This R package 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.

## 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 devel
BiocManager::install(version='devel')

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., 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. (2019a). ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls. arXiv preprint, https://arxiv.org/abs/1905.11465.

Tian, J. and Ramdas, A. (2019b). Online control of the familywise error rate. arXiv preprint, https://arxiv.org/abs/1910.04900.

Zrnic, T., Ramdas, A. and Jordan, M.I. (2018). Asynchronous Online Testing of Multiple Hypotheses. arXiv preprint, https://arxiv.org/abs/1812.05068.