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

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")

Documentation

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:

browseVignettes("onlineFDR")

References

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., Wason, J.M.S. and Ramdas, A. (2022). Online multiple hypothesis testing for reproducible research. arXiv preprint, https://arxiv.org/abs/2208.11418.

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, 30(4):976–993.

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, PMLR, 108:3806-3815.

Zrnic, T., Ramdas, A. and Jordan, M.I. (2021). Asynchronous Online Testing of Multiple Hypotheses. Journal of Machine Learning Research, 22:1-33.