DESCRIPTION

Package: tmleCommunity
Title: Targeted Maximum Likelihood Estimation for Hierarchical Data
Version: 0.1.0
Authors@R: c(person("Chi", "Zhang", email = "chi.zhang@berkeley.edu", role = c("aut", "cre")), 
             person("Oleg", "Sofrygin", email = "oleg.sofrygin@gmail.com", role = c("aut")), 
             person("Jennifer", "Ahern", email = "jahern@berkeley.edu", role = c("aut")),
             person("Mark J.", "van der Laan", email = "laan@berkeley.edu", role = c("aut", "ths")))             
Author: Chi Zhang  [aut, cre],
     Oleg Sofrygin [aut],
     Jennifer Ahern [aut],
     Mark J. van der Laan [aut, ths]
Maintainer: Chi Zhang <chi.zhang@berkeley.edu>             
Description: Targeted minimum loss-based estimation (TMLE) of the average causal effect of 
    community-based intervention(s) at a single time point on an individual-based outcome of 
    interest. It provides three approaches to analyze hierarchical data: community-level TMLE, 
    inidividual-level TMLE and stratified TMLE. Implementations of the inverse-probability-of-
    treatment-weighting (IPTW) and the G-computation formula (GCOMP) are also available for 
    each approach. The package supports multivariate arbitrary interventions (deterministic 
    or stochastic) with a binary or continuous outcome. The tmleCommunity() function calculates 
    the marginal treatment effect among independent community units (or i.i.d individual units 
    if no hierarchical structure) using TMLE. Besides, it allows user-specified data-adaptive 
    machine learning algorithms through SuperLearner and h2oEnsemble packages. The input dataset 
    should be made up of rows of community-specific and individual-specific observations, with 
    each row i (in community j) containing random variables (W_{i,j}, E_j, A_j, Y_{i,j}), where 
    E_j represents a vector of community j's environmental baseline covariates, W_{i,j}represents
    a vector of individual i's individual-level baseline covariates, A_j is the exposure(s) 
    (can be univariate or multivariate, can be binary, categorical or continuous) assigned or 
    naturally occurred in community j and Y_{i,j} is i's outcome (either binary or continuous). 
    More details can be found in '?tmleCommunity-package' and '?tmleCommunity'.
URL: https://github.com/chizhangucb/tmleCommunity
BugReports: https://github.com/chizhangucb/tmleCommunity/issues
Depends: R (>= 3.3.0)
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: assertthat,
    data.table,
    Matrix,
    R6,
    stats,
    speedglm,
    methods,
    Hmisc,
    plm,
    SuperLearner,
    h2o, 
    h2oEnsemble,
    sl3
Suggests: doParallel,
    foreach,
    simcausal,
    testthat,
    knitr,
    glmnet,
    gam,
    arm,
    randomForest
RoxygenNote: 6.1.1
Collate: 
    'GeneralUtilities.R'
    'BinaryOutModelClass.R'
    'DatKeepClass.R'
    'GenericModelClasses.R'
    'MonteCarloSimClass.R'
    'hbarDensityModel.R'
    'tmleCommunity-package.R'
    'tmleCommunity.R'
    'zzz.R'