DESCRIPTION

Type: Package
Package: multimput
Title: Using Multiple Imputation to Address Missing Data
Version: 0.2.14
Authors@R: c(
    person("Thierry", "Onkelinx", , "thierry.onkelinx@inbo.be", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-8804-4216", affiliation = "Research Institute for Nature and Forest (INBO)")),
    person("Koen", "Devos", , "koen.devos@inbo.be", role = "aut",
           comment = c(ORCID = "0000-0001-7265-6349", affiliation = "Research Institute for Nature and Forest (INBO)")),
    person("Paul", "Quataert", role = "aut",
           comment = c(ORCID = "0000-0002-6894-9402")),
    person("Research Institute for Nature and Forest (INBO)", , , "info@inbo.be", role = c("cph", "fnd"))
  )
Description: 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>).
License: GPL-3
URL: https://doi.org/10.5281/zenodo.598331,
    https://github.com/inbo/multimput, https://inbo.github.io/multimput/
BugReports: https://github.com/inbo/multimput/issues
Depends:
    R (>= 3.0.0)
Imports:
    assertthat,
    digest,
    dplyr,
    INLA (>= 22.01.19),
    lme4,
    methods,
    mvtnorm,
    purrr,
    rlang,
    tibble,
    tidyr,
    tidyselect
Suggests:
    ggplot2,
    knitr,
    MASS,
    mgcv,
    rmarkdown,
    sn,
    testthat
VignetteBuilder: 
    knitr
Additional_repositories: https://inla.r-inla-download.org/R/stable
Config/checklist/communities: inbo
Config/checklist/keywords: missing data, multiple imputation, Rubin
Encoding: UTF-8
Language: en-GB
LazyData: TRUE
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
Collate: 
    'raw_imputed_class.R'
    'aggregated_imputed_class.R'
    'aggregate_impute.R'
    'check_old_names.R'
    'datasets.R'
    'generate_data.R'
    'hurdle_impute.R'
    'import_s3_classes.R'
    'impute_generic.R'
    'impute_glmermod.R'
    'impute_inla.R'
    'impute_lm.R'
    'missing_at_random.R'
    'missing_current_count.R'
    'missing_observed.R'
    'missing_volunteer.R'
    'model_impute.R'