Gray-level co-occurrence matrices (GLCM) have been applied with success in fields such as medical imaging. However, GLCMs could provide valuable insight when applied to objects other than images. We believe that the study of fitness landscapes, within the field of evolutionary biology, could benefit from analysis via GLCMs. To that end, this package generalizes the methodology underlying GLCMs to non-imaging data. The current focus is, as previously mentioned, on application of GLCMs to fitness landscapes.