Software packages for randomization inference are few and far between. This forces researchers either to rely on specialized stand-alone programs or to use classical statistical tests that may require implausible assumptions about their data-generating process. The absence of a flexible and comprehensive package for randomization inference is an obstacle for researchers from a wide range of disciplines who turn to R as a language for carrying out their data analysis. We present permuter, a package for randomization inference. We illustrate the program's capabilities with several examples:
- a randomized experiment comparing the student evaluations of teaching for male and female instructors (MacNell et. al, 2014)
- a study of the association between salt consumption and mortality at the level of nations
- an assessment of inter-rater reliability for a series of labels assigned by multiple raters to video footage of children on the autism spectrum
We discuss future plans for permuter and the role of software development in statistics.
Kellie Ottoboni is a former BIDS Data Science Fellow and a graduate of UC Berkeley's Department of Statistics. Her research at BIDS focused on using robust nonparametric statistics and machine learning to make causal inferences from data in the health and social sciences. The goal was to make reliable inferences while making minimal assumptions about the models generating the data. In addition to developing new statistical methods and studying their theoretical properties, Kellie wrote open source software implementing nonparametric methods in R and Python.