\name{VIM-package}
\alias{VIM-package}
\alias{VIM}
\docType{package}
\title{Visualization and Imputation of Missing Values}
\description{
    This package introduces new tools for the visualization of missing or
    imputed  values in \R, which can be used for exploring the data and the
    structure of the missing  or imputed values. Depending on this structure,
    they may help to identify the mechanism generating the missing values or
    errors, which may have happened in the imputation process. This knowledge
    is necessary for selecting an appropriate imputation method in order to
    reliably estimate the missing values. Thus the visualization tools should
    be applied before imputation and the diagnostic tools afterwards.
    
    Detecting missing values mechanisms is usually done by statistical tests 
    or models.  Visualization of missing and imputed values can support the
    test decision, but also reveals more details about the data structure. Most
    notably, statistical requirements for a test can be checked graphically,
    and problems like outliers or skewed data distributions can be discovered.
    Furthermore, the included plot methods may also be able to detect missing 
    values mechanisms in the first place.
    
    A graphical user interface allows an easy handling of the plot methods.  
    In addition, \code{VIM} can be used for data from essentially any field.
}
\details{
\tabular{ll}{
Package: \tab VIM\cr
Version: \tab 3.0.3\cr
Date: \tab 2013-01-09\cr
Depends: \tab R (>= 2.10),e1071,car, colorspace, nnet, robustbase, tcltk,
tkrplot, sp, vcd, Rcpp\cr
Imports: \tab car, colorspace, grDevices, robustbase, stats, tcltk, sp,
utils, vcd\cr
License: \tab GPL (>= 2)\cr
URL: \tab http://cran.r-project.org/package=VIM\cr
}
}
\references{
M. Templ, A. Alfons, P. Filzmoser (2012)  
Exploring incomplete data using visualization tools. 
\emph{Journal of Advances in Data Analysis and Classification}, Online first. DOI: 10.1007/s11634-011-0102-y.  

M. Templ, A. Kowarik, P. Filzmoser (2011)  
Iterative stepwise regression imputation using standard and robust methods. 
\emph{Journal of Computational Statistics and Data Analysis}, Vol. 55, pp. 2793-2806.  
}
\author{
Matthias Templ, Andreas Alfons, Alexander Kowarik, Bernd Prantner

Maintainer: Matthias Templ <templ@tuwien.ac.at>
}
\keyword{package}
