RandseqR: An R Package for Describing Performance on the Random Number Generation Task

The Random Number Generation (RNG) task has a long history in neuropsychology as an assessment procedure for executive functioning. In recent years, understanding of human (executive) behavior has gradually changed from reflecting a static to a dynamic process and this shift in thinking about behavior gives a new angle to interpret test results. However, this shift also asks for different methods to process random number sequences. The RNG task is suited for applying non-linear methods needed to uncover the underlying dynamics of random number generation. In the current article we present RandseqR: an R-package that combines the calculation of classic randomization measures and Recurrence Quantification Analysis. RandseqR is an easy to use, flexible and fast way to process random number sequences and readies the RNG task for current scientific and clinical use.