A unified latent class modeling framework that encompasses both latent class analysis (LCA) and latent profile analysis (LPA), offering a one-stop solution for latent class modeling. It implements state-of-the-art parameter estimation methods, including the expectation–maximization (EM) algorithm, neural network estimation (NNE; requires users to have 'Python' and its dependent libraries installed on their computer), and integration with 'Mplus' (requires users to have 'Mplus' installed on their computer). In addition, it provides commonly used model fit indices such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC), as well as classification accuracy measures such as entropy. The package also includes fully functional likelihood ratio tests (LRT) and bootstrap likelihood ratio tests (BLRT) to facilitate model comparison, along with bootstrap-based and observed information matrix-based standard error estimation. Furthermore, it supports the standard three-step approach for LCA, LPA, and latent transition analysis (LTA) with covariates, enabling detailed covariate analysis. Finally, it includes several user-friendly auxiliary functions to enhance interactive usability.

An implementation of the Log Cumulative Probability Model (LCPM) 
  and Proportional Probability Model (PPM) for which the Maximum Likelihood Estimates are determined using constrained optimization. 
  This implementation accounts for the implicit constraints on the parameter space. Other 
  features such as standard errors, z tests and p-values use standard methods adapted from the results based on constrained optimization.

For fitting Bayesian joint latent class and regression models using
    Gibbs sampling. See the documentation for the model.
    The technical details of the model implemented here are described in Elliott,
    Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham,
    Belinda L., "Methods to account for uncertainty in latent class assignments when
    using latent classes as predictors in regression models, with application to
    acculturation strategy measures" (2020) In press at Epidemiology
    <doi:10.1097/EDE.0000000000001139>.