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 reed.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Citation: |
mase citation info |
| Materials: |
README |
| CRAN checks: |
mase results |
Documentation:
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Reverse dependencies:
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