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adace: Estimator of the Adherer Average Causal Effect

Estimate the causal treatment effect for subjects that can adhere to one or both of the treatments. Given longitudinal data with missing observations, consistent causal effects are calculated. Unobserved potential outcomes are estimated through direct integration as described in: Qu et al., (2019) <doi:10.1080/19466315.2019.1700157> and Zhang et. al., (2021) <doi:10.1080/19466315.2021.1891965>.

Version: 1.0.2
Depends: R (≥ 4.0.0)
Imports: reshape2, pracma
Suggests: testthat (≥ 3.0.0), cubature (≥ 2.0.4), MASS (≥ 7.3-55)
Published: 2023-08-28
DOI: 10.32614/CRAN.package.adace
Author: Jiaxun Chen [aut], Rui Jin [aut], Yongming Qu [aut], Run Zhuang [aut, cre], Ying Zhang [aut], Eli Lilly and Company [cph]
Maintainer: Run Zhuang <capecod0321 at gmail.com>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: NEWS
CRAN checks: adace results

Documentation:

Reference manual: adace.pdf

Downloads:

Package source: adace_1.0.2.tar.gz
Windows binaries: r-devel: adace_1.0.2.zip, r-release: adace_1.0.2.zip, r-oldrel: adace_1.0.2.zip
macOS binaries: r-release (arm64): adace_1.0.2.tgz, r-oldrel (arm64): adace_1.0.2.tgz, r-release (x86_64): adace_1.0.2.tgz, r-oldrel (x86_64): adace_1.0.2.tgz
Old sources: adace archive

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