onlineFDR
onlineFDR allows users to control the false discovery rate (FDR) or
familywise error rate (FWER) for online hypothesis testing, where
hypotheses arrive in a stream. In this framework, a null hypothesis is
rejected based on the evidence against it and on the previous rejection
decisions.
Installation
You can install onlineFDR from Bioconductor (release or devel) or from
GitHub:
Bioconductor (release)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("onlineFDR")
Bioconductor (devel)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(version = "devel")
BiocManager::install("onlineFDR")
GitHub (development snapshot)
# install.packages("devtools") # If devtools not installed
devtools::install_github("dsrobertson/onlineFDR")
Documentation
Documentation is hosted at https://dsrobertson.github.io/onlineFDR/
To view the vignettes for the version of this package installed in your
system, start R and enter:
browseVignettes("onlineFDR")
References
Aharoni, E. and Rosset, S. (2014). Generalized alpha-investing:
definitions, optimality results and application to public databases.
Journal of the Royal Statistical Society (Series B), 76(4):771–794.
Fischer, L., Bofill Roig, M. and Brannath, W. (2024). An exhaustive
ADDIS principle for online FWER control. Biometrical Journal,
66(3):2300237.
Foster, D. and Stine, R. (2008). alpha-investing: a procedure for
sequential control of expected false discoveries. Journal of the Royal
Statistical Society (Series B), 70(2):429-444.
Javanmard, A., and Montanari, A. (2015). On Online Control of False
Discovery Rate. arXiv preprint, https://arxiv.org/abs/1502.06197.
Javanmard, A., and Montanari, A. (2018). Online Rules for Control of
False Discovery Rate and False Discovery Exceedance. Annals of
Statistics, 46(2):526-554.
Ramdas, A., Yang, F., Wainwright, M.J. and Jordan, M.I. (2017). Online
control of the false discovery rate with decaying memory. Advances in
Neural Information Processing Systems 30, 5650-5659.
Ramdas, A., Zrnic, T., Wainwright, M.J. and Jordan, M.I. (2018).
SAFFRON: an adaptive algorithm for online control of the false discovery
rate. Proceedings of the 35th International Conference on Machine
Learning, 80:4286-4294.
Robertson, D.S. and Wason, J.M.S. (2018). Online control of the false
discovery rate in biomedical research. arXiv preprint,
https://arxiv.org/abs/1809.07292.
Robertson, D.S., Wason, J.M.S. and Ramdas, A. (2023). Online multiple
hypothesis testing. Statistical Science, 38(4):557-575,
https://doi.org/10.1214/23-STS901.
Robertson, D.S., Wildenhain, J., Javanmard, A. and Karp, N.A. (2019).
onlineFDR: an R package to control the false discovery rate for growing
data repositories. Bioinformatics, 35(20):4196-4199,
https://doi.org/10.1093/bioinformatics/btz191.
Tian, J. and Ramdas, A. (2019). ADDIS: an adaptive discarding algorithm
for online FDR control with conservative nulls. Advances in Neural
Information Processing Systems 32, 9388-9396.
Tian, J. and Ramdas, A. (2021). Online control of the familywise error
rate. Statistical Methods in Medical Research, 30(4):976-993.
Zrnic, T., Jiang, D., Ramdas, A. and Jordan, M.I. (2020). The Power of
Batching in Multiple Hypothesis Testing. International Conference on
Artificial Intelligence and Statistics, PMLR, 108:3806-3815.
Zrnic, T., Ramdas, A. and Jordan, M.I. (2021). Asynchronous Online
Testing of Multiple Hypotheses. Journal of Machine Learning Research,
22(33):1-39.
