sdafilter: Symmetrized Data Aggregation
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 |
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