The `tmlenet`

R package performs estimation of average causal effects for single time point interventions in network-dependent (non-IID) data in the presence of interference and/or spillover. Currently implemented estimation algorithms are the targeted maximum likelihood estimation (TMLE), Horvitz-Thompson or the inverse-probability-of-treatment (IPTW) estimator and the parametric G-computation estimator. The user-specified interventions can be either static, dynamic or stochastic. Asymptotically correct influence-curve-based confidence intervals are also constructed for the TMLE and IPTW. See the paper below for more information on the estimation methodology employed by the `tmlenet`

R package:

M. J. van der Laan, “Causal inference for a population of causally connected units,” J. Causal Inference J. Causal Infer., vol. 2, no. 1, pp. 13–74, 2014.

To install the development version of `tmlenet`

(requires the `devtools`

package):

`devtools::install_github('osofr/tmlenet', build_vignettes = FALSE)`

Once the package is installed, please refer to the help file `?'tmlenet-package'`

and `tmlenet`

function documentation for details and examples:

```
?'tmlenet-package'
?tmlenet
```

The input data are assumed to consist of rows of unit-specific observations, with 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`

(also referred to as `i`

's "**network**"), `W.i`

is a vector of `i`

's baseline covariates, `A.i`

is `i`

's exposure (either binary, categorical or continuous) and `Y.i`

is `i`

's binary outcome.

Each exposure `A.i`

depends on (possibly multivariate) baseline summary measure(s) `sW.i`

, where `sW.i`

can be any user-specified function of `i`

's baseline covariates `W.i`

and the baseline covariates of `i`

's friends in `F.i`

(all `W.j`

such that `j`

is in `F.i`

). Similarly, each outcome `Y.i`

depends on `sW.i`

and (possibly multivariate) summary measure(s) `sA.i`

, where `sA.i`

can be any user-specified function of `i`

's baseline covariates and exposure (`W.i`

,`A.i`

) and the baseline covariates and exposures of `i`

's friends (all `W.j`

,`A.j`

such that `j`

is in `i`

's friend set `F.i`

).

The summary measures (`sW.i`

,`sA.i`

) are defined simultaneously for all `i`

with functions `def.sW`

and `def.sA`

. It is assumed that (`sW.i`

,`sA.i`

) have the same dimensionality across `i`

. The function `eval.summaries`

can be used for evaluating these summary measures.

All estimation is performed by calling the `tmlenet`

function. The vector of friends `F.i`

can be specified either as a single column in the input data (where each `F.i`

is a string of friend IDs or friend row numbers delimited by character `sep`

) or as a separate input matrix of network IDs (where each row is a vector of friend IDs or friend row numbers). Specifying the network as a matrix generally results in significant improvements to run time. See `tmlenet`

function help file for additional details on how to specify these and the rest of the input arguments.

...

To cite `tmlenet`

in publications, please use: > Sofrygin O, van der Laan MJ (2015). *tmlenet: Targeted Maximum Likelihood Estimation for Networks.* R package version 0.1.

The development of this package was funded through an NIH grant (R01 AI074345-07).

This software is distributed under the GPL-2 license.