Package: mase 0.1.5.900

Kelly McConville

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>.

Authors:Kelly McConville [cre, aut, cph], Josh Yamamoto [aut], Becky Tang [aut], George Zhu [aut], Grayson White [aut], Sida Li [ctb], Shirley Chueng [ctb], Daniell Toth [ctb]

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Peer review:

Bug tracker:https://github.com/mcconvil/mase/issues

On CRAN:

8 exports 8 stars 2.74 score 41 dependencies 2 dependents 41 scripts 281 downloads

Last updated 1 months agofrom:43ecb83a72. Checks:OK: 9. Indexed: yes.

Exports:greggregElasticNetgregTreehorvitzThompsonmodifiedGregpostStratratioratioEstimator

Dependencies:bootclicodetoolscpp11DBIdplyrfansiforeachgenericsglmnetglueiteratorslatticelifecyclemagrittrMatrixminqamitoolsnumDerivpillarpkgconfigpurrrR6rbibutilsRcppRcppArmadilloRcppEigenRdpackrlangrpmsshapestringistringrsurveysurvivaltibbletidyrtidyselectutf8vctrswithr