Pathwise Estimation of Covariate Balancing Propensity Scores
Overview
Provides pathwise estimation of regularized logistic propensity score models using covariate balancing loss functions rather than maximum likelihood. Regularization paths are fit via the 'adelie' elastic-net solver with a 'glmnet'-like interface, yielding balancing weights that target covariate balance for the ATE and ATT. Under lasso penalization, lambda bounds the maximum covariate imbalance, so the regularization path traces a sequence of decreasing imbalance tolerances. For details, see Sverdrup & Hastie (2026) doi:10.48550/arXiv.2602.18577.
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- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK12 OK2026-04-25
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- NOTE12 OK, 1 NOTE2026-04-22
- ERROR11 OK, 1 NOTE, 1 ERROR2026-04-18
- NOTE9 OK, 1 NOTE2026-04-08
- OK4 OK2026-04-04
Documentation
- Help pages
- 18 (5 internal)
- Examples that run
- 0%11 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 11%
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- 94%
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- 12
- Median days to close, 50 most recently updated closed PRs
- 0
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Package metadata
- First published
- 2026-04-03
- Total releases
- 4 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 299 KB
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