| clustGLMM-package | Model-Based Clustering of Mixed-Type Longitudinal Data |
| as_coda | Change the Output Formats of 'clustGLMM' |
| clustering_probabilities_and_deviance | Evaluate Clustering Probabilities and Deviance of Output of 'clustGLMM' |
| clustGLMM | Sample MCMC for a Mixture of GLMMs of Mixed-type |
| default_param | Setting Default Values in 'clustGLMM' |
| default_save | Setting Default Values in 'clustGLMM' |
| default_tuning | Setting Default Values in 'clustGLMM' |
| default_varying | Setting Default Values in 'clustGLMM' |
| from_C_to_list | Change the Output Formats of 'clustGLMM' |
| from_C_to_matrix | Change the Output Formats of 'clustGLMM' |
| from_list_to_C | Change the Output Formats of 'clustGLMM' |
| from_list_to_coda | Change the Output Formats of 'clustGLMM' |
| from_list_to_matrix | Change the Output Formats of 'clustGLMM' |
| from_matrix_to_C | Change the Output Formats of 'clustGLMM' |
| from_matrix_to_coda | Change the Output Formats of 'clustGLMM' |
| from_matrix_to_list | Change the Output Formats of 'clustGLMM' |
| generate_longitudinal_mixed_type_data | Generate an Artificial Longitudinal Dataset with Mixed-type Outcomes |
| get_scalar_samples | Extract Samples for Specified Scalar Model Parameter |
| longitudinal_mixed_type_data | A Simulated Dataset of Longitudinal Mixed-Type Data |
| nice_nrow_ncol | Determine Optimal Matrix Dimensions Given the Number of Cells |
| permute_cluster_labels | Post-process the Chains Sampled with 'clustGLMM' |
| plot.clustglmm | Plot the Output of 'clustGLMM' |
| plot_ACF | Plot MCMC Samples from 'clustGLMM' |
| plot_ACF_param | Plot MCMC Samples from 'clustGLMM' |
| plot_cat_vs_x_grouped | Plot Clustered Longitudinal (Panel) Data |
| plot_clusters | Plot MCMC Samples from 'clustGLMM' |
| plot_clusters_ECDF_param | Plot MCMC Samples from 'clustGLMM' |
| plot_clusters_kerneldensity_param | Plot MCMC Samples from 'clustGLMM' |
| plot_diagnostics | Plot MCMC Samples from 'clustGLMM' |
| plot_ECDF | Plot MCMC Samples from 'clustGLMM' |
| plot_ECDF_param | Plot MCMC Samples from 'clustGLMM' |
| plot_kerneldensity | Plot MCMC Samples from 'clustGLMM' |
| plot_kerneldensity_param | Plot MCMC Samples from 'clustGLMM' |
| plot_ng_trace_chain_split | Plot MCMC Samples from 'clustGLMM' |
| plot_num_vs_x_grouped | Plot Clustered Longitudinal (Panel) Data |
| plot_traceplots | Plot MCMC Samples from 'clustGLMM' |
| plot_traceplots_param | Plot MCMC Samples from 'clustGLMM' |
| post_processing | Post-process the Chains Sampled with 'clustGLMM' |
| predict.clustglmm | Predict Method for Class 'clustglmm' |
| print.clustglmm | Print the Output of 'clustGLMM' |
| print.summary.clustglmm | Compute the Summary Statistics of 'clustGLMM' Output |
| psurvey | A Simulated Panel Survey Data |
| psurvey_latent | A Simulated Panel Survey Data |
| slategray.colors | Slategray Color Palette |
| slategrey.colors | Slategray Color Palette |
| summary.clustglmm | Compute the Summary Statistics of 'clustGLMM' Output |