Balancing Confounder Distributions with Forest Energy Balancing R package forestBalance version 0.1.0 Estimates average treatment effects using kernel energy balancing with random forest similarity kernels. A multivariate random forest jointly models covariates, outcome, and treatment to build a similarity kernel between observations. This kernel is then used for energy balancing to create weights that control for confounding. The method is described in De and Huling (2025) <doi:10.48550/arXiv.2512.18069>. Software Imports: grf (>= 2.3.0), MASS, Matrix, methods, Rcpp LinkingTo: Rcpp, RcppEigen Suggests: ggplot2, knitr, osqp, rmarkdown, testthat (>= 3.0.0), WeightIt Jared Huling <jaredhuling@gmail.com> Comprehensive R Archive Network (CRAN) Jared Huling [aut, cre], Simion De [aut] GPL (>= 3) 2026-04-07 application/tgz https://CRAN.R-project.org/package=forestBalance doi:10.32614/CRAN.package.forestBalance