(with Husson, F. & Lê, S.) The FactoMineR package is dedicated to principal components methods to explore, sum-up and visualize data. Dimensionality reduction methods include PCA, correspondence analysis (CA) for count data such as documents-words data, multiple correspondence analysis (MCA) for categorical data, factorial analysis of mixed data (FAMD) for both types of variables as well as methods for groups of variables, of individuals (multiple factorial analysis, MFA), for hierarchy …
(with Husson, F.) The missMDA package is dedicated to missing values in and with Multivariate Data Analysis. It allows one to apply PCA, MCA, FAMD and MFA on incomplete data. It performs single and multiple imputation for continuous, categorical and mixed data based on principal components methods.
The denoiseR package is dedicated to estimate a low rank matrix from noisy data. Different regularized versions of the singular values decomposition (SVD) are implemented: soft-thresholding of the singular values, nonlinear transformation, adaptive shrinkage, etc. Iterative Stable Autoencoder is also implemented and is a new way to denoise count data.