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| missMDA-package | Handling missing values with/in multivariate data analysis (principal component methods) |
| estim_ncpFAMD | Estimate the number of dimensions for the Factorial Analysis of Mixed Data by cross-validation |
| 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 |
| imputeCA | Impute contingency table |
| 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 |
| MIMCA | Multiple Imputation with MCA |
| 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. |
| Overimpute | Overimputation diagnostic plot |
| ozone | Daily measurements of meteorological variables and ozone concentration |
| plot.MIMCA | Plot the graphs for the Multiple Imputation in MCA |
| plot.MIPCA | Plot the graphs for the Multiple Imputation in PCA |
| prelim | Converts a dataset imputed by MIMCA or MIPCA into a mids object |
| snorena | Characterization of people who snore |
| TitanicNA | Categorical data set with missing values: Survival of passengers on the Titanic |
| vnf | Questionnaire done by 1232 individuals who answered 14 questions |