Package: multimput 0.2.13

Thierry Onkelinx

multimput: Using Multiple Imputation to Address Missing Data

Accompanying package for the paper: Working with population totals in the presence of missing data comparing imputation methods in terms of bias and precision. Published in 2017 in the Journal of Ornithology volume 158 page 603–615 (<doi:10.1007/s10336-016-1404-9>).

Authors:Thierry Onkelinx [aut, cre], Koen Devos [aut], Paul Quataert [aut], Research Institute for Nature and Forest [cph, fnd]

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multimput.pdf |multimput.html✨
multimput/json (API)

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

Peer review:

Bug tracker:https://github.com/inbo/multimput/issues

Datasets:
  • waterfowl - The observation pattern in the Flemish waterfowl dataset

On CRAN:

imputationimputation-model

14 exports 1 stars 0.71 score 34 dependencies 1 dependents 14 scripts

Last updated 12 months agofrom:1b1fd170bf. Checks:ERROR: 5 WARNING: 2. Indexed: yes.

TargetResultDate
Doc / VignettesFAILAug 26 2024
R-4.5-winERRORAug 26 2024
R-4.5-linuxERRORAug 26 2024
R-4.4-winERRORAug 26 2024
R-4.4-macERRORAug 26 2024
R-4.3-winWARNINGAug 26 2024
R-4.3-macWARNINGAug 26 2024

Exports:aggregate_imputegenerate_datagenerateDatahurdle_imputeimputemissing_at_randommissing_current_countmissing_observedmissing_volunteermissingAtRandommissingCurrentCountmissingObservedmissingVolunteermodel_impute

Dependencies:assertthatbootclicpp11digestdplyrfansigenericsgluelatticelifecyclelme4magrittrMASSMatrixminqamvtnormnlmenloptrpillarpkgconfigpurrrR6RcppRcppEigenrlangstringistringrtibbletidyrtidyselectutf8vctrswithr

Readme and manuals

Help Manual

Help pageTopics
Aggregate an imputed datasetaggregate_impute aggregate_impute,aggregatedImputed-method aggregate_impute,ANY-method aggregate_impute,rawImputed-method
The 'aggregatedImputed' class Holds an aggregated imputation data setaggregatedImputed-class
Generate simulated datagenerate_data
Deprecated functionsgenerateData missingAtRandom missingCurrentCount missingObserved missingVolunteer
Combine two models into a hurdle modelhurdle_impute
Impute a datasetimpute impute,ANY-method impute,glmerMod-method impute,lm-method impute,maybeInla-method
The 'maybeInla' classmaybeInla-class
Generate missing data at randommissing_at_random
Generate missing data depending on the countsmissing_current_count
Generate missing data based on the observed patterns in the real dataset.missing_observed
Generate missing data mimicking choices made by volunteers.missing_volunteer
Model an imputed datasetmodel_impute model_impute,aggregatedImputed-method model_impute,ANY-method
The 'rawImputed' class Holds a dataset and imputed valuesrawImputed-class
The observation pattern in the Flemish waterfowl datasetwaterfowl