A set of modelassisted 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 HorvitzThompson, ratio, poststratified, and regression estimators summarized by Sarndal et al. (1992, ISBN:9780387406206). 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/S01697161(08)000023> and the bootstrap variance estimator is presented in Mashreghi et al. (2016) <doi:10.1214/16SS113>.
Package details 


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 <[email protected]> 
License  GPL2 
Version  0.1.2 
Package repository  View on CRAN 
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