## 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.

Version: |
0.1.0 |

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 |

Published: |
2015-09-28 |

Author: |
Oleg Sofrygin [aut, cre],
Mark J. van der Laan [aut] |

Maintainer: |
Oleg Sofrygin <oleg.sofrygin at gmail.com> |

BugReports: |
https://github.com/osofr/tmlenet/issues |

License: |
GPL-2 |

URL: |
https://github.com/osofr/tmlenet |

NeedsCompilation: |
yes |

Materials: |
README NEWS |

CRAN checks: |
tmlenet results |

#### Downloads:

#### Reverse dependencies: