Package: NestedCategBayesImpute 1.2.1

NestedCategBayesImpute: Modeling, Imputing and Generating Synthetic Versions of Nested Categorical Data in the Presence of Impossible Combinations

This tool set provides a set of functions to fit the nested Dirichlet process mixture of products of multinomial distributions (NDPMPM) model for nested categorical household data in the presence of impossible combinations. It has direct applications in imputing missing values for and generating synthetic versions of nested household data.

Authors:Quanli Wang, Olanrewaju Akande, Jingchen Hu, Jerry Reiter and Andres Barrientos

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NestedCategBayesImpute/json (API)

# Install 'NestedCategBayesImpute' in R:
install.packages('NestedCategBayesImpute', repos = c('https://akandelanre.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

27 exports 2 stars 0.49 score 20 dependencies 5 scripts 144 downloads

Last updated 6 years agofrom:d5764eb9b6. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 07 2024
R-4.5-win-x86_64NOTESep 07 2024
R-4.5-linux-x86_64NOTESep 07 2024
R-4.4-win-x86_64NOTESep 07 2024
R-4.4-mac-x86_64NOTESep 07 2024
R-4.4-mac-aarch64NOTESep 07 2024
R-4.3-win-x86_64NOTESep 07 2024
R-4.3-mac-x86_64NOTESep 07 2024
R-4.3-mac-aarch64NOTESep 07 2024

Exports:checkconstraintscheckconstraints_HHhead_at_group_levelcheckSZcheckSZ2GetImpossibleHouseholdsgroupcountgroupcount1Dhouseholds2individualsinitDatainitMissinginitOutputinitParametersRunModelsampleGsamplehouseholdssampleMSampleMissingUpdateAlphaUpdateBetaUpdateLambdaUpdateLambdaWeightedUpdateOmegaUpdateOmegaWeightedUpdatePhiUpdatePhiWeightedUpdatePiUpdatePiWeighted

Dependencies:clicodadplyrfansigenericsgluelatticelifecyclemagrittrpillarpkgconfigR6RcppRcppParallelrlangtibbletidyselectutf8vctrswithr

Readme and manuals

Help Manual

Help pageTopics
Checking a data matrix of households for the possible/impossible status under a predefined set of structural zeros.checkconstraints
Checking a data matrix of households for the possible/impossible status under a predefined set of structural zeros.checkconstraints_HHhead_at_group_level
The new implementation of checkconstraints and will evently replace checkconstraints.checkSZ
Michael: Edit herecheckSZ2
Generate the desired number of impossible households required to observe a given number of possible households.GetImpossibleHouseholds
Generate 2D count table for two integer-valued vectors.groupcount
Generate histogram count for an integer-valued vector.groupcount1D
Convert a household data matrix to the corresponding individual member data matrix.households2individuals
Initialize the input data structure.initData
Initilize the misising data structure from input datainitMissing
Set the output structure for saving posterior samples of parameters.initOutput
Initialize the model parameters for the MCMC.initParameters
Run the mcmc sampler for the model.RunModel
Update household (group) level latent class indexes.sampleG
Rcpp implementation for sampling household data without constraints.samplehouseholds
Update individual level latent class indexes.sampleM
Sample and update missing dataSampleMissing
Update alpha.UpdateAlpha
Update beta.UpdateBeta
Update lambda.UpdateLambda
Update lambda.UpdateLambdaWeighted
Update omega and v.UpdateOmega
Update omega and v.UpdateOmegaWeighted
Update phi.UpdatePhi
Update phi.UpdatePhiWeighted
Update pi and u.UpdatePi
Update pi and u.UpdatePiWeighted