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mase  

Model-Assisted Survey Estimators
View on CRAN: Click here


Download and install mase package within the R console
Install from CRAN:
install.packages("mase")

Install from Github:
library("remotes")
install_github("cran/mase")

Install by package version:
library("remotes")
install_version("mase", "0.1.5.2")



Attach the package and use:
library("mase")
Maintained by
Kelly McConville
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-06-14
Latest Update: 2023-08-30
Description:
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) , and the regression tree estimator described in McConville and Toth (2017) . The variance estimators which approximate the joint inclusion probabilities can be found in Berger and Tille (2009) and the bootstrap variance estimator is presented in Mashreghi et al. (2016) .
How to cite:
Kelly McConville (2018). mase: Model-Assisted Survey Estimators. R package version 0.1.5.2, https://cran.r-project.org/web/packages/mase
Previous versions and publish date:
0.1.1 (2018-06-14 20:33), 0.1.2 (2018-10-13 01:00), 0.1.3 (2021-07-10 01:00), 0.1.4 (2023-08-30 18:50), 0.1.5.1 (2023-11-27 22:50), 0.1.5 (2023-11-16 18:30)
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