robsurvey: robust survey statistics estimation

Abstract

The robsurvey package provides robust estimation methods for data from complex sample surveys. The package implements the following methods: (1) basic outlier-robust location estimators of the population mean and total using weight reduction, trimming, winsorization, and M-estimation (robust Horvitz-Thompson and Hajek estimators); (2) robust survey regression M- and GM-estimators of the type Mallows and Schweppe; (3) robust model-assisted estimators of the population mean and total.

A key design pattern of the package is that the methods are available in two flavors: bare-bone functions and survey methods. Bare-bone functions are stripped-down versions of the survey methods in terms of functionality. They may serve package developers as building blocks. The survey methods are much more capable and depend–for variance estimation–on the R package survey. The talk is organized into three parts: (1) Overview of the robust methods in robsurvey, including a comparison with other R packages (survey, robustbase, robeth, and MASS), Stata (robstat and rreg), SAS (robustreg), NAG and GNU Scientific Library. (2) Design patterns and possible extensions of the package. (3) Use cases and applications of the package.