We review the GIDNA and CDM packages in R for fitting cognitive diagnosis/diagnostic classification models. We first provide a summary of their core capabilities and then use both simulated and real data to compare their functionalities in practice. We found that the most relevant routines in the two packages appear to be more similar than different with the Shiny R app of GDINA making the program usage very user-friendly for key tasks. However, working with complex parametric latent-variable models in both packages will always be a task best suited for well-trained data scientists.