Profile analysis is a multivariate data analysis technique employed in the social sciences that is the statistical equivalent of a repeated measures extension of the MANOVA model. Profile analysis is mainly concerned with test scores; more specifically with profiles of test scores obtained from an assessment. A test score profile shows differences in subscores on tests that are commonly administered in medical, psychological, and educational studies to rank participants of a study on some latent construct. Practitioners in these fields are typically interested in quantifying both an individual’s overall performance on a test (i.e., their level) and variation between scores on subtests within the test (i.e., their pattern). A suite of profile analytic procedures for decomposing observed scores into both level and pattern effects exists for the R programming language in the profileR package (Bulut and Desjardins 2015). This package includes routines to perform criterion-related profile analysis, profile analysis via multidimensional scaling, moderated profile analysis, profile analysis by group, and a within-person factor model to derive score profiles. This presentation will showcase several of these methods, illustrating their application with various data sets included within the package, as well as describing the future direction for the profileR package.