R: Model-Based Clustering of Mixed-Type Longitudinal Data

# Model-Based Clustering of Mixed-Type Longitudinal Data

## Documentation for package ‘clustGLMM’ version 1.0.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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