Latent class analysis (LCA) refers to techniques for identifying groups in data based on a parametric model. Examples include mixture models, LCA with ordinal indicators, and latent class growth analysis. Despite its popularity, there is limited guidance with respect to decisions that must be made when conducting and reporting LCA. Moreover, there is a lack of user-friendly open-source implementations. Based on contemporary academic discourse, this paper introduces recommendations for LCA which are summarized in the SMART-LCA checklist: Standards for More Accuracy in Reporting of different Types of Latent Class Analysis. The free open-source R-package package "tidySEM" implements the practices recommended here. It is easy for beginners to adopt thanks to user-friendly wrapper functions, and yet remains relevant for expert users as its models are integrated within the "OpenMx" structural equation modeling framework and remain fully customizable. The Appendices and "tidySEM" package vignettes include tutorial examples of common applications of LCA.