hal9001
The facts below are what the maker publishes about hal9001, checked and dated. Our editors have not written about it yet.
The facts
Each one read from hal9001's own pages, with the date it was checked.
What it does
- Available builds
- CRAN lists Windows binaries, macOS binaries for arm64 and x86_64, and a source package.checked 12 Oct 2026
- Basis construction
- The package includes routines for constructing sparse matrices of basis functions from observed data.checked 12 Oct 2026
- Contributions
- The project welcomes contributions and directs prospective contributors to its contribution guidelines.checked 11 Oct 2026
- Data import formats
- R matrix, numeric vector, or formula interfacechecked 24 Sep 2026
- Dependencies
- The package depends on R version 3.1.0 or later and Rcpp, and imports Matrix, stats, utils, methods, assertthat, origami, glmnet, data.table, and stringr.checked 12 Oct 2026
- Deployment
- desktopchecked 24 Sep 2026
- Development install
- The development version can be installed from the tlverse/hal9001 GitHub repository using remotes::install_github().checked 11 Oct 2026
- Development version
- The development version can be installed from the tlverse/hal9001 GitHub repository using remotes::install_github().checked 12 Oct 2026
- Documentation
- CRAN lists a reference manual and a vignette on fitting the highly adaptive lasso with hal9001.checked 12 Oct 2026
- Estimation
- HAL regression can estimate arbitrarily complex functional forms under global smoothness assumptions.checked 11 Oct 2026
- Installation
- The maker recommends installing the package from CRAN with install.packages("hal9001").checked 12 Oct 2026
- Integrations
- CRAN lists glmnet among the package imports and SuperLearner among its suggested packages.checked 11 Oct 2026
- Intended use
- The maker describes HAL regression as a way to estimate complex functional forms under global smoothness assumptions, with applications to nuisance parameter estimation.checked 12 Oct 2026
- Lasso fitting
- Its lasso routines use glmnet by default and include a custom implementation tailored to predictors composed of indicator functions.checked 12 Oct 2026
- License
- The package is distributed under the GPL-3 license.checked 12 Oct 2026
- Maintainer
- Jeremy Coyle is listed as the package maintainer on CRAN.checked 11 Oct 2026
- Method
- It applies L1-regularized lasso regression to indicator functions for covariate supports and their interactions.checked 11 Oct 2026
- Model basis
- It applies L1-regularized lasso regression to indicator-function basis terms for covariates and their interactions.checked 12 Oct 2026
- Purpose
- hal9001 is an R package implementing the scalable highly adaptive lasso, a nonparametric regression estimator.checked 11 Oct 2026
- R dependency
- The package depends on R version 3.1.0 or later and Rcpp.checked 11 Oct 2026
- Regression models
- Yeschecked 24 Sep 2026
- Scripting support
- Yeschecked 24 Sep 2026
Money
- Free plan
- Yeschecked 24 Sep 2026
Where it runs
- Platforms
- CRAN lists Windows and macOS binary packages and a source package; the package depends on R.checked 11 Oct 2026
