mase

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) doi:10.48550/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.

Last 30 days

This package has been downloaded 316 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.

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Last 365 days

This package has been downloaded 3,989 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Dec 03, 2024 with 52 downloads.

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