lmtp: Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies

Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, and Hoffman (<arXiv:2006.01366>), traditional point treatment, and traditional longitudinal effects. Continuous, binary, and categorical treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Estimation is enhanced using the Super Learner from 'sl3' available for download from GitHub using 'remotes::install_github("tlverse/sl3@devel")'.

Version:	0.0.5
Depends:	R (≥ 2.10)
Imports:	slider, stats, nnls, cli, utils, R6, generics, origami, future (≥ 1.17.0), progressr
Suggests:	testthat (≥ 2.1.0), covr, rmarkdown, knitr, ranger, twang
Enhances:	sl3 (≥ 1.3.7)
Published:	2020-07-18
Author:	Nicholas Williams [aut, cre, cph], Iván Díaz [aut, cph]
Maintainer:	Nicholas Williams <niw4001 at med.cornell.edu>
License:	MIT + file LICENSE
NeedsCompilation:	no
Citation:	lmtp citation info
Materials:	README
CRAN checks:	lmtp results

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