remiod1.0.2 package

Reference-Based Multiple Imputation for Ordinal/Binary Response

clm_MI_CR

Apply Copy-Reference(CR) Method to Update JAGS MCMC outputs under MAR ...

clm_MI_delta

Apply Delta adjustment to Update JAGS MCMC outputs under MAR for Cumul...

clm_MI_J2R

Apply Jump-to-Reference(J2R) Method to Update JAGS MCMC outputs under ...

commParams

Common Parameters used by functions of remiod

extract_MIdata

Extract a specified number of multiple imputed datasets

get_class

Obtain ordinal results based on log-odds (eta) and cut-offs (gamma) fr...

get_MI_RB

Create multiple imputed datasets based on assigned imputation method.

get_Mlist

Prepare imputation-model-related information

get_subset

Extract specific parameters from MCMC samples

glm_MI_CR

Apply Copy-Reference(CR) Method to Update JAGS MCMC outputs under MAR ...

glm_MI_delta

Apply Delta adjustment to Update JAGS MCMC outputs under MAR for Gener...

glm_MI_J2R

Apply Jump-to-Reference(J2R) Method to Update JAGS MCMC outputs under ...

list.models

Listing the sequence of models used for imputation

mcmcplot

Visualizing the posterior sample Creates a set of plots for visually e...

miAnalyze

Takes multiply imputed datasets (as generated by the `extract_MIdata()...

opm_MI_CR

Apply Copy-Reference(CR) Method to Update JAGS MCMC outputs under MAR ...

opm_MI_delta

Apply Delta adjustment to Update JAGS MCMC outputs under MAR for Cumul...

opm_MI_J2R

Apply Jump-to-Reference(J2R) Method to Update JAGS MCMC outputs under ...

prep_MCMC

Extract MCMC samples of monitored parameters from JointAI object. Code...

remiod

Reference-Based Multiple Imputation for Ordinal/Binary Response

summary

Summarize the results from an object of class remiod

tang_MI_RB

Implement controlled multiple imputation algorithms proposed by Tang

updateMI

Apply a MI method following initial run of remoid function

Reference-based multiple imputation of ordinal and binary responses under Bayesian framework, as described in Wang and Liu (2022) <arXiv:2203.02771>. Methods for missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta Adjustment which can generate tipping point analysis.