crumble: Flexible and General Mediation Analysis Using Riesz Representers
Implements a modern, unified estimation strategy for common
mediation estimands (natural effects, organic effects, interventional effects,
and recanting twins) in combination with modified treatment policies as
described in Liu, Williams, Rudolph, and Díaz (2024)
<doi:10.48550/arXiv.2408.14620>. Estimation makes use of recent advancements
in Riesz-learning to estimate a set of required nuisance parameters with
deep learning. The result is the capability to estimate mediation effects with
binary, categorical, continuous, or multivariate exposures with
high-dimensional mediators and mediator-outcome confounders using machine
learning.
| Version: |
0.1.2 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
checkmate, Matrix, origami, torch, Rsymphony, purrr, cli, S7, data.table, coro, generics, lmtp, mlr3superlearner, progressr, ife (≥ 0.1.0) |
| Suggests: |
testthat (≥ 3.0.0), truncnorm, mma |
| Published: |
2024-12-02 |
| DOI: |
10.32614/CRAN.package.crumble |
| Author: |
Nicholas Williams 
[aut, cre, cph],
Richard Liu [ctb],
Iván Díaz [aut,
cph] |
| Maintainer: |
Nicholas Williams <ntwilliams.personal at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
crumble results |
Documentation:
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