--- a/README.md
+++ b/README.md
@@ -1,255 +1,171 @@
-
-<!-- README.md is generated from README.Rmd. Please edit that file -->
-
-# R/`biotmle`
-
-[](https://github.com/nhejazi/biotmle/actions)
-[](https://codecov.io/github/nhejazi/biotmle?branch=master)
-[](http://www.repostatus.org/#active)
-[](https://bioconductor.org/checkResults/release/bioc-LATEST/biotmle)
-[](https://bioconductor.org/packages/release/bioc/html/biotmle.html)
-[](https://bioconductor.org/packages/release/bioc/html/biotmle.html)
-[](http://opensource.org/licenses/MIT)
-[](https://zenodo.org/badge/latestdoi/65854775)
-[](http://joss.theoj.org/papers/02be843d9bab1b598187bfbb08ce3949)
-
-> Targeted Learning with Moderated Statistics for Biomarker Discovery
-
-**Authors:** [Nima Hejazi](https://nimahejazi.org), [Mark van der
-Laan](https://vanderlaan-lab.org/about), and [Alan
-Hubbard](https://hubbard.berkeley.edu)
-
------
-
-## What’s `biotmle`?
-
-The `biotmle` R package facilitates biomarker discovery through a
-generalization of the moderated t-statistic (Smyth 2004) that extends
-the procedure to locally efficient estimators of asymptotically linear
-target parameters (Tsiatis 2007). The set of methods implemented modify
-targeted maximum likelihood (TML) estimators of statistical (or causal)
-target parameters (e.g., average treatment effect) to apply variance
-moderation to the standard variance estimator based on the efficient
-influence function (EIF) of the target parameter (van der Laan and Rose
-2011, 2018). By performing a moderated hypothesis test that pools the
-individual probe-specific EIF-based variance estimates, a robust
-variance estimator is constructed, which stabilizes the standard error
-estimates and improves the performance of such estimators both in
-smaller samples and in settings where the EIF is poorly estimated. The
-resultant procedure allows for the construction of conservative
-hypothesis tests that reduce the false discovery rate and/or the
-family-wise error rate (Hejazi, van der Laan, and Hubbard 2021).
-Improvements upon prior TML-based approaches to biomarker discovery
-(e.g., Bembom et al. (2009)) include both the moderated variance
-estimator as well as the use of conservative reference distributions for
-the corresponding moderated test statistics (e.g., logistic
-distribution), inspired by tail bounds based on concentration
-inequalities (Rosenblum and van der Laan 2009); the latter prove
-critical for obtaining robust inference when the finite-sample
-distribution of the estimator deviates from normality.
-
------
-
-## Installation
-
-For standard use, install from
-[Bioconductor](https://bioconductor.org/packages/biotmle) using
-[`BiocManager`](https://CRAN.R-project.org/package=BiocManager):
-
-``` r
-if (!requireNamespace("BiocManager", quietly=TRUE)) {
- install.packages("BiocManager")
-}
-BiocManager::install("biotmle")
-```
-
-To contribute, install the bleeding-edge *development version* from
-GitHub via [`remotes`](https://CRAN.R-project.org/package=remotes):
-
-``` r
-remotes::install_github("nhejazi/biotmle")
-```
-
-Current and prior [Bioconductor](https://bioconductor.org) releases are
-available under branches with numbers prefixed by “RELEASE\_”. For
-example, to install the version of this package available via
-Bioconductor 3.6, use
-
-``` r
-remotes::install_github("nhejazi/biotmle", ref = "RELEASE_3_6")
-```
-
------
-
-## Example
-
-For details on how to best use the `biotmle` R package, please consult
-the most recent [package
-vignette](https://bioconductor.org/packages/release/bioc/vignettes/biotmle/inst/doc/exposureBiomarkers.html)
-available through the [Bioconductor
-project](https://bioconductor.org/packages/biotmle).
-
------
-
-## Issues
-
-If you encounter any bugs or have any specific feature requests, please
-[file an issue](https://github.com/nhejazi/biotmle/issues).
-
------
-
-## Contributions
-
-Contributions are very welcome. Interested contributors should consult
-our [contribution
-guidelines](https://github.com/nhejazi/biotmle/blob/master/CONTRIBUTING.md)
-prior to submitting a pull request.
-
------
-
-## Citation
-
-After using the `biotmle` R package, please cite both of the following:
-
-```
- @article{hejazi2017biotmle,
- author = {Hejazi, Nima S and Cai, Weixin and Hubbard, Alan E},
- title = {biotmle: Targeted Learning for Biomarker Discovery},
- journal = {The Journal of Open Source Software},
- volume = {2},
- number = {15},
- month = {July},
- year = {2017},
- publisher = {The Open Journal},
- doi = {10.21105/joss.00295},
- url = {https://doi.org/10.21105/joss.00295}
- }
-
- @article{hejazi2021generalization,
- author = {Hejazi, Nima S and Boileau, Philippe and {van der Laan},
- Mark J and Hubbard, Alan E},
- title = {A generalization of moderated statistics to data adaptive
- semiparametric estimation in high-dimensional biology},
- journal={under review},
- volume={},
- number={},
- pages={},
- year = {2021+},
- publisher={},
- doi = {},
- url = {https://arxiv.org/abs/1710.05451}
- }
-
- @manual{hejazi2019biotmlebioc,
- author = {Hejazi, Nima S and {van der Laan}, Mark J and Hubbard, Alan
- E},
- title = {{biotmle}: {Targeted Learning} with moderated statistics for
- biomarker discovery},
- doi = {10.18129/B9.bioc.biotmle},
- url = {https://bioconductor.org/packages/biotmle},
- note = {R package version 1.10.0}
- }
-```
-
------
-
-## Related
-
- - [R/`biotmleData`](https://github.com/nhejazi/biotmleData) - R
- package with example experimental data for use with this analysis
- package.
