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Assess whether and how a specific continuous or categorical exposure affects the outcome of interest through one- or multi-dimensional mediators using an adaptive bootstrap (AB) approach. The AB method allows to make inference for composite null hypotheses of no mediation effect, providing valid type I error control and thus optimizes statistical power. For more technical details, refer to He, Song and Xu (2024) <doi:10.1093/jrsssb/qkad129>.
Version: | 1.1 |
Imports: | boot, stats |
Suggests: | testthat (≥ 3.0.0) |
Published: | 2024-10-25 |
DOI: | 10.32614/CRAN.package.abima |
Author: | Canyi Chen [aut, cre], Yinqiu He [aut], Gongjun Xu [aut], Peter X.-K. Song [aut, cph] |
Maintainer: | Canyi Chen <cychen.stats at outlook.com> |
BugReports: | https://github.com/canyi-chen/abima/issues |
License: | MIT + file LICENSE |
URL: | https://websites.umich.edu/~songlab/software.html#ABIMA, https://github.com/canyi-chen/abima |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | abima results |
Reference manual: | abima.pdf |
Package source: | abima_1.1.tar.gz |
Windows binaries: | r-devel: abima_1.1.zip, r-release: abima_1.1.zip, r-oldrel: abima_1.1.zip |
macOS binaries: | r-release (arm64): abima_1.1.tgz, r-oldrel (arm64): abima_1.1.tgz, r-release (x86_64): abima_1.1.tgz, r-oldrel (x86_64): abima_1.1.tgz |
Old sources: | abima archive |
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These binaries (installable software) and packages are in development.
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