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CRAN: Package adace

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: Downloads: Linking:

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