## missMDA

missMDA is a R package that allows one to:

How to use missMDA to apply a statistical method on an incomplete data set ?

- handle missing values in exploratory multivariate analysis such as principal component analysis (PCA), multiple correspondence analysis (MCA), factor analysis for mixed data (FAMD) and multiple factor analysis (MFA)
- impute missing values in:
- continuous data sets using the PCA model
- categorical data sets using MCA
- mixed data using FAMD

- generate multiple imputed data sets:
- for continuous data using the PCA model
- for categorical data using MCA

- visualize multiple imputation in PCA

How to use missMDA to apply a statistical method on an incomplete data set ?

## micemd

micemd is a R package dedicated to multiple imputation with two-level data.

Why using micemd?

Statistical analysis often requires allowance for a multilevel structure. For example, a two-level structure occurs when individual data from several studies are aggregated, as in individual participant data (IPD) meta-analysis: individuals are at the lowest level, and the studies at the higher level.

However, variables of each study are often incomplete (sporadically missing) and often differ between studies (leading to systematically missing variables), making challenging to analyse such data. micemd offers several solutions to overcome such issues.

micemd is an ad-don for the mice R package which performs multiple imputation using chained equations. Its additional functionnalities consist of:

imputation methods dedicated to:

tools for multiple imputation with mice:

CRAN Task View on missing data

All R packages dealing with missing data can be found here !

Why using micemd?

Statistical analysis often requires allowance for a multilevel structure. For example, a two-level structure occurs when individual data from several studies are aggregated, as in individual participant data (IPD) meta-analysis: individuals are at the lowest level, and the studies at the higher level.

However, variables of each study are often incomplete (sporadically missing) and often differ between studies (leading to systematically missing variables), making challenging to analyse such data. micemd offers several solutions to overcome such issues.

micemd is an ad-don for the mice R package which performs multiple imputation using chained equations. Its additional functionnalities consist of:

imputation methods dedicated to:

- sporadically and systematically missing values
- continuous, binary or count variables

tools for multiple imputation with mice:

- parallel calculation
- choice of the number of imputed tables
- overimputation for model checking

CRAN Task View on missing data

All R packages dealing with missing data can be found here !