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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 |
| 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 |
| Reference manual: | adace.pdf |
| 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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