Prepares a dataset for imputation by mapping factor levels to integers and scaling Y. Primarily used by hcmm_impute internally
● Data Source:
CranContrib
● Keywords:
● Alias: prepare_data
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Imputations are generated using nonparametric Bayesian joint models (specifically the hierarchcially coupled mixture model with local dependence described in Murray and Reiter (2015); see citation(MixedDataImpute) or http://arxiv.org/abs/1410.0438).
● Data Source:
CranContrib
● Keywords:
● Alias: hcmm_impute
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MixedDataImpute
(Package: MixedDataImpute) :
Missing data imputation for continuous and categorical data using nonparametric Bayesian joint models
Missing data imputation for continuous and categorical data, using nonparametric Bayesian joint models (specifically the hierarchcially coupled mixture model with local dependence described in Murray and Reiter (2015); see citation(NPBayesMixedDataImpute)).
● Data Source:
CranContrib
● Keywords:
● Alias: MixedDataImpute, MixedDataImpute-package
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Generates a list of hyperparameters for use in hcmm_impute. Specifying only hcmmdat or q AND cx will generate default values (see citation).
● Data Source:
CranContrib
● Keywords:
● Alias: hcmm_hyperpar
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remap_imputations
(Package: MixedDataImpute) :
Map raw imputations back to original scale/factor labels.
Map raw imputations back to original scale (for continuous data) or factor labels. Most users can ignore this function, which is primarily used by hcmm_impute internally.
● Data Source:
CranContrib
● Keywords:
● Alias: remap_imputations
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providing 
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