-
------
-
-## Funding
-
-The development of this software was supported in part through grants
-from the National Institutes of Health: [P42
-ES004705-29](https://projectreporter.nih.gov/project_info_details.cfm?aid=9260357&map=y)
-and [R01
-ES021369-05](https://projectreporter.nih.gov/project_info_description.cfm?aid=9210551&icde=37849782&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&pball=).
-
------
-
-## License
-
-© 2016-2021 [Nima S. Hejazi](https://nimahejazi.org)
-
-The contents of this repository are distributed under the MIT license.
-See file `LICENSE` for details.
-
------
-
-## References
-
-<div id="refs" class="references">
-
-<div id="ref-bembom2009biomarker">
-
-Bembom, Oliver, Maya L Petersen, Soo-Yon Rhee, W Jeffrey Fessel, Sandra
-E Sinisi, Robert W Shafer, and Mark J van der Laan. 2009. “Biomarker
-Discovery Using Targeted Maximum-Likelihood Estimation: Application to
-the Treatment of Antiretroviral-Resistant Hiv Infection.” *Statistics in
-Medicine* 28 (1): 152–72.
-
-</div>
-
-<div id="ref-hejazi2021generalization">
-
-Hejazi, Nima S, Mark J van der Laan, and Alan E Hubbard. 2021. “A
-Generalization of Moderated Statistics to Data Adaptive Semiparametric
-Estimation in High-Dimensional Biology.” *Under Review*.
-<https://arxiv.org/abs/1710.05451>.
-
-</div>
-
-<div id="ref-rosenblum2009confidence">
-
-Rosenblum, Michael A, and Mark J van der Laan. 2009. “Confidence
-Intervals for the Population Mean Tailored to Small Sample Sizes, with
-Applications to Survey Sampling.” *The International Journal of
-Biostatistics* 5 (1).
-
-</div>
-
-<div id="ref-smyth2004linear">
-
-Smyth, Gordon K. 2004. “Linear Models and Empirical Bayes Methods for
-Assessing Differential Expression in Microarray Experiments.”
-*Statistical Applications in Genetics and Molecular Biology* 3 (1):
-1–25. <https://doi.org/10.2202/1544-6115.1027>.
-
-</div>
-
-<div id="ref-tsiatis2007semiparametric">
-
-Tsiatis, Anastasios. 2007. *Semiparametric Theory and Missing Data*.
-Springer Science & Business Media.
-
-</div>
-
-<div id="ref-vdl2011targeted">
-
-van der Laan, Mark J., and Sherri Rose. 2011. *Targeted Learning: Causal
-Inference for Observational and Experimental Data*. Springer Science &
-Business Media.
-
-</div>
-
-<div id="ref-vdl2018targeted">
-
-van der Laan, Mark J, and Sherri Rose. 2018. *Targeted Learning in Data
-Science: Causal Inference for Complex Longitudinal Studies*. Springer
-Science & Business Media.
-
-</div>
-
-</div>
+
+
+# R/`biotmle`
+
+[](https://github.com/nhejazi/biotmle/actions)
+[](https://codecov.io/github/nhejazi/biotmle?branch=master)
+[](http://www.repostatus.org/#active)
+[](https://bioconductor.org/checkResults/release/bioc-LATEST/biotmle)
+[](https://bioconductor.org/packages/release/bioc/html/biotmle.html)
+[](https://bioconductor.org/packages/release/bioc/html/biotmle.html)
+[](http://opensource.org/licenses/MIT)
+[](https://zenodo.org/badge/latestdoi/65854775)
+[](http://joss.theoj.org/papers/02be843d9bab1b598187bfbb08ce3949)
+
+ Targeted Learning with Moderated Statistics for Biomarker Discovery
+
+__Authors:__ [Nima Hejazi](https://nimahejazi.org), [Mark van der
+Laan](https://vanderlaan-lab.org/about), and [Alan
+Hubbard](https://hubbard.berkeley.edu)
+
+---
+
+## What's `biotmle`?
