robCompositions: Robust Estimation for Compositional Data. The package includes methods for imputation of compositional data including robust methods, methods to impute rounded zeros, (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.

References in zbMATH (referenced in 17 articles )

Showing results 1 to 17 of 17.
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  1. de Sousa, J.; Hron, K.; Fačevicová, K.; Filzmoser, P.: Robust principal component analysis for compositional tables (2021)
  2. Hron, Karel; Engle, Mark; Filzmoser, Peter; Fišerová, Eva: Weighted symmetric pivot coordinates for compositional data with geochemical applications (2021)
  3. Štefelová, Nikola; Alfons, Andreas; Palarea-Albaladejo, Javier; Filzmoser, Peter; Hron, Karel: Robust regression with compositional covariates including cellwise outliers (2021)
  4. Filzmoser, Peter; Gregorich, Mariella: Multivariate outlier detection in applied data analysis: global, local, compositional and cellwise outliers (2020)
  5. Templ, M.; Gussenbauer, J.; Filzmoser, P.: Evaluation of robust outlier detection methods for zero-inflated complex data (2020)
  6. Boonen, Tim J.; Guillen, Montserrat; Santolino, Miguel: Forecasting compositional risk allocations (2019)
  7. Morais, Joanna; Thomas-Agnan, Christine; Simioni, Michel: Using compositional and Dirichlet models for market share regression (2018)
  8. Todorov, Valentin: Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” (2018)
  9. Tsagris, Michail; Stewart, Connie: A Dirichlet regression model for compositional data with zeros (2018)
  10. Xia, Yinglin; Sun, Jun; Chen, Ding-Geng: Statistical analysis of microbiome data with R (2018)
  11. Giordan, M.; Vaggi, F.; Wehrens, R.: On the maximization of likelihoods belonging to the exponential family using a Levenberg-Marquardt approach (2017)
  12. Templ, Matthias: Statistical disclosure control for microdata. Methods and applications in R (2017)
  13. Templ, M.; Hron, K.; Filzmoser, P.: Exploratory tools for outlier detection in compositional data with structural zeros (2017)
  14. Den Boogaart, Karl Gerald van; Tolosana-Delgado, Raimon; Templ, Matthias: Regression with compositional response having unobserved components or below detection limit values (2015)
  15. Martín-Fernández, Josep-Antoni; Hron, Karel; Templ, Matthias; Filzmoser, Peter; Palarea-Albaladejo, Javier: Bayesian-multiplicative treatment of count zeros in compositional data sets (2015)
  16. Ros-Freixedes, Roger; Estany, Joan: On the compositional analysis of fatty acids in pork (2014)
  17. Andreas Alfons; Matthias Templ; Peter Filzmoser: An Object-Oriented Framework for Statistical Simulation: The R Package simFrame (2010) not zbMATH