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.">

crumble: Flexible and General Mediation Analysis Using Riesz Representers (original) (raw)

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 ORCID iD [aut, cre, cph], Richard Liu [ctb], Iván Díaz ORCID iD [aut, cph]
Maintainer: Nicholas Williams <ntwilliams.personal at gmail.com>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: crumble results

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