygin@gmail.com"),
    person(c("Mark", "J."), "van der Laan", role="aut", email="laan@berkeley.edu"))
Description: 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.
URL: https://github.com/osofr/tmlenet
BugReports: https://github.com/osofr/tmlenet/issues
Depends:
    R (>= 3.2.0)
Imports:
    assertthat,
    data.table,
    Matrix,
    methods,
    R6,
    Rcpp,
    simcausal,
    speedglm,
    stats,
    stringr
LinkingTo: Rcpp
Suggests:
    doParallel,
    foreach,
    igraph,
    knitr,
    locfit,
    matrixStats,
    RUnit
VignetteBuilder: knitr
License: GPL-2
LazyData: true
RoxygenNote: 6.0.1
Collate: 
    'GlmAlgorithmClass.R'
    'BinOutModelClass.R'
    'DatNetClass.R'
    'DefineSummariesClass.R'
    'Inference.R'
    'RcppExports.R'
    'SummariesModelClass.R'
    'dhist.r'
    'mcEvalPsiClass.R'
    'modelhdensity.R'
    'parserfunctions_R6.r'
    'tmlenet-package.R'
    'tmlenet.R'
    'zzz.R'