robCompositions: An R‐package for Robust Statistical Analysis of Compositional Data
By Matthias Templ, Karel Hron, Peter Filzmoser. 2011
368 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Environmental Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/8ee18a57ee4a0e0b792fd9aa9608b4c6b58fbb73
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Matthias Templ
- Karel Hron
- Peter Filzmoser
- J. Palarea‐Albaladejo
- Pieter Vermeesch
- Josep-Antoni Martín-Fernández
- Eduardo Garzanti
- Alberto Resentini
In Environmental Engineering
- Pieter Vermeesch
- Karel Hron
- Matthias Templ
- Josep-Antoni Martín-Fernández
- Peter Filzmoser
- J. Palarea‐Albaladejo
- Eduardo Garzanti
- Alberto Resentini
In Statistics and Probability
- Matthias Templ
- Karel Hron
- Peter Filzmoser
- J. Palarea‐Albaladejo
- Josep-Antoni Martín-Fernández
- Christine Thomas-Agnan
- Pieter Vermeesch
- Eduardo Garzanti
Citations in the corpus
- An R package for statistical provenance analysis (2016, methodology): “The user is referred to other R packages such as compositions (van den Boogaart and Tolosana-Delgado, 2008) and robCompositions (Templ et al., 2011) for a more comprehensive toolset.”
- Bayesian-multiplicative treatment of count zeros in compositional data sets (2015, methodology): “A version of this procedure is currently implemented in the function impRZilr in the library robCompositions (Templ et al., 2011) of the statistical software R.”
- Using compositional and Dirichlet models for market share regression (2018, methodology): “Implementation in R: the packages ‘compositions’ [24] and ‘robCompositions’ [23] allow to transform compositional data, to fit the compositional model by OLS on the coordinates and to back transform the results into compositions.”