hal9001

hal9001: Scalable highly adaptive lasso regression in R - Published in JOSS (2020)

Science Score: 95.0%

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Found 13 DOI reference(s) in README and JOSS metadata
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Links to: joss.theoj.org, zenodo.org
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3 of 10 committers (30.0%) from academic institutions
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JOSS paper metadata
Published in Journal of Open Source Software

Keywords

cross-validation lasso-regression machine-learning-algorithms nonparametric-regression

Keywords from Contributors

causal-inference conditional-density-estimates density-estimation highly-adaptive-lasso inverse-probability-weights propensity-score

Last synced: 4 months ago · JSON representation

Repository

🤠 📿 The Highly Adaptive Lasso

Basic Info

Host: GitHub
Owner: tlverse
License: gpl-3.0
Language: R
Default Branch: master
Homepage: https://tlverse.org/hal9001
Size: 13.1 MB

Statistics

Stars: 49
Watchers: 4
Forks: 14
Open Issues: 7
Releases: 5

Topics

cross-validation lasso-regression machine-learning-algorithms nonparametric-regression

Created over 8 years ago · Last pushed about 1 year ago

Metadata Files

Readme Changelog Contributing License

README.Rmd

---
output:
  rmarkdown::github_document
bibliography: "inst/REFERENCES.bib"
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# R/`hal9001`

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> The _Scalable_ Highly Adaptive Lasso

__Authors:__ [Jeremy Coyle](https://github.com/tlverse), [Nima
Hejazi](https://nimahejazi.org), [Rachael
Phillips](https://github.com/rachaelvp), [Lars van der
Laan](https://github.com/Larsvanderlaan), and [Mark van der
Laan](https://vanderlaan-lab.org/)

---

## What's `hal9001`?

`hal9001` is an R package providing an implementation of the scalable _highly
adaptive lasso_ (HAL), a nonparametric regression estimator that applies
L1-regularized lasso regression to a design matrix composed of indicator
functions corresponding to the support of the functional over a set of
covariates and interactions thereof. HAL regression allows for arbitrarily
complex functional forms to be estimated at fast (near-parametric) convergence
rates under only global smoothness assumptions [@vdl2017generally;
@bibaut2019fast]. For detailed theoretical discussions of the highly adaptive
lasso estimator, consider consulting, for example, @vdl2017generally,
@vdl2017finite, and @vdl2017uniform. For a computational demonstration of the
versatility of HAL regression, see @benkeser2016hal. Recent theoretical works
have demonstrated success in building efficient estimators of complex
parameters when particular variations of HAL regression are used to estimate
nuisance parameters [e.g., @vdl2019efficient; @ertefaie2020nonparametric].

---

## Installation

For standard use, we recommend installing the package from
[CRAN](https://CRAN.R-project.org/package=hal9001) via

```{r cran-installation, eval = FALSE}
install.packages("hal9001")
```

To contribute, install the _development version_ of `hal9001` from GitHub via
[`remotes`](https://CRAN.R-project.org/package=remotes):

```{r gh-master-installation, eval = FALSE}
remotes::install_github("tlverse/hal9001")
```

---

## Issues

If you encounter any bugs or have any specific feature requests, please [file an
issue](https://github.com/tlverse/hal9001/issues).

---

## Example

Consider the following minimal example in using `hal9001` to generate
predictions via Highly Adaptive Lasso regression:

```{r example}
# load the package and set a seed
library(hal9001)
set.seed(385971)

# simulate data
n <- 100
p <- 3
x <- matrix(rnorm(n * p), n, p)
y <- x[, 1] * sin(x[, 2]) + rnorm(n, mean = 0, sd = 0.2)

# fit the HAL regression
hal_fit <- fit_hal(X = x, Y = y, yolo = TRUE)
hal_fit$times

# training sample prediction
preds <- predict(hal_fit, new_data = x)
mean(hal_mse <- (preds - y)^2)
```

---

## Contributions

Contributions are very welcome. Interested contributors should consult our
[contribution
guidelines](https://github.com/tlverse/hal9001/blob/master/CONTRIBUTING.md)
prior to submitting a pull request.

---

## Citation

After using the `hal9001` R package, please cite both of the following:

        @software{coyle2022hal9001-rpkg,
          author = {Coyle, Jeremy R and Hejazi, Nima S and Phillips, Rachael V
            and {van der Laan}, Lars and {van der Laan}, Mark J},
          title = {{hal9001}: The scalable highly adaptive lasso},
          year  = {2022},
          url = {https://doi.org/10.5281/zenodo.3558313},
          doi = {10.5281/zenodo.3558313}
          note = {{R} package version 0.4.2}
        }

        @article{hejazi2020hal9001-joss,
          author = {Hejazi, Nima S and Coyle, Jeremy R and {van der Laan}, Mark
            J},
          title = {{hal9001}: Scalable highly adaptive lasso regression in
            {R}},
          year  = {2020},
          url = {https://doi.org/10.21105/joss.02526},
          doi = {10.21105/joss.02526},
          journal = {Journal of Open Source Software},
          publisher = {The Open Journal}
        }

---

## License

© 2017-2024 [Jeremy Coyle](https://github.com/tlverse) and [Nima
Hejazi](https://nimahejazi.org)

The contents of this repository are distributed under the GPL-3 license. See
file `LICENSE` for details.

