robust2sls: Outlier Robust Two-Stage Least Squares Inference and Testing

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) <>.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: AER, doRNG, foreach, pracma, stats
Suggests: datasets, doFuture, doParallel, future, ggplot2, grDevices, knitr, MASS, parallel, rmarkdown, testthat, utils
Published: 2021-11-23
Author: Jonas Kurle [aut, cre] (0000-0003-2197-2012)
Maintainer: Jonas Kurle <mail at>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: robust2sls results


Reference manual: robust2sls.pdf
Vignettes: Monte Carlo Simulations
Introduction to the robust2sls Package


Package source: robust2sls_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): robust2sls_0.1.0.tgz, r-release (x86_64): robust2sls_0.1.0.tgz, r-oldrel: robust2sls_0.1.0.tgz


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