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ptLasso: Pretraining the Lasso

Implements lasso pretraining for generalized linear models for data with grouped observations; data without grouped observations, but with multinomial responses; data with multiple Gaussian responses; and data with repeated measurements over time. The method fits a lasso model to an initial dataset and then uses its support and coefficient values to guide model fitting for a related task, which can improve performance by leveraging larger datasets and preserve interpretability by identifying features which are generally shared, and which are task specific. See Craig et al. (2026) <doi:10.1093/jrsssb/qkaf050> for details. Model fitting in this package is done with cv.glmnet() from the 'glmnet' package, and syntax closely follows that of 'glmnet' (Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01>).

Version: 1.0.2
Depends: ggplot2, glmnet, gridExtra, Matrix
Suggests: knitr, rmarkdown, rpart, survival, xgboost, sparsepca, MASS, testthat
Published: 2026-10-27
DOI: 10.32614/CRAN.package.ptLasso (may not be active yet)
Author: Erin Craig ORCID iD [aut, cre, cph], Rob Tibshirani [aut, cph]
Maintainer: Erin Craig <ercr at umich.edu>
BugReports: https://github.com/erincr/ptLasso/issues
License: GPL-2
URL: https://erincr.github.io/ptLasso/, https://doi.org/10.1093/jrsssb/qkaf050
NeedsCompilation: yes
Language: en
Citation: ptLasso citation info
Materials: NEWS
CRAN checks: ptLasso results

Documentation:

Reference manual: ptLasso.html , ptLasso.pdf
Vignettes: Input grouped data (source, R code)
Multi-response data with Gaussian responses (source, R code)
Target grouped or multinomial response data (source, R code)
Time series data (source, R code)
ptLasso (source, R code)

Downloads:

Package source: ptLasso_1.0.2.tar.gz
Windows binaries: r-devel: not available, r-release: ptLasso_1.0.2.zip, r-oldrel: not available
macOS binaries: r-release (arm64): ptLasso_1.0.2.tgz, r-oldrel (arm64): ptLasso_1.0.2.tgz, r-release (x86_64): ptLasso_1.0.2.tgz, r-oldrel (x86_64): ptLasso_1.0.2.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.