A General Framework for Latent Classify and Profile Analysis
Adjust Categorical Response Data for Polytomous Items
Validate response matrix against expected polytomous category counts
Model Comparison Tool
S3 Methods: extract
Calculate Number of Free Parameters in Latent Class Analysis
Calculate Number of Free Parameters in Latent Profile Analysis
Calculate Number of Free Parameters in Latent Transition Analysis
Compute Posterior Latent Class Probabilities Based on Fixed Parameters
Compute Posterior Latent Profile Probabilities Based on Fixed Paramete...
Compute Standard Errors
Install Required Python Dependencies for Neural Latent Variable Models
Initialize LCA Parameters via K-means Clustering
Fit Latent Class Analysis Models
Latent Class/Profile Analysis with Covariates
Compute the Logistic (Sigmoid) Function
Fit Latent Profile Analysis
Bootstrap Likelihood Ratio Test
Likelihood Ratio Test
Lo-Mendell-Rubin likelihood ratio test
Latent Transition Analysis (LTA)
Column-wise Z-Score Standardization
Visualize Response Distributions with Density Plots
S3 Methods: print
Generate Random Samples from the Dirichlet Distribution
Generate a Random Correlation Matrix via C-Vine Partial Correlations
Simulate Data for Latent Class Analysis
Simulate Data for Latent Profile Analysis
Simulate Data for Latent Transition Analysis (LTA)
S3 Methods: summary
S3 Methods: update
Calculate Average Posterior Probability (AvePP)
Compute Classification Error Probability (CEP) Matrices
Calculate Classification Entropy
Calculate Fit Indices
Calculate Log-Likelihood for Latent Class Analysis
Calculate Log-Likelihood for Latent Profile Analysis
Calculate Log-Likelihood for Latent Transition Analysis
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.