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: 0.1.1
Depends: R (≥ 3.1)
Imports: MASS, glmnet, Matrix, foreach, survey, dplyr, magrittr, rpms, boot, stats, Rdpack
Suggests: roxygen2, testthat
Published: 2018-06-14
Author: Kelly McConville [aut, cre, cph], Becky Tang [aut], George Zhu [aut], Sida Li [ctb], Shirley Chueng [ctb], Daniell Toth [ctb, cph] (Author and copyright holder of treeDesignMatrix helper function)
Maintainer: Kelly McConville <mcconville at reed.edu>
License: GPL-2
NeedsCompilation: no
Citation: mase citation info
Materials: README
CRAN checks: mase results

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Reference manual: mase.pdf
Package source: mase_0.1.1.tar.gz
Windows binaries: r-devel: mase_0.1.1.zip, r-release: mase_0.1.1.zip, r-oldrel: mase_0.1.1.zip
OS X binaries: r-release: mase_0.1.1.tgz, r-oldrel: mase_0.1.1.tgz

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