Package: NPBayesImputeCat 0.5

Jingchen Hu

NPBayesImputeCat: Non-Parametric Bayesian Multiple Imputation for Categorical Data

These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) <doi:10.1080/10618600.2013.844700>.

Authors:Quanli Wang, Daniel Manrique-Vallier, Jerome P. Reiter and Jingchen Hu

NPBayesImputeCat_0.5.tar.gz
NPBayesImputeCat_0.5.tar.gz(r-4.5-noble)NPBayesImputeCat_0.5.tar.gz(r-4.4-noble)
NPBayesImputeCat_0.5.tgz(r-4.4-emscripten)NPBayesImputeCat_0.5.tgz(r-4.3-emscripten)
NPBayesImputeCat.pdf |NPBayesImputeCat.html✨
NPBayesImputeCat/json (API)

# Install 'NPBayesImputeCat' in R:
install.packages('NPBayesImputeCat', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • MCZ - Example dataframe for structrual zeros based on the NYMockexample dataset.
  • MCZ - Example dataframe for structrual zeros based on the NYMockexample dataset.
  • X - Example dataframe for input categorical data with missing values based on the NYMockexample dataset.
  • X - Example dataframe for input categorical data with missing values based on the NYMockexample dataset.
  • ss16pusa_ds_MCZ - Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.
  • ss16pusa_mi_MCZ - Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.
  • ss16pusa_sample_nozeros - Example dataframe for input categorical data without structural zeros (without missing values).
  • ss16pusa_sample_nozeros_miss - Example dataframe for input categorical data without structural zeros (with missing values).
  • ss16pusa_sample_zeros - Example dataframe for input categorical data with structural zeros (without missing values).
  • ss16pusa_sample_zeros_miss - Example dataframe for input categorical data with structural zeros (with missing values).

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

2.08 score 1 packages 40 scripts 290 downloads 15 exports 46 dependencies

Last updated 2 years agofrom:1f7f89a107. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 07 2024
R-4.5-linux-x86_64NOTEDec 07 2024

Exports:compute_probsCreateModelDPMPM_nozeros_impDPMPM_nozeros_synDPMPM_zeros_impfit_GLMsGetDataFrameGetMCZkstar_MCMCdiagLcmmarginal_compare_all_impmarginal_compare_all_synpool_estimated_probspool_fitted_GLMsUpdateX

Dependencies:abindbackportsbayesplotcheckmateclicolorspacedistributionaldplyrfansifarvergenericsggplot2ggridgesgluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgconfigplyrposteriorR6RColorBrewerRcppreshape2rlangscalesstringistringrtensorAtibbletidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Bayesian Multiple Imputation for Large-Scale Categorical Data with Structural ZerosNPBayesImputeCat-package NPBayesImputeCat
Estimating marginal and joint probabilities in imputed or synthetic datasetscompute_probs
Create and initialize the Lcm model objectCreateModel
Use DPMPM models to impute missing data where there are no structural zerosDPMPM_nozeros_imp
Use DPMPM models to synthesize data where there are no structural zerosDPMPM_nozeros_syn
Use DPMPM models to impute missing data where there are no structural zerosDPMPM_zeros_imp
Fit GLM models for imputed or synthetic datasetsfit_GLMs
Convert imputed data to a dataframe, using the same setting from original input data.GetDataFrame
Convert disjointed structrual zeros to a dataframe, using the same setting from original structrual zero data.GetMCZ
Perform MCMC diagnostics for kstarkstar_MCMCdiag
Class '"Rcpp_Lcm"'Lcm
Plot estimated marginal probabilities from observed data vs imputed datasetsmarginal_compare_all_imp
Plot estimated marginal probabilities from observed data vs synthetic datasetsmarginal_compare_all_syn
Example dataframe for structrual zeros based on the NYMockexample dataset.MCZ
Pool probability estimates from imputed or synthetic datasetspool_estimated_probs
Pool estimates of fitted GLM models in imputed or synthetic datasetspool_fitted_GLMs
Rcpp implemenation of the Lcm functionsRcpp_Lcm-class
Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.ss16pusa_ds_MCZ
Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.ss16pusa_mi_MCZ
Example dataframe for input categorical data without structural zeros (without missing values).ss16pusa_sample_nozeros
Example dataframe for input categorical data without structural zeros (with missing values).ss16pusa_sample_nozeros_miss
Example dataframe for input categorical data with structural zeros (without missing values).ss16pusa_sample_zeros
Example dataframe for input categorical data with structural zeros (with missing values).ss16pusa_sample_zeros_miss
Allow user to update the model with data matrix of same kind.UpdateX
Example dataframe for input categorical data with missing values based on the NYMockexample dataset.X