A collection of randomization tests, data sets and examples. The current version focuses on three testing problems and their implementation in empirical work. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (2017) <https://goo.gl/UZFqt7>. More specifically, it allows the user to select a set of covariates and test the aforementioned hypothesis using a permutation test based on the Cramervon Miss test statistic. Graphical inspection of the empirical CDF and histograms for the variables of interest is also supported in the package. Third, it provides the practitioner with an effortless implementation of a permutation test based on the martingale decomposition of the empirical process for the goodnessoffit testing problem with an estimated nuisance parameter. An application of this testing problem is the one of testing for heterogeneous treatment effects in a randomized control trial.
Package details 


Author  Mauricio OlivaresGonzalez [aut, cre], Ignacio SarmientoBarbieri [aut] 
Maintainer  Mauricio OlivaresGonzalez <[email protected]> 
License  GPL (>= 2) 
Version  0.1.4 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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