In jeremyrcoyle/mangolassi: The Scalable Highly Adaptive Lasso

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R/`hal9001`

The Scalable Highly Adaptive Lasso

Authors: Jeremy Coyle, Nima Hejazi, Rachael Phillips, Lars van der Laan, and Mark van der Laan

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 via

install.packages("hal9001")

To contribute, install the development version of hal9001 from GitHub via remotes:

remotes::install_github("tlverse/hal9001")

Issues

If you encounter any bugs or have any specific feature requests, please file an issue.

Example

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

# 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 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}
    }