Package: flexCausal
Title: Causal Effect Estimation via Doubly Robust One-Step Estimators
        and TMLE in Graphical Models with Unmeasured Variables
Version: 0.1.0
Date: 2026-03-18
Authors@R: 
    c(person("Anna", "Guo", 
         email = "guo.anna617@gmail.com",
         role = c("aut", "cre"),
         comment = c(GitHub = "https://github.com/annaguo-bios")),
      person("Razieh", "Nabi",
         email = "razieh.nabi@emory.edu",
         role = "aut"))
Description: Provides doubly robust one-step and targeted maximum likelihood
    (TMLE) estimators for average causal effects in acyclic directed mixed
    graphs (ADMGs) with unmeasured variables. Automatically determines whether
    the treatment effect is identified via backdoor adjustment or the extended
    front-door functional, and dispatches to the appropriate estimator.
    Supports incorporation of machine learning algorithms via 'SuperLearner'
    and cross-fitting for nuisance estimation. Methods are described in Guo and Nabi (2024) <doi:10.48550/arXiv.2409.03962>.
License: GPL-3
LazyData: true
URL: https://github.com/annaguo-bios/flexCausal
BugReports: https://github.com/annaguo-bios/flexCausal/issues
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.3.3.9000
Imports: rlang, dplyr, SuperLearner, densratio, MASS, mvtnorm, stats,
        utils
Depends: R (>= 4.1)
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), earth, ranger
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-03-24 14:58:47 UTC; apple
Author: Anna Guo [aut, cre] (GitHub: https://github.com/annaguo-bios),
  Razieh Nabi [aut]
Maintainer: Anna Guo <guo.anna617@gmail.com>
Repository: CRAN
Date/Publication: 2026-03-29 15:20:08 UTC
Built: R 4.6.0; ; 2026-03-29 23:51:27 UTC; windows
