In educational and psychological research, we are often interested in discrete latent states of individuals responding to an assessment (e.g., proficiency or non-proficiency on educational standards, the presence or absence of a psychological disorder). Diagnostic classification models (DCMs; also called cognitive diagnostic models [CDMs]) are a type of psychometric model that facilitates these inferences (Rupp et al., 2010; von Davier & Lee, 2019). DCMs are multi-dimensional, meaning that we can classify respondents on multiple latent attributes within a profile of skills. A Q-matrix is then used to define which items on the assessment measure each attribute. Using the pre-defined latent profiles and the Q-matrix, DCMs then estimate the probability that respondents are in profile, or have the corresponding pattern of proficiency, or presence, of the attributes. This means that DCMs are able to provide fine-grained feedback on specific skills that may need additional instruction in an educational context, or particular symptoms that may be contributing to a diagnosis in a psychological context. Finally, because DCMs are classifying respondents rather than placing them along a performance continuum, these models are able to achieve more reliable results with shorter test lengths (Templin & Bradshaw, 2013), reducing the burden on respondents. Given these benefits, the goal of measr is to make DCMs more accessible to applied researchers and practitioners by providing a simple interface for estimating and evaluating DCMs