Package: balnet
Title: Pathwise Estimation of Covariate Balancing Propensity Scores
Version: 0.0.3
Authors@R: c(
    person("Erik", "Sverdrup", role = c("aut", "cre"), email = "erik.sverdrup@monash.edu"),
    person("Trevor", "Hastie", role = "aut"),
    person("James", "Yang", role = "ctb", comment = "Author of the bundled adelie C++ library.")
    )
Description: 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>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
LinkingTo: Rcpp, RcppEigen
SystemRequirements: C++17
Imports: Rcpp, Matrix, methods
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
URL: https://github.com/erikcs/balnet
BugReports: https://github.com/erikcs/balnet/issues
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-05-26 00:35:42 UTC; erikcs
Author: Erik Sverdrup [aut, cre],
  Trevor Hastie [aut],
  James Yang [ctb] (Author of the bundled adelie C++ library.)
Maintainer: Erik Sverdrup <erik.sverdrup@monash.edu>
Repository: CRAN
Date/Publication: 2026-05-26 03:30:09 UTC
Built: R 4.7.0; x86_64-w64-mingw32; 2026-06-17 02:50:52 UTC; windows
Archs: x64
