Package: missCompare
Type: Package
Title: Intuitive Missing Data Imputation Framework
Version: 1.0.1
Authors@R: c(person("Tibor", "V. Varga", email = "tirgit@hotmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2383-699X")),
            person("David", "Westergaard", email = "david.westergaard@cpr.ku.dk", role= c("aut"), comment = c(ORCID = "0000-0003-0128-8432")))
Maintainer: Tibor V. Varga <tirgit@hotmail.com>
Description: Offers a convenient pipeline to test and compare various missing data
  imputation algorithms on simulated and real data. The central assumption behind missCompare is that structurally
  different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets
  with non correlated variables) will benefit differently from different missing data imputation algorithms.
  missCompare takes measurements of your dataset and sets up a sandbox to try a curated list of standard and 
  sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns.
  missCompare will also impute your real-life dataset for you after the selection of the best performing algorithm
  in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you 
  assess imputation performance.    
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
BugReports: https://github.com/Tirgit/missCompare/issues
Depends: R (>= 3.5.0)
biocViews:
Imports: Amelia, data.table, dplyr, ggdendro, ggplot2, Hmisc, ltm,
        magrittr, MASS, Matrix, mi, mice, missForest, missMDA,
        pcaMethods, plyr, rlang, stats, utils, tidyr, VIM
Suggests: testthat, knitr, rmarkdown, devtools
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-31 07:55:03 UTC; med-tv_
Author: Tibor V. Varga [aut, cre] (<https://orcid.org/0000-0002-2383-699X>),
  David Westergaard [aut] (<https://orcid.org/0000-0003-0128-8432>)
Repository: CRAN
Date/Publication: 2019-02-05 22:22:07 UTC
