Non-parametric Bayesian Multiple Imputation for Categorical Data
Quanli Wang, Daniel Manrique-Vallier, Jerome P. Reiter and Jingchen Hu
These routines create multiple imputations of missing at random categorical data, with or without structural zeros. Imputations are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling.

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.

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>.

methods, rlang, reshape2, ggplot2, dplyr, bayesplot

Nonparametric Combination of Hypothesis Tests
person("Devin", "Caughey", email="devin.caughey@gmail.com", role=c("aut", "cre"))
An implementation of nonparametric combination of hypothesis tests.
This package performs nonparametric combination (Pesarin and Salmaso 2010),
a permutation-based procedure for jointly testing multiple hypotheses. The
tests are conducted under the global "sharp" null hypothesis of no effects,
and the component tests are combined on the metric of their p-values. A
key feature of nonparametric combination is that it accounts for the
dependence among tests under the null hypothesis. In addition to the
"NPC" function, which performs nonparametric combination itself, the
package also contains a number of helper functions, many of which calculate
a test statistic given an input of data.