https://github.com/dsrobertson/onlinefdr
Clone of the Bioconductor repository for the onlineFDR package. See https://bioconductor.org/packages/devel/bioc/html/onlineFDR.html for the official development version, and https://dsrobertson.github.io/onlineFDR/ for easy access to documentation.
https://github.com/dsrobertson/onlinefdr
error-rate-control fdr fwer hypothesis-testing
Last synced: about 1 year ago
JSON representation
Clone of the Bioconductor repository for the onlineFDR package. See https://bioconductor.org/packages/devel/bioc/html/onlineFDR.html for the official development version, and https://dsrobertson.github.io/onlineFDR/ for easy access to documentation.
- Host: GitHub
- URL: https://github.com/dsrobertson/onlinefdr
- Owner: dsrobertson
- Created: 2018-04-13T15:27:02.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2023-04-12T10:38:35.000Z (about 3 years ago)
- Last Synced: 2025-04-26T08:39:09.469Z (about 1 year ago)
- Topics: error-rate-control, fdr, fwer, hypothesis-testing
- Language: R
- Homepage: https://dsrobertson.github.io/onlineFDR/
- Size: 11.7 MB
- Stars: 14
- Watchers: 3
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
README
---
output:
md_document:
variant: gfm
github_document: default
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
[](https://github.com/dsrobertson/onlineFDR/actions)
[](https://codecov.io/gh/dsrobertson/onlineFDR)
# 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:
```{r, message=FALSE, warning=FALSE, results=FALSE}
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, message=FALSE, warning=FALSE, results=FALSE}
# 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:
```{r, message=FALSE, warning=FALSE, results=FALSE}
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.