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>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: NEWS
CRAN checks: adace results


Reference manual: adace.pdf


Package source: adace_1.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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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