Kmeans.LCA              Initialize LCA Parameters via K-means
                        Clustering
LCA                     Fit Latent Class Analysis Models
LCPA                    Latent Class/Profile Analysis with Covariates
LPA                     Fit Latent Profile Analysis
LRT.test                Likelihood Ratio Test
LRT.test.Bootstrap      Bootstrap Likelihood Ratio Test
LRT.test.VLMR           Lo-Mendell-Rubin likelihood ratio test
LTA                     Latent Transition Analysis (LTA)
adjust.response         Adjust Categorical Response Data for Polytomous
                        Items
check.response          Validate response matrix against expected
                        polytomous category counts
compare.model           Model Comparison Tool
extract                 S3 Methods: extract
get.AvePP               Calculate Average Posterior Probability (AvePP)
get.CEP                 Compute Classification Error Probability (CEP)
                        Matrices
get.Log.Lik.LCA         Calculate Log-Likelihood for Latent Class
                        Analysis
get.Log.Lik.LPA         Calculate Log-Likelihood for Latent Profile
                        Analysis
get.Log.Lik.LTA         Calculate Log-Likelihood for Latent Transition
                        Analysis
get.P.Z.Xn.LCA          Compute Posterior Latent Class Probabilities
                        Based on Fixed Parameters
get.P.Z.Xn.LPA          Compute Posterior Latent Profile Probabilities
                        Based on Fixed Parameters
get.SE                  Compute Standard Errors
get.entropy             Calculate Classification Entropy
get.fit.index           Calculate Fit Indices
get.npar.LCA            Calculate Number of Free Parameters in Latent
                        Class Analysis
get.npar.LPA            Calculate Number of Free Parameters in Latent
                        Profile Analysis
get.npar.LTA            Calculate Number of Free Parameters in Latent
                        Transition Analysis
install_python_dependencies
                        Install Required Python Dependencies for Neural
                        Latent Variable Models
logit                   Compute the Logistic (Sigmoid) Function
normalize               Column-wise Z-Score Standardization
plotResponse            Visualize Response Distributions with Density
                        Plots
print                   S3 Methods: print
rdirichlet              Generate Random Samples from the Dirichlet
                        Distribution
sim.LCA                 Simulate Data for Latent Class Analysis
sim.LPA                 Simulate Data for Latent Profile Analysis
sim.LTA                 Simulate Data for Latent Transition Analysis
                        (LTA)
sim.correlation         Generate a Random Correlation Matrix via C-Vine
                        Partial Correlations
summary                 S3 Methods: summary
update                  S3 Methods: update
