Efficient implementations of network science tools to facilitate research into human (semantic) memory. In its current version, the package contains several methods to infer networks from verbal fluency data, various network growth models, diverse (switcher-) random walk processes, and tools to analyze and visualize networks. To deliver maximum performance the majority of the code is written in C++. For an application see: Wulff, D. U., Hills, T., & Mata, R. (2018) <doi:10.31234/osf.io/s73dp>.
| Label | Latest Version |
|---|---|
| main | 0.1.0 |
