mase: Model-Assisted Survey Estimators

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) <doi:10.48550/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>.

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
Published: 2024-01-17
DOI: 10.32614/CRAN.package.mase
Author: Kelly McConville [cre, aut, cph], Josh Yamamoto [aut], Becky Tang [aut], George Zhu [aut], Sida Li [ctb], Shirley Chueng [ctb], Daniell Toth [ctb]
Maintainer: Kelly McConville <kmcconville at>
License: GPL-2
NeedsCompilation: yes
Citation: mase citation info
Materials: README
CRAN checks: mase results


Reference manual: mase.pdf


Package source: mase_0.1.5.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mase_0.1.5.2.tgz, r-oldrel (arm64): mase_0.1.5.2.tgz, r-release (x86_64): mase_0.1.5.2.tgz, r-oldrel (x86_64): mase_0.1.5.2.tgz
Old sources: mase archive

Reverse dependencies:

Reverse imports: FIESTAutils


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