---

## References

Owner

Name: tlverse
Login: tlverse
Kind: organization
Location: Berkeley, CA, USA

Website: https://tlverse.org
Twitter: tlverse
Repositories: 37
Profile: https://github.com/tlverse

An extensible ecosystem of R packages for targeted causal machine learning

JOSS Publication

hal9001: Scalable highly adaptive lasso regression in R

Published

September 26, 2020

DOI

10.21105/joss.02526

Volume 5, Issue 53, Page 2526

Authors

Nima S. Hejazi

Graduate Group in Biostatistics, University of California, Berkeley, Division of Biostatistics, School of Public Health, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley

Jeremy R. Coyle

Division of Biostatistics, School of Public Health, University of California, Berkeley

Mark J. van der Laan

Division of Biostatistics, School of Public Health, University of California, Berkeley, Department of Statistics, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley

Editor

Mikkel Meyer Andersen

GitHub Events

Total

Issues event: 1
Watch event: 1
Push event: 2
Pull request event: 3

Last Year

Issues event: 1
Watch event: 1
Push event: 2
Pull request event: 3

Committers

Last synced: 5 months ago

All Time

Total Commits: 444
Total Committers: 10
Avg Commits per committer: 44.4
Development Distribution Score (DDS): 0.534

Past Year

Commits: 1
Committers: 1
Avg Commits per committer: 1.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Nima Hejazi	nh@n****g	207
rachaelvp	r**s@g**m	96
Jeremy Coyle	j**e@g**m	54
Lars van der Laan	V**s@y**m	47
Lars van der Laan	l**n@b**u	23
Wilson Cai	w**i@b**u	8
YuZhang2019	y**2@o**m	3
David McCoy	5****s	3
Benkeser	b**r@e**u	2
Katrin Leinweber	k**i@p**e	1

Committer Domains (Top 20 + Academic)

berkeley.edu: 2 posteo.de: 1 emory.edu: 1 nimahejazi.org: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 41
Total pull requests: 70
Average time to close issues: 10 months
Average time to close pull requests: 28 days
Total issue authors: 21
Total pull request authors: 11
Average comments per issue: 2.05
Average comments per pull request: 1.37
Merged pull requests: 62
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 1
Pull requests: 1
Average time to close issues: 4 months
Average time to close pull requests: 20 minutes
Issue authors: 1
Pull request authors: 1
Average comments per issue: 0.0
Average comments per pull request: 0.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

nhejazi (15)
jlstiles (3)
benkeser (3)
rrrlw (2)
daviddewhurst (2)
rachaelvp (1)
tcovert (1)
SeraphinaShi (1)
Larsvanderlaan (1)
jaganmn (1)
HughSt (1)
matthewvowels1 (1)
Lauren-EylerDang (1)
prockenschaub (1)
jeremyrcoyle (1)

Pull Request Authors

nhejazi (32)
Larsvanderlaan (13)
jeremyrcoyle (10)
rachaelvp (6)
wilsoncai1992 (4)
YuZhang2019 (2)
tq21 (2)
katrinleinweber (1)
WenxinZhang25 (1)
blind-contours (1)
benkeser (1)

Top Labels

Issue Labels

enhancement (12) bug (9) question (6) help wanted (1)

Pull Request Labels

enhancement (15) bug (4)

Packages

Total packages: 1
Total downloads:
- cran 1,496 last-month

Total dependent packages: 3
Total dependent repositories: 26
Total versions: 7
Total maintainers: 1

cran.r-project.org: hal9001

The Scalable Highly Adaptive Lasso

Homepage: https://github.com/tlverse/hal9001
Documentation: http://cran.r-project.org/web/packages/hal9001/hal9001.pdf
License: GPL-3
Latest release: 0.4.6
published about 2 years ago

Versions: 7
Dependent Packages: 3
Dependent Repositories: 26
Downloads: 1,496 Last month

Rankings

Forks count: 4.8%

Dependent repos count: 5.4%

Stargazers count: 7.0%

Average: 7.5%

Downloads: 9.6%

Dependent packages count: 10.9%

Maintainers (1)

jeremyrcoyle@gmail.com

Last synced: 4 months ago

Dependencies

DESCRIPTION cran

R >= 3.1.0 depends
Rcpp * depends
Matrix * imports
assertthat * imports
data.table * imports
glmnet * imports
methods * imports
origami >= 1.0.3 imports
stats * imports
stringr * imports
utils * imports
SuperLearner * suggests
dplyr * suggests
future * suggests
ggplot2 * suggests
knitr * suggests
microbenchmark * suggests
rmarkdown * suggests
survival * suggests
testthat * suggests
tidyr * suggests

.github/workflows/R-CMD-check.yml actions

actions/checkout v2 composite
r-lib/actions/setup-pandoc v1 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-tinytex v1 composite

hal9001

Science Score: 95.0%

Keywords

Keywords from Contributors

Repository

Basic Info

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Topics

Metadata Files

README.Rmd

Owner

JOSS Publication

hal9001: Scalable highly adaptive lasso regression in R

Authors

Editor

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