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NPBayesImputeCat
View on CRAN: Click
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Download and install NPBayesImputeCat package within the R console
Install from CRAN:
install.packages("NPBayesImputeCat")
Install from Github:
library("remotes")
install_github("cran/NPBayesImputeCat")
Install by package version:
library("remotes")
install_version("NPBayesImputeCat", "0.5")
Attach the package and use:
library("NPBayesImputeCat")
Maintained by
Jingchen Hu
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-11-18
Latest Update: 2022-10-03
Description:
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) .
How to cite:
Jingchen Hu (2018). NPBayesImputeCat: Non-Parametric Bayesian Multiple Imputation for Categorical Data. R package version 0.5, https://cran.r-project.org/web/packages/NPBayesImputeCat
Previous versions and publish date:
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Functions, R codes and Examples using
the NPBayesImputeCat R package
Some associated functions: CreateModel . DPMPM_nozeros_imp . DPMPM_nozeros_syn . DPMPM_zeros_imp . GetDataFrame . GetMCZ . Lcm . MCZ . NPBayesImputCat-package . Rcpp_Lcm-class . UpdateX . X . compute_probs . fit_GLMs . kstar_MCMCdiag . marginal_compare_all_imp . marginal_compare_all_syn . pool_estimated_probs . pool_fitted_GLMs . ss16pusa_ds_MCZ . ss16pusa_mi_MCZ . ss16pusa_sample_nozeros . ss16pusa_sample_nozeros_miss . ss16pusa_sample_zeros . ss16pusa_sample_zeros_miss .
Some associated R codes: AllFunctions.R . ArrayUtils.R . NewUpdates_v2.R . zzz.R . Full NPBayesImputeCat package functions and examples
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