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.