| FactoMineR: an R package for multivariate analysis S Lê, J Josse, F Husson Journal of statistical software 25, 1-18, 2008 | 6218 | 2008 |
| FactoMineR: multivariate exploratory data analysis and data mining with R F Husson, J Josse, S Le, J Mazet R package version 1 (1.29), 2013 | 756* | 2013 |
| missMDA: a package for handling missing values in multivariate data analysis J Josse, F Husson Journal of Statistical Software 70, 1-31, 2016 | 595 | 2016 |
| Principal component methods-hierarchical clustering-partitional clustering: why would we need to choose for visualizing data F Husson, J Josse, J Pages Applied Mathematics Department 17, 2010 | 470 | 2010 |
| Handling missing values in exploratory multivariate data analysis methods J Josse, F Husson Journal de la Société Française de Statistique 153 (2), 79-99, 2012 | 255 | 2012 |
| FactoMineR: Multivariate exploratory data analysis and data mining with R. R package version 1.26 F Husson, J Josse, S Le, J Mazet Available at h ttp://CRAN. R-project. org/p ackage= FactoMineR, 2014 | 248 | 2014 |
| Testing the significance of the RV coefficient J Josse, J Pagès, F Husson Computational Statistics & Data Analysis 53 (1), 82-91, 2008 | 244 | 2008 |
| Selecting the number of components in principal component analysis using cross-validation approximations J Josse, F Husson Computational Statistics & Data Analysis 56 (6), 1869-1879, 2012 | 214 | 2012 |
| Principal component analysis with missing values: a comparative survey of methods S Dray, J Josse Plant Ecology 216 (5), 657-667, 2015 | 168 | 2015 |
| Package ‘factominer’ F Husson, J Josse, S Le, J Mazet, MF Husson An R package 96, 698, 2016 | 148 | 2016 |
| A principal component method to impute missing values for mixed data V Audigier, F Husson, J Josse Advances in Data Analysis and Classification 10 (1), 5-26, 2016 | 123 | 2016 |
| FactoMineR: an R package for multivariate analysis F Husson, J Josse, S Lê, J Mazet Journal of statistical software 25 (1), 1-18, 2008 | 120 | 2008 |
| Handling missing values with regularized iterative multiple correspondence analysis J Josse, M Chavent, B Liquet, F Husson Journal of classification 29 (1), 91-116, 2012 | 118 | 2012 |
| Multiple imputation in principal component analysis J Josse, F Husson Advances in data analysis and classification 5 (3), 231-246, 2011 | 118 | 2011 |
| Statistiques avec R PA Cornillon, F Husson, N Jégou, E Matzner-Lober, J Josse, A Guyader, ... Lectures, Les livres, 2012 | 107 | 2012 |
| Multiple correspondence analysis F Husson, J Josse Visualization and verbalization of data, 165-184, 2014 | 82 | 2014 |
| Measuring multivariate association and beyond J Josse, S Holmes Statistics surveys 10, 132, 2016 | 72 | 2016 |
| Multivariate exploratory data analysis and data mining F Husson, J Josse, S Le, J Mazet R package, 2016 | 60 | 2016 |
| On the consistency of supervised learning with missing values J Josse, N Prost, E Scornet, G Varoquaux arXiv preprint arXiv:1902.06931, 2019 | 59 | 2019 |
| MIMCA: multiple imputation for categorical variables with multiple correspondence analysis V Audigier, F Husson, J Josse Statistics and computing 27 (2), 501-518, 2017 | 47 | 2017 |
François HussonProfessor in StatisticsAdresse e-mail validée de agrocampus-ouest.fr
