R package missMDA: Handling missing values with/in multivariate data analysis (principal component methods). Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA

References in zbMATH (referenced in 14 articles , 1 standard article )

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  1. Jiang, Wei; Josse, Julie; Lavielle, Marc; TraumaBase Group: Logistic regression with missing covariates -- parameter estimation, model selection and prediction within a joint-modeling framework (2020)
  2. Hadrien Lorenzo, Jérôme Saracco, Rodolphe Thiébaut: Supervised Learning for Multi-Block Incomplete Data (2019) arXiv
  3. Parrella, Maria Lucia; Albano, Giuseppina; La Rocca, Michele; Perna, Cira: Reconstructing missing data sequences in multivariate time series: an application to environmental data (2019)
  4. Borcard, Daniel; Gillet, François; Legendre, Pierre: Numerical ecology with R (2018)
  5. Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
  6. Solaro, N.; Barbiero, A.; Manzi, G.; Ferrari, P. A.: A simulation comparison of imputation methods for quantitative data in the presence of multiple data patterns (2018)
  7. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  8. Debón, A.; Chaves, L.; Haberman, S.; Villa, F.: Characterization of between-group inequality of longevity in European union countries (2017)
  9. Solaro, Nadia; Barbiero, Alessandro; Manzi, Giancarlo; Ferrari, Pier Alda: A sequential distance-based approach for imputing missing data: forward imputation (2017)
  10. Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016) not zbMATH
  11. Audigier, Vincent; Husson, François; Josse, Julie: A principal component method to impute missing values for mixed data (2016)
  12. Julie Josse; François Husson: missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016) not zbMATH
  13. Josse, Julie; Chavent, Marie; Liquet, Benot; Husson, François: Handling missing values with regularized iterative multiple correspondence analysis (2012)
  14. Josse, Julie; Pagès, Jérôme; Husson, François: Multiple imputation in principal component analysis (2011)