Source code for spreadr can be downloaded here: https://github.com/csqsiew/spreadr Abstract The notion of spreading activation is a central theme in the cognitive sciences; however, the tools for implementing spreading activation computationally are not as readily available. This paper introduces the spreadr R package, which can implement spreading activation within a specified network structure. The algorithmic method implemented in the spreadr subroutines followed the approach described in Vitevitch, Ercal, and Adagarla (2011), who viewed activation as a fixed cognitive resource that could Dzspreaddz among connected nodes in a network. Three sets of simulations were conducted using the package. The first set of simulations successfully reproduced the results reported in Vitevitch et al. (2011), who showed that a simple mechanism of spreading activation could account for the clustering coefficient effect in spoken word recognition. The second set of simulations showed that the same mechanism could be extended to account for higher false alarm rates for low clustering coefficient words in a false memory task. The final set of simulations demonstrated how spreading activation could be used to account for semantic priming effects. It is hoped that this package will encourage cognitive and language scientists to explicitly consider how the structure of cognitive systems such as the mental lexicon and semantic memory interacts with the process of spreading activation. Open-Access Article: https://link.springer.com/article/10.3758/s13428-018-1186-5 OSF project contains the following: 1. Data and R code used in the simulation studies reported in the paper. 2. User manual and R code that explains how spreadr works. 3. Benchmark tests 4. Appendix C - Simulations to compare the outputs of spreadr to a random walk model.