Provides a unified framework for finite-mixture latent variable models, including latent class analysis (LCA), latent profile analysis (LPA), latent class/profile analysis with covariates, and latent transition analysis (LTA), within one consistent interface. Estimation methods include the expectation-maximization (EM) algorithm; neural network estimation, which requires 'Python' and its dependent libraries; integration with 'Mplus', which requires an installed copy of 'Mplus'; and stochastic EM (SEM) through the optional 'flexmix', 'Rmixmod', and 'RMixtComp' backends. 'flexmix' and the default 'Rmixmod' path perform configurable warm-up trajectories and promote the best candidates to full SEM replications. 'Rmixmod' additionally exposes its native strategy interface, including chained SEM-to-EM estimation, whereas 'RMixtComp' exposes its native SEM and Gibbs controls without the external warm-up stage. Model assessment includes the Akaike information criterion (AIC), Bayesian information criterion (BIC), Schwarz information criterion (SIC), consistent AIC (CAIC), approximate weight of evidence (AWE), sample-size-adjusted BIC (SABIC), entropy, and average posterior probabilities. Model-comparison procedures include the ordinary likelihood-ratio test, the Mplus TECH11 Vuong-Lo-Mendell-Rubin and adjusted Lo-Mendell-Rubin tests, and fixed-replicate or sequential parametric bootstrap likelihood-ratio tests. Standard errors can be estimated by nonparametric bootstrap, numerical observed information, or analytic observed information based on Louis' identity. Classification-error-adjusted maximum-likelihood and Bolck-Croon-Hagenaars three-step methods support covariates predicting latent membership, initial-status and transition regressions, and latent classes or states predicting continuous and categorical external observed dependent variables. Simulation, posterior-probability, classification-error, extraction, summary, plotting, model-adjustment, and update utilities are also provided for reproducible workflows.