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: | 0.1.2 |

Depends: | R (≥ 3.1) |

Imports: | glmnet, Matrix, foreach, survey, dplyr, magrittr, rpms, boot, stats, Rdpack |

Suggests: | roxygen2, testthat |

Published: | 2018-10-12 |

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 |

Reference manual: | mase.pdf |

Package source: | mase_0.1.2.tar.gz |

Windows binaries: | r-devel: mase_0.1.2.zip, r-release: mase_0.1.2.zip, r-oldrel: mase_0.1.2.zip |

macOS binaries: | r-release: mase_0.1.2.tgz, r-oldrel: mase_0.1.2.tgz |

Old sources: | mase archive |

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