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<!-- badges: start --> [![R-CMD-check](]( [![codecov](]( <!-- badges: end --> # onlineFDR <img src="man/figures/logo.png" align="right" /> `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: ``` r 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: ``` r # install.packages("devtools") # If devtools not installed devtools::install_github("dsrobertson/onlineFDR") ``` ## Documentation Documentation is hosted at <> To view the vignette for the version of this package installed in your system, start R and enter: ``` r 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*, <>. 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*, <>. Robertson, D.S., Wason, J.M.S. and Ramdas, A. (2022). Online multiple hypothesis testing for reproducible research. *arXiv preprint*, <>. 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, <>. 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.