Implementing a Class of Permutation Tests: The coin Package

By Torsten Hothorn, Kurt Hornik, M. V. D. Wiel, Achim Zeileis. 2008

1,472 citations recorded by the source corpus.

The R package coin implements a unified approach to permutation tests providing a huge class of independence tests for nominal, ordered, numeric, and censored data as well as multivariate data at mixed scales. Based on a rich and flexible conceptual framework that embeds different permutation test procedures into a common theory, a computational framework is established in coin that likewise embeds the corresponding R functionality in a common S4 class structure with associated generic functions. As a consequence, the computational tools in coin inherit the flexibility of the underlying theory and conditional inference functions for important special cases can be set up easily. Conditional versions of classical tests---such as tests for location and scale problems in two or more samples, independence in two- or three-way contingency tables, or association problems for censored, ordered categorical or multivariate data---can easily be implemented as special cases using this computational toolbox by choosing appropriate transformations of the observations. The paper gives a detailed exposition of both the internal structure of the package and the provided user interfaces along with examples on how to extend the implemented functionality.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e4d91d60cc7b3c0a9c646a477495cd4e9d3e4910

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Citations in the corpus

Metagenomic biomarker discovery and explanation (2011, methodology): “LEfSe is implemented in Python and makes use of R statistical functions in the coin [111] and MASS [112] libraries through the rpy2 library [113] and of the matplotlib [114] library for graphical output.”
Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R package (2010, neutral): “These results agree with Huang et al. (2008) within the error to be expected from such an imputation method (Huang et al.”
Plant species diversity in alien black locust stands: a paired comparison with native stands across a north-Mediterranean range expansion. (2012, methodology): “The one-way permutation test was performed using the ‘coin’ package (Torsten et al., 2008).”
Regeneration of sandy old-fields in the forest steppe region of Hungary (2011, methodology): “Differences in species richness and cover of species groups among habitats were examined using Kruskal-Wallis test and Dunn post hoc tests performed in R statistical environment (R Development Core Team 2010) using the ‘‘coin’’ package (Hothorn et al. 2008).”
The Musicality of Non-Musicians: An Index for Assessing Musical Sophistication in the General Population (2014, methodology): “As a second analysis step we used conditional inference significance tests, implemented in the R package coin [135] and based on permutation statistics [136], as post-hoc tests to identify the categories within these variables for which significant main effects could be observed.”
Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data (2010, methodology): “Therefore, conditional inference tests (Hothorn et al. 2008) are used to determine the most relevant predictor variable and its splitting point in the nodes of conditional inference trees.”
An analysis of parameter adaptation in reactive tabu search (2014, methodology): “The test is implemented in the R coin package (Hothorn et al., 2008).”
Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests (2015, positive): “…a powerful, permutation-based approach to test the null hypothesis that two variables (measured on arbitrary scales) are independent of each other (Hothorn et al., 2008), using the package coin (Hothorn et al., 2013) within the R language and environment for statistical computing (R Development…”