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", .


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.0.0 by Lilun Du, 7 months ago

Browse source code at

Authors: Lilun Du [aut, cre] , Xu Guo [ctb] , Wenguang Sun [ctb] , Changliang Zou [ctb]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports glmnet, glasso, huge, POET, stats

Suggests testthat

See at CRAN