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) <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>.

Version: 0.1.3
Depends: R (≥ 3.1)
Imports: glmnet, survey, dplyr, magrittr, rpms, boot, stats, Rdpack
Suggests: roxygen2, testthat, knitr, rmarkdown
Published: 2021-07-09
Author: Kelly McConville [aut, cre, cph], Becky Tang [aut], George Zhu [aut], Sida Li [ctb], Shirley Chueng [ctb], Daniell Toth [ctb]
Maintainer: Kelly McConville <mcconville at>
License: GPL-2
NeedsCompilation: no
Citation: mase citation info
Materials: README
CRAN checks: mase results


Reference manual: mase.pdf


Package source: mase_0.1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mase_0.1.3.tgz, r-release (x86_64): mase_0.1.3.tgz, r-oldrel: mase_0.1.3.tgz
Old sources: mase archive


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