Man pages for NPBayesImputeCat
Non-Parametric Bayesian Multiple Imputation for Categorical Data
| compute_probs | Estimating marginal and joint probabilities in imputed or... |
| CreateModel | Create and initialize the Lcm model object |
| DPMPM_nozeros_imp | Use DPMPM models to impute missing data where there are no... |
| DPMPM_nozeros_syn | Use DPMPM models to synthesize data where there are no... |
| DPMPM_zeros_imp | Use DPMPM models to impute missing data where there are no... |
| fit_GLMs | Fit GLM models for imputed or synthetic datasets |
| GetDataFrame | Convert imputed data to a dataframe, using the same setting... |
| GetMCZ | Convert disjointed structrual zeros to a dataframe, using the... |
| kstar_MCMCdiag | Perform MCMC diagnostics for kstar |
| Lcm | Class '"Rcpp_Lcm"' |
| marginal_compare_all_imp | Plot estimated marginal probabilities from observed data vs... |
| marginal_compare_all_syn | Plot estimated marginal probabilities from observed data vs... |
| MCZ | Example dataframe for structrual zeros based on the... |
| NPBayesImputCat-package | Bayesian Multiple Imputation for Large-Scale Categorical Data... |
| pool_estimated_probs | Pool probability estimates from imputed or synthetic datasets |
| pool_fitted_GLMs | Pool estimates of fitted GLM models in imputed or synthetic... |
| Rcpp_Lcm-class | Rcpp implemenation of the Lcm functions |
| ss16pusa_ds_MCZ | Example dataframe for structrual zeros based on the... |
| ss16pusa_mi_MCZ | Example dataframe for structrual zeros based on the... |
| ss16pusa_sample_nozeros | Example dataframe for input categorical data without... |
| ss16pusa_sample_nozeros_miss | Example dataframe for input categorical data without... |
| ss16pusa_sample_zeros | Example dataframe for input categorical data with structural... |
| ss16pusa_sample_zeros_miss | Example dataframe for input categorical data with structural... |
| UpdateX | Allow user to update the model with data matrix of same kind. |
| X | Example dataframe for input categorical data with missing... |