+
+The `biotmle` R package facilitates biomarker discovery through a generalization
+of the moderated t-statistic [@smyth2004linear] that extends the procedure to
+locally efficient estimators of asymptotically linear target parameters
+[@tsiatis2007semiparametric]. The set of methods implemented modify targeted
+maximum likelihood (TML) estimators of statistical (or causal) target parameters
+(e.g., average treatment effect) to apply variance moderation to the standard
+variance estimator based on the efficient influence function (EIF) of the target
+parameter [@vdl2011targeted; @vdl2018targeted]. By performing a moderated
+hypothesis test that pools the individual probe-specific EIF-based variance
+estimates, a robust variance estimator is constructed, which stabilizes the
+standard error estimates and improves the performance of such estimators both in
+smaller samples and in settings where the EIF is poorly estimated. The resultant
+procedure allows for the construction of conservative hypothesis tests that
+reduce the false discovery rate and/or the family-wise error rate
+[@hejazi2021generalization]. Improvements upon prior TML-based approaches to
+biomarker discovery (e.g., @bembom2009biomarker) include both the moderated
+variance estimator as well as the use of conservative reference distributions
+for the corresponding moderated test statistics (e.g., logistic distribution),
+inspired by tail bounds based on concentration
+inequalities [@rosenblum2009confidence]; the latter prove critical for obtaining
+robust inference when the finite-sample distribution of the estimator deviates
+from normality.
+
+---
+
+## Installation
+
+For standard use, install from
+[Bioconductor](https://bioconductor.org/packages/biotmle) using
+[`BiocManager`](https://CRAN.R-project.org/package=BiocManager):
+
+```{r bioc-installation, eval = FALSE}
+if (!requireNamespace("BiocManager", quietly=TRUE)) {
+ install.packages("BiocManager")
+}
+BiocManager::install("biotmle")
+```
+
+To contribute, install the bleeding-edge _development version_ from GitHub via
+[`remotes`](https://CRAN.R-project.org/package=remotes):
+
+```{r gh-master-installation, eval = FALSE}
+remotes::install_github("nhejazi/biotmle")
+```
+
+Current and prior [Bioconductor](https://bioconductor.org) releases are
+available under branches with numbers prefixed by "RELEASE_". For example, to
+install the version of this package available via Bioconductor 3.6, use
+
+```{r gh-develop-installation, eval = FALSE}
+remotes::install_github("nhejazi/biotmle", ref = "RELEASE_3_6")
+```
+
+---
+
+## Example
+
+For details on how to best use the `biotmle` R package, please consult the most
+recent [package
+vignette](https://bioconductor.org/packages/release/bioc/vignettes/biotmle/inst/doc/exposureBiomarkers.html)
+available through the [Bioconductor
+project](https://bioconductor.org/packages/biotmle).
+
+---
+
+## Issues
+
+If you encounter any bugs or have any specific feature requests, please [file an
+issue](https://github.com/nhejazi/biotmle/issues).
+
+---
+
+## Contributions
+
+Contributions are very welcome. Interested contributors should consult our
+[contribution
+guidelines](https://github.com/nhejazi/biotmle/blob/master/CONTRIBUTING.md)
+prior to submitting a pull request.
+
+---
+
+## Citation
+
+After using the `biotmle` R package, please cite both of the following:
+
+ @article{hejazi2017biotmle,
+ author = {Hejazi, Nima S and Cai, Weixin and Hubbard, Alan E},
+ title = {biotmle: Targeted Learning for Biomarker Discovery},
+ journal = {The Journal of Open Source Software},
+ volume = {2},
+ number = {15},
+ month = {July},
+ year = {2017},
+ publisher = {The Open Journal},
+ doi = {10.21105/joss.00295},
+ url = {https://doi.org/10.21105/joss.00295}
+ }
+
+ @article{hejazi2021generalization,
+ author = {Hejazi, Nima S and Boileau, Philippe and {van der Laan},
+ Mark J and Hubbard, Alan E},
+ title = {A generalization of moderated statistics to data adaptive
+ semiparametric estimation in high-dimensional biology},
+ journal={under review},
+ volume={},
+ number={},
+ pages={},
+ year = {2021+},
+ publisher={},
+ doi = {},
+ url = {https://arxiv.org/abs/1710.05451}
+ }
+
+ @manual{hejazi2019biotmlebioc,
+ author = {Hejazi, Nima S and {van der Laan}, Mark J and Hubbard, Alan
+ E},
+ title = {{biotmle}: {Targeted Learning} with moderated statistics for
+ biomarker discovery},
+ doi = {10.18129/B9.bioc.biotmle},
+ url = {https://bioconductor.org/packages/biotmle},
+ note = {R package version 1.10.0}
+ }
+
+---
+
+## Related
+
+* [R/`biotmleData`](https://github.com/nhejazi/biotmleData) - R package with
+ example experimental data for use with this analysis package.
+
+---
+
+## Funding
+
+The development of this software was supported in part through grants from the
+National Institutes of Health: [P42 ES004705-29](https://projectreporter.nih.gov/project_info_details.cfm?aid=9260357&map=y) and [R01 ES021369-05](https://projectreporter.nih.gov/project_info_description.cfm?aid=9210551&icde=37849782&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&pball=).
+
+---
+
+## License
+
+© 2016-2021 [Nima S. Hejazi](https://nimahejazi.org)
+
+The contents of this repository are distributed under the MIT license. See file
+`LICENSE` for details.
+
+---
Datasets
Models
