Semi-Supervised Estimation of Average Treatment Effects R package SMMAL version 0.0.5 Provides a pipeline for estimating the average treatment effect via semi-supervised learning. Outcome regression is fit with cross-fitting using various machine learning method or user customized function. Doubly robust ATE estimation leverages both labeled and unlabeled data under a semi-supervised missing-data framework. For more details see Hou et al. (2021) <doi:10.48550/arxiv.2110.12336>. A detailed vignette is included. Software Depends: R (>= 3.5.0) Imports: glmnet,randomForest,splines2,xgboost,stats,utils Suggests: knitr,rmarkdown,testthat (>= 3.0.0) Jue Hou <hou00123@umn.edu> Comprehensive R Archive Network (CRAN) Jue Hou [aut, cre], Yuming Zhang [aut], Shuheng Kong [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=SMMAL/LICENSE) 2025-08-28 application/tgz https://CRAN.R-project.org/package=SMMAL doi:10.32614/CRAN.package.SMMAL