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opticut  

Likelihood Based Optimal Partitioning and Indicator Species Analysis
View on CRAN: Click here


Download and install opticut package within the R console
Install from CRAN:
install.packages("opticut")

Install from Github:
library("remotes")
install_github("cran/opticut")

Install by package version:
library("remotes")
install_version("opticut", "0.1-4")



Attach the package and use:
library("opticut")
Maintained by
Peter Solymos
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-12-17
Latest Update: 2025-07-13
Description:
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) . The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations.
How to cite:
Peter Solymos (2016). opticut: Likelihood Based Optimal Partitioning and Indicator Species Analysis. R package version 0.1-4, https://cran.r-project.org/web/packages/opticut. Accessed 07 Mar. 2026.
Previous versions and publish date:
0.1-0 (2016-12-17 10:48), 0.1-1 (2018-01-31 06:41), 0.1-2 (2018-02-01 17:10), 0.1-3 (2024-05-22 00:00)
Other packages that cited opticut R package
View opticut citation profile
Other R packages that opticut depends, imports, suggests or enhances
Complete documentation for opticut
Functions, R codes and Examples using the opticut R package
Some associated functions: allComb . bestmodel . beta2i . birdrec . dolina . lorenz . multicut . occolors . ocoptions . opticut-package . opticut . optilevels . rankComb . uncertainty . 
Some associated R codes: allComb.R . as.data.frame.multicut.R . as.data.frame.opticut.R . as.data.frame.summary.multicut.R . as.data.frame.summary.opticut.R . as.data.frame.summary.uncertainty.R . as.data.frame.uncertainty.R . bestmodel.R . bestmodel.multicut.R . bestmodel.opticut.R . bestmodel.optilevels.R . bestpart.R . bestpart.multicut.R . bestpart.opticut.R . bestpart.uncertainty.R . bestpart.uncertainty1.R . beta2i.R . bsmooth.R . bsmooth.uncertainty.R . bsmooth.uncertainty1.R . checkComb.R . check_strata.R . col2gray.R . extractOpticutdot.R . fitted.multicut.R . fitted.opticut.R . fix_levels.R . getMLE.R . getMLE.multicut.R . getMLE.opticut.R . get_linkinvdot.R . iquantile.R . iquantile.lorenz.R . kComb.R . lc_cutdot.R . lcplot.R . lcplot.multicut1.R . lorenz.R . multicut.R . multicut.default.R . multicut.formula.R . multicut1.R . oComb.R . occolors.R . ocoptions.R . opticut.R . opticut.default.R . opticut.formula.R . opticut1.R . opticut1dot.R . opticut_distdot.R . optilevels.R . optilevelsdot.R . parseAssocdot.R . plot.lorenz.R . plot.multicut.R . plot.multicut1.R . plot.opticut.R . predict.multicut.R . predict.opticut.R . predict_distdot.R . print.multicut.R . print.multicut1.R . print.opticut.R . print.opticut1.R . print.summary.lorenz.R . print.summary.multicut.R . print.summary.opticut.R . print.summary.uncertainty.R . print.uncertainty.R . print.uncertainty1.R . quantile.lorenz.R . rankComb.R . strata.R . strata.multicut.R . strata.opticut.R . strata.uncertainty.R . subset.multicut.R . subset.opticut.R . subset.uncertainty.R . summary.lorenz.R . summary.multicut.R . summary.opticut.R . summary.uncertainty.R . summary_opticutdot.R . summary_uncertaintydot.R . uncertainty.R . uncertainty.multicut.R . uncertainty.opticut.R . uncertaintyMulticut1dot.R . uncertaintyOpticut1dot.R . wplot.R . wplot.opticut.R . wplot.opticut1.R . zzz.R .  Full opticut package functions and examples
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