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VIM (version 6.2.6)

Visualization and Imputation of Missing Values

Description

New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.

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Version

Install

install.packages('VIM')

Monthly Downloads

11,768

Version

6.2.6

License

GPL (>= 2)

Issues

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Maintainer

Matthias Templ

Last Published

September 18th, 2025

Functions in VIM (6.2.6)

growdotMiss

Growing dot map with information about missing/imputed values
irmi

Iterative robust model-based imputation (IRMI)
hotdeck

Hot-Deck Imputation
imputeRobustChain

FUNCTION_TITLE
imputeRobust

Robust imputation
histMiss

Histogram with information about missing/imputed values
impPCA

Iterative EM PCA imputation
initialise

Initialization of missing values
kola.background

Background map for the Kola project data
kNN

k-Nearest Neighbour Imputation
medianSamp

Aggregation function for a ordinal variable
mosaicMiss

Mosaic plot with information about missing/imputed values
marginplot

Scatterplot with additional information in the margins
maxCat

Aggregation function for a factor variable
matrixplot

Matrix plot
mapMiss

Map with information about missing/imputed values
matchImpute

Fast matching/imputation based on categorical variable
marginmatrix

Marginplot Matrix
parcoordMiss

Parallel coordinate plot with information about missing/imputed values
pairsVIM

Scatterplot Matrices
rangerImpute

Random Forest Imputation
pulplignin

Pulp lignin content
sampleCat

Random aggregation function for a factor variable
scattJitt

Bivariate jitter plot
pbox

Parallel boxplots with information about missing/imputed values
prepare

Transformation and standardization
rugNA

Rug representation of missing/imputed values
regressionImp

Regression Imputation
scattMiss

Scatterplot with information about missing/imputed values
scattmatrixMiss

Scatterplot matrix with information about missing/imputed values
xgboostImpute

Xgboost Imputation
tableMiss

create table with highlighted missings/imputations
sleep

Mammal sleep data
spineMiss

Spineplot with information about missing/imputed values
toydataMiss

Simulated toy data set for examples
testdata

Simulated data set for testing purpose
wine

Wine tasting and price
tao

Tropical Atmosphere Ocean (TAO) project data
brittleness

Brittleness index data set
bcancer

Breast cancer Wisconsin data set
collisions

Subset of the collision data
alphablend

Alphablending for colors
colormapMiss

Colored map with information about missing/imputed values
barMiss

Barplot with information about missing/imputed values
chorizonDL

C-horizon of the Kola data with missing values
bgmap

Backgound map
colSequence

HCL and RGB color sequences
colic

Colic horse data set
Animals_na

Animals_na
food

Food consumption
evaluation

Error performance measures
gapMiss

Missing value gap statistics
SBS5242

Synthetic subset of the Austrian structural business statistics data
gowerD

Computes the extended Gower distance of two data sets
VIM-package

The VIM Package
countInf

Count number of infinite or missing values
diabetes

Indian Prime Diabetes Data
aggr

Aggregations for missing/imputed values