# Reference

## All functions
`classify()` | Classify data points
`coef(<dpGLM>)` | Extract dpGLM fitted coefficients
`coef(<hdpGLM>)` | Extract hdpGLM fitted coefficients
`hdpGLM()` | Fit Hierarchical Dirichlet Process GLM
`hdpGLM_classify()` | Deprecated
`hdpGLM_package` | hdpGLM: A package for computating Hierarchical Dirichlet Process Generalized Linear Models
`hdpGLM_simParameters()` | Simulate the parameters of the model
`hdpGLM_simulateData()` | Simulate a Data Set from hdpGLM
`mcmc_info.dpGLM()` | mcmc
`mcmc_info.hdpGLM()` | mcmc
`nclusters()` | nclusters
`plot(<dpGLM>)` | Default plot for class dpGLM
`plot(<hdpGLM>)` | Plot
`plot_beta()` | Plot beta posterior distribution
`plot_beta_sim()` | Plot simulated data
`plot_hdpglm()` | Plot posterior distributions
`plot_pexp_beta()` | Plot beta posterior expectation
`plot_tau()` | Plot tau
`predict(<dpGLM>)` | dpGLM Predicted values
`predict(<hdpGLM>)` | hdpGLM Predicted values
`print(<dpGLM>)` | Print
`print(<dpGLM_data>)` | Print
`print(<hdpGLM>)` | Print
`print(<hdpGLM_data>)` | Print
`summary(<dpGLM>)` | Summary for dpGLM class
`summary(<dpGLM_data>)` | Summary dpGLM data
`summary(<hdpGLM>)` | Summary for hdpGLM class
`summary(<hdpGLM_data>)` | Summary
`summary_tidy()` | Tidy summary
`welfare` | Fake data set with 2000 observations
`welfare2` | Fake data set with 2000 observations