Data-adaptive statistics for multiple testing in high-dimensional problems
Authors: Wilson Cai and Nima Hejazi
The adaptest R package is a tool for performing multiple testing on
effect sizes under high-dimensional settings, using the approach of
data-adaptive statistical target parameters and inference. For technical
details on the data-adaptive multiple testing procedure, consult Cai,
Hejazi, and Hubbard. For an introduction to statistical inference
procedures using data-adaptive target parameters, the interested reader
is directed to Hubbard, Kherad-Pajouh, and van der Laan (2016).
Install the most recent stable release from GitHub via
devtools:
devtools::install_github("wilsoncai1992/adaptest")If you encounter any bugs or have any specific feature requests, please file an issue.
It is our hope that adaptest will grow to be widely adopted as a tool
for employing data-adaptive multiple hypothesis testing procedures in
high-dimensional and complex problem settings. To that end,
contributions are very welcome, though we ask that interested
contributors consult our contribution guidelines
prior to submitting a pull request.
After using the adaptest R package, please cite it:
@article{cai2017adaptest,
doi = {},
url = {},
year = {2017},
month = {},
publisher = {The Open Journal},
volume = {submitted},
number = {},
author = {Cai, Weixin and Hubbard, Alan E and Hejazi, Nima S},
title = {adaptest: Data-Adaptive Statistics for High-Dimensional Testing
in R},
journal = {The Journal of Open Source Software}
}
The software contents of this repository are distributed under the GPL-2
license. See file LICENSE for details.
Cai, Weixin, Nima S Hejazi, and Alan E Hubbard. “Data-Adaptive Statistics for Multiple Hypothesis Testing in High-Dimensional Settings.” https://arxiv.org/abs/1704.07008.
Hubbard, Alan E, Sara Kherad-Pajouh, and Mark J van der Laan. 2016. “Statistical Inference for Data Adaptive Target Parameters.” The International Journal of Biostatistics 12 (1): 3–19.