The article introduces the R package *missMDA*, which is designed to perform principal component methods on incomplete datasets, aiming to estimate parameters and obtain graphical representations despite missing values. The package includes methods for principal component analysis (PCA), multiple correspondence analysis (MCA), factorial analysis (FAMD) for mixed data, and multiple factor analysis (MFA). It also supports single and multiple imputation for continuous, categorical, and mixed variables. The core of these methods is the singular value decomposition (SVD), and the package provides a comprehensive framework for handling missing data, including confidence areas around graphical outputs to assess the credibility of results. The article discusses the methodology, implementation, and applications of *missMDA*, highlighting its advantages over other software packages and existing methods. It also provides detailed examples and code snippets to illustrate how to use the package for various types of data and analyses.The article introduces the R package *missMDA*, which is designed to perform principal component methods on incomplete datasets, aiming to estimate parameters and obtain graphical representations despite missing values. The package includes methods for principal component analysis (PCA), multiple correspondence analysis (MCA), factorial analysis (FAMD) for mixed data, and multiple factor analysis (MFA). It also supports single and multiple imputation for continuous, categorical, and mixed variables. The core of these methods is the singular value decomposition (SVD), and the package provides a comprehensive framework for handling missing data, including confidence areas around graphical outputs to assess the credibility of results. The article discusses the methodology, implementation, and applications of *missMDA*, highlighting its advantages over other software packages and existing methods. It also provides detailed examples and code snippets to illustrate how to use the package for various types of data and analyses.