Package: swfdr 1.31.0
Simina M. Boca
swfdr: Estimation of the science-wise false discovery rate and the false discovery rate conditional on covariates
This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the false discovery rate conditional on covariates, using a regression framework, as per Boca and Leek, "A direct approach to estimating false discovery rates conditional on covariates," 2018, PeerJ.
Authors:
swfdr_1.31.0.tar.gz
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swfdr.pdf |swfdr.html✨
swfdr/json (API)
NEWS
| # Install 'swfdr' in R: |
| install.packages('swfdr', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/leekgroup/swfdr/issues
- BMI_GIANT_GWAS_sample - Subset of SNPs from meta-analysis of BMI GWAS study.
- journals_pVals - P-values from abstracts from articles in 5 biomedical journals (American Journal of Epidemiology, BMJ, JAMA, Lancet, New England Journal of Medicine), over 11 years (2000-2010).
On BioConductor:swfdr-1.31.0(bioc 3.20)swfdr-1.30.0(bioc 3.19)
Last updated 2 months agofrom:1ba4d3bc15
Exports:calculateSwfdrlm_pi0lm_qvalue
Dependencies:
