We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2020), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", <arXiv:2002.11992>.
| Version: | 1.0.0 |
| Imports: | glmnet, glasso, huge, POET, stats |
| Suggests: | testthat (≥ 2.1.0) |
| Published: | 2020-03-19 |
| Author: | Lilun Du [aut, cre], Xu Guo [ctb], Wenguang Sun [ctb], Changliang Zou [ctb] |
| Maintainer: | Lilun Du <dulilun at ust.hk> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | sdafilter results |
| Reference manual: | sdafilter.pdf |
| Package source: | sdafilter_1.0.0.tar.gz |
| Windows binaries: | r-devel: sdafilter_1.0.0.zip, r-release: sdafilter_1.0.0.zip, r-oldrel: sdafilter_1.0.0.zip |
| macOS binaries: | r-release: sdafilter_1.0.0.tgz, r-oldrel: sdafilter_1.0.0.tgz |
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