A B C D E F G H I K L M N P R S T V W
VIM-package | Visualization and Imputation of Missing Values |
aggr | Aggregations for missing/imputed values |
aggr.data.frame | Aggregations for missing/imputed values |
aggr.default | Aggregations for missing/imputed values |
aggr.survey.design | Aggregations for missing/imputed values |
aggr_work | Aggregations for missing/imputed values |
alphablend | Alphablending for colors |
barMiss | Barplot with information about missing/imputed values |
barMiss.data.frame | Barplot with information about missing/imputed values |
barMiss.default | Barplot with information about missing/imputed values |
barMiss.survey.design | Barplot with information about missing/imputed values |
bcancer | Breast cancer Wisconsin data set |
bgmap | Backgound map |
brittleness | Brittleness index data set |
bubbleMiss | Growing dot map with information about missing/imputed values |
chorizonDL | C-horizon of the Kola data with missing values |
colic | Colic horse data set |
collisions | Subset of the collision data |
colormapMiss | Colored map with information about missing/imputed values |
colormapMiss.data.frame | Colored map with information about missing/imputed values |
colormapMiss.default | Colored map with information about missing/imputed values |
colormapMiss.survey.design | Colored map with information about missing/imputed values |
colormapMissLegend | Colored map with information about missing/imputed values |
colSequence | HCL and RGB color sequences |
colSequenceHCL | HCL and RGB color sequences |
colSequenceRGB | HCL and RGB color sequences |
countInf | Count number of infinite or missing values |
countNA | Count number of infinite or missing values |
diabetes | Indian Prime Diabetes Data |
evaluation | Error performance measures |
existsVm | Environment for the GUI for Visualization and Imputation of Missing Values |
food | Food consumption |
gapMiss | Missing value gap statistics |
getVm | Environment for the GUI for Visualization and Imputation of Missing Values |
gowerD | Computes the extended Gower distance of two data sets |
growdotMiss | Growing dot map with information about missing/imputed values |
growdotMiss.data.frame | Growing dot map with information about missing/imputed values |
growdotMiss.default | Growing dot map with information about missing/imputed values |
growdotMiss.survey.design | Growing dot map with information about missing/imputed values |
histMiss | Histogram with information about missing/imputed values |
histMiss.data.frame | Histogram with information about missing/imputed values |
histMiss.default | Histogram with information about missing/imputed values |
histMiss.survey.design | Histogram with information about missing/imputed values |
hotdeck | Hot-Deck Imputation |
hotdeck.data.frame | Hot-Deck Imputation |
hotdeck.default | Hot-Deck Imputation |
hotdeck.survey.design | Hot-Deck Imputation |
iimagMiss | Matrix plot |
initialise | Initialization of missing values |
irmi | Iterative robust model-based imputation (IRMI) |
irmi.data.frame | Iterative robust model-based imputation (IRMI) |
irmi.default | Iterative robust model-based imputation (IRMI) |
irmi.survey.design | Iterative robust model-based imputation (IRMI) |
kNN | k-Nearest Neighbour Imputation |
kNN.data.frame | k-Nearest Neighbour Imputation |
kNN.data.table | k-Nearest Neighbour Imputation |
kNN.default | k-Nearest Neighbour Imputation |
kNN.survey.design | k-Nearest Neighbour Imputation |
kola.background | Background map for the Kola project data |
lr | Error performance measures |
mape | Error performance measures |
mapMiss | Map with information about missing/imputed values |
mapMiss.data.frame | Map with information about missing/imputed values |
mapMiss.default | Map with information about missing/imputed values |
mapMiss.survey.design | Map with information about missing/imputed values |
marginmatrix | Marginplot Matrix |
marginmatrix.data.frame | Marginplot Matrix |
marginmatrix.default | Marginplot Matrix |
marginmatrix.survey.design | Marginplot Matrix |
marginplot | Scatterplot with additional information in the margins |
matchImpute | Fast matching/imputation based on categorical variable |
matchImpute.data.frame | Fast matching/imputation based on categorical variable |
matchImpute.data.table | Fast matching/imputation based on categorical variable |
matchImpute.default | Fast matching/imputation based on categorical variable |
matchImpute.survey.design | Fast matching/imputation based on categorical variable |
matrixplot | Matrix plot |
matrixplot.data.frame | Matrix plot |
matrixplot.default | Matrix plot |
matrixplot.survey.design | Matrix plot |
maxCat | Aggregation function for a factor variable |
mosaicMiss | Mosaic plot with information about missing/imputed values |
mosaicMiss.data.frame | Mosaic plot with information about missing/imputed values |
mosaicMiss.default | Mosaic plot with information about missing/imputed values |
mosaicMiss.survey.design | Mosaic plot with information about missing/imputed values |
msecor | Error performance measures |
msecov | Error performance measures |
nrmse | Error performance measures |
pairsVIM | Scatterplot Matrices |
parcoordMiss | Parallel coordinate plot with information about missing/imputed values |
parcoordMiss.data.frame | Parallel coordinate plot with information about missing/imputed values |
parcoordMiss.default | Parallel coordinate plot with information about missing/imputed values |
parcoordMiss.survey.design | Parallel coordinate plot with information about missing/imputed values |
pbox | Parallel boxplots with information about missing/imputed values |
pbox.data.frame | Parallel boxplots with information about missing/imputed values |
pbox.default | Parallel boxplots with information about missing/imputed values |
pbox.survey.design | Parallel boxplots with information about missing/imputed values |
pfc | Error performance measures |
plot.aggr | Aggregations for missing/imputed values |
prepare | Transformation and standardization |
prepare.data.frame | Transformation and standardization |
prepare.default | Transformation and standardization |
prepare.survey.design | Transformation and standardization |
print.aggr | Aggregations for missing/imputed values |
print.summary.aggr | Aggregations for missing/imputed values |
pulplignin | Pulp lignin content |
putVm | Environment for the GUI for Visualization and Imputation of Missing Values |
regressionImp | Regression Imputation |
regressionImp.data.frame | Regression Imputation |
regressionImp.default | Regression Imputation |
regressionImp.survey.design | Regression Imputation |
rmVm | Environment for the GUI for Visualization and Imputation of Missing Values |
rugNA | Rug representation of missing/imputed values |
sampleCat | Random aggregation function for a factor variable |
SBS5242 | Synthetic subset of the Austrian structural business statistics data |
scattJitt | Bivariate jitter plot |
scattmatrixMiss | Scatterplot matrix with information about missing/imputed values |
scattmatrixMiss.data.frame | Scatterplot matrix with information about missing/imputed values |
scattmatrixMiss.default | Scatterplot matrix with information about missing/imputed values |
scattmatrixMiss.survey.design | Scatterplot matrix with information about missing/imputed values |
scattMiss | Scatterplot with information about missing/imputed values |
sleep | Mammal sleep data |
smape | Error performance measures |
spineMiss | Spineplot with information about missing/imputed values |
summary.aggr | Aggregations for missing/imputed values |
tao | Tropical Atmosphere Ocean (TAO) project data |
testdata | Simulated data set for testing purpose |
TKRmatrixplot | Matrix plot |
toydataMiss | Simulated toy data set for examples |
VIM | Visualization and Imputation of Missing Values |
vmGUIenvir | Environment for the GUI for Visualization and Imputation of Missing Values |
wine | Wine tasting and price |