This package is considered a duplicate. The official version of this package is found at:https://inbo.r-universe.dev/multimput

Package: multimput 0.2.15

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]

multimput_0.2.15.tar.gz
multimput_0.2.15.zip(r-4.7)multimput_0.2.15.zip(r-4.6)multimput_0.2.15.zip(r-4.5)
multimput_0.2.15.tgz(r-4.6-any)multimput_0.2.15.tgz(r-4.5-any)
multimput_0.2.15.tar.gz(r-4.7-any)multimput_0.2.15.tar.gz(r-4.6-any)
multimput_0.2.15.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
multimput/json (API)

# Install 'multimput' in R:

install.packages('multimput', repos = c('https://thierryo.r-universe.dev', 'https://cloud.r-project.org'))

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

Pkgdown/docs site:https://inbo.github.io

Datasets:

waterfowl - The observation pattern in the Flemish waterfowl dataset

On CRAN:

imputation imputation-model

3.32 score 1 stars 1 packages 14 scripts 9 exports 51 dependencies

Last updated from:654e208239. Checks:9 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	OK	268
source / vignettes	OK	266
linux-release-x86_64	OK	221
macos-release-arm64	OK	248
macos-oldrel-arm64	OK	271
windows-devel	OK	250
windows-release	OK	242
windows-oldrel	OK	225
wasm-release	OK	152

Exports:aggregate_impute generate_data hurdle_impute impute missing_at_random missing_current_count missing_observed missing_volunteer model_impute

Dependencies:assertthat boot class classInt cli cpp11 DBI digest dplyr e1071 fmesher generics glue INLA KernSmooth lattice lifecycle lme4 magrittr MASS Matrix MatrixModels minqa mvtnorm nlme nloptr pillar pkgconfig proxy purrr R6 rbibutils Rcpp RcppEigen Rdpack reformulas rlang s2 sf sp splancs stringi stringr tibble tidyr tidyselect units utf8 vctrs withr wk

Model data with missing observations using multiple imputation

Thierry Onkelinx

Rendered fromimpute.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2024-10-04
Started: 2022-02-03

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Aggregate an imputed dataset	aggregate_impute aggregate_impute,aggregatedImputed-method aggregate_impute,ANY-method aggregate_impute,rawImputed-method
The 'aggregatedImputed' class Holds an aggregated imputation data set	aggregatedImputed-class
Generate simulated data	generate_data
Combine two models into a hurdle model	hurdle_impute
Impute a dataset	impute impute,ANY-method impute,glmerMod-method impute,lm-method impute,maybeInla-method
The 'maybeInla' class	maybeInla-class
Generate missing data at random	missing_at_random
Generate missing data depending on the counts	missing_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 dataset	model_impute model_impute,aggregatedImputed-method model_impute,ANY-method
The 'rawImputed' class Holds a dataset and imputed values	rawImputed-class
The observation pattern in the Flemish waterfowl dataset	waterfowl