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:
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')) |
- 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.
Last updated 2 years agofrom:1f7f89a107. Checks:OK: 1 NOTE: 1. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 07 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 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