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

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Repository

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.

README.Rmd

---
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%"
)
```

[![R-CMD-check](https://github.com/dsrobertson/onlineFDR/workflows/R-CMD-check/badge.svg)](https://github.com/dsrobertson/onlineFDR/actions)
[![codecov](https://codecov.io/gh/dsrobertson/onlineFDR/branch/master/graph/badge.svg)](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.

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