doi:10.1556/ComEc.15.2014.2.6>. The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations.">

opticut: Likelihood Based Optimal Partitioning and Indicator Species Analysis (original) (raw)

Likelihood based optimal partitioning and indicator species analysis. Finding the best binary partition for each species based on model selection, with the possibility to take into account modifying/confounding variables as described in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>. The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations.

Version: 0.1-4
Depends: R (≥ 3.1.0), pbapply (≥ 1.3-0)
Imports: MASS, pscl, betareg, ResourceSelection (≥ 0.3-2), parallel, mefa4
Published: 2025-07-13
DOI: 10.32614/CRAN.package.opticut
Author: Peter Solymos ORCID iD [aut, cre], Ermias T. Azeria ORCID iD [ctb]
Maintainer: Peter Solymos
BugReports: https://github.com/psolymos/opticut/issues
License: GPL-2
URL: https://github.com/psolymos/opticut
NeedsCompilation: no
CRAN checks: opticut results

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