Handling missing values with/in multivariate data analysis (principal component methods)

Documentation for package ‘missMDA’ version 1.7.3

missMDA-package	Handling missing values with/in multivariate data analysis (principal component methods)
estim_ncpMCA	Estimate the number of dimensions for the Multiple Correspondence Analysis by cross-validation
estim_ncpPCA	Estimate the number of dimensions for the Principal Component Analysis by cross-validation
gene	Gene expression
geno	Genotype-environment data set with missing values
imputeFAMD	Impute mixed dataset
imputeMCA	Impute categorical dataset
imputeMFA	Impute dataset with variables structured into groups of variables (groups of continuous or categorical variables)
imputePCA	Impute dataset with PCA
MIPCA	Multiple Imputation with PCA
missMDA	Handling missing values with/in multivariate data analysis (principal component methods)
orange	Sensory description of 12 orange juices by 8 attributes.
ozone	Daily measurements of meteorological variables and ozone concentration
plot.MIPCA	Plot the graphs for the Multiple Imputation in PCA
snorena	Characterization of people who snore
vnf	Questionnaire done by 1232 individuals who answered 14 questions