onlineFDR allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online hypothesis testing, where hypotheses arrive sequentially in a stream. In this framework, a null hypothesis is rejected based only on the previous decisions, as the future p-values and the number of hypotheses to be tested are unknown.
To install the latest (development) version of the onlineFDR package from Bioconductor, please run the following code:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") # The following initializes usage of Bioc BiocManager::install() BiocManager::install("onlineFDR")
Alternatively, you can install the package directly from GitHub:
# install.packages("devtools") # If devtools not installed devtools::install_github("dsrobertson/onlineFDR")
We recommend the GitHub version for faster implementations of the algorithms.
Documentation is hosted at https://dsrobertson.github.io/onlineFDR/
To view the vignette for the version of this package installed in your system, start R and enter:
Aharoni, E. and Rosset, S. (2014). Generalized alpha-investing: definitions, optimality results and applications to public databases. Journal of the Royal Statistical Society (Series B), 76(4):771–794.
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), 29(4):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 in 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., Wildenhain, J., Javanmard, A. and Karp, N.A. (2019). onlineFDR: an R package to control the false discovery rate for growing data repositories. Bioinformatics, 35:4196-4199, 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, 9388-9396.
Tian, J. and Ramdas, A. (2021). Online control of the familywise error rate. Statistical Methods for Medical Research (to appear), https://arxiv.org/abs/1910.04900.
Zrnic, T., Jiang D., Ramdas A. and Jordan M. (2020). The Power of Batching in Multiple Hypothesis Testing. International Conference on Artificial Intelligence and Statistics, 3806-3815.
Zrnic, T., Ramdas, A. and Jordan, M.I. (2021). Asynchronous Online Testing of Multiple Hypotheses. Journal of Machine Learning Research (to appear), https://arxiv.org/abs/1812.05068.