Implementation of hierarchical inference based on Meinshausen (2008). Hierarchical testing of variable importance. Biometrika, 95(2), 265-278 and Renaux, Buzdugan, Kalisch, and Bühlmann, (2020). Hierarchical inference for genome-wide association studies: a view on methodology with software. Computational Statistics, 35(1), 1-40. The R-package 'hierbase' offers tools to perform hierarchical inference for one or multiple data sets based on ready-to-use (group) test functions or alternatively a user specified (group) test function. The procedure is based on a hierarchical multiple testing correction and controls the family-wise error rate (FWER). The functions can easily be run in parallel. Hierarchical inference can be applied to (low- or) high-dimensional data sets to find significant groups or single variables (depending on the signal strength and correlation structure) in a data-driven and automated procedure. Possible applications can for example be found in statistical genetics and statistical genomics.