An implementation of easy tools for outlier robust inference in two-stage least squares (2SLS) models. The user specifies a reference distribution against which observations are classified as outliers or not. After removing the outliers, adjusted standard errors are automatically provided. Furthermore, several statistical tests for the false outlier detection rate can be calculated. The outlier removing algorithm can be iterated a fixed number of times or until the procedure converges. The algorithms and robust inference are described in more detail in Jiao (2019) <https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.
Version: 0.2.3 Depends: R (≥ 2.10) Imports: exactci, foreach, ivreg, MASS, mathjaxr, pracma, stats Suggests: covr, datasets, doFuture, doParallel, doRNG, future, ggplot2, grDevices, ivgets, knitr, parallel, rmarkdown, testthat, utils Published: 2025-05-20 DOI: 10.32614/CRAN.package.robust2sls Author: Jonas Kurle [aut, cre] Maintainer: Jonas Kurle <mail at jonaskurle.com> BugReports: https://github.com/jkurle/robust2sls/issues License: GPL-3 URL: https://github.com/jkurle/robust2sls NeedsCompilation: no Materials: README NEWS CRAN checks: robust2sls results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=robust2sls to link to this page.
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