Model-Assisted Survey Estimators R package mase version 0.1.5.2 A set of model-assisted survey estimators and corresponding variance estimators for single stage, unequal probability, without replacement sampling designs. All of the estimators can be written as a generalized regression estimator with the Horvitz-Thompson, ratio, post-stratified, and regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6). Two of the estimators employ a statistical learning model as the assisting model: the elastic net regression estimator, which is an extension of the lasso regression estimator given by McConville et al. (2017) <doi:10.1093/jssam/smw041>, and the regression tree estimator described in McConville and Toth (2017) <arXiv:1712.05708>. The variance estimators which approximate the joint inclusion probabilities can be found in Berger and Tille (2009) <doi:10.1016/S0169-7161(08)00002-3> and the bootstrap variance estimator is presented in Mashreghi et al. (2016) <doi:10.1214/16-SS113>. Software Depends: R (>= 4.1.0) Imports: glmnet, survey, dplyr, tidyr, rpms, boot, stats, Rdpack, ellipsis, Rcpp LinkingTo: Rcpp, RcppEigen Suggests: roxygen2, testthat (>= 3.0.0), knitr, rmarkdown Kelly McConville <kmcconville@fas.harvard.edu> Comprehensive R Archive Network (CRAN) Kelly McConville [cre, aut, cph], Josh Yamamoto [aut], Becky Tang [aut], George Zhu [aut], Sida Li [ctb], Shirley Chueng [ctb], Daniell Toth [ctb] GPL-2 2024-01-17 application/tgz https://CRAN.R-project.org/package=mase doi:10.32614/CRAN.package.mase