Package: opticut 0.1-3

opticut: Likelihood Based Optimal Partitioning and Indicator Species Analysis

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.

Authors:Peter Solymos [cre, aut], Ermias T. Azeria [ctb]

opticut_0.1-3.tar.gz
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opticut_0.1-3.tgz(r-4.4-any)opticut_0.1-3.tgz(r-4.3-any)
opticut_0.1-3.tar.gz(r-4.5-noble)opticut_0.1-3.tar.gz(r-4.4-noble)
opticut_0.1-3.tgz(r-4.4-emscripten)opticut_0.1-3.tgz(r-4.3-emscripten)
opticut.pdf |opticut.html
opticut/json (API)

# Install 'opticut' in R:
install.packages('opticut', repos = c('https://psolymos.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/psolymos/opticut/issues

Datasets:

On CRAN:

ecologyindicator-species-analysislikelihoodoptimal-partitioningspecies

26 exports 2 stars 0.93 score 15 dependencies 82 scripts 211 downloads

Last updated 4 months agofrom:5e8e4fe774. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 19 2024
R-4.5-winOKAug 19 2024
R-4.5-linuxOKAug 19 2024
R-4.4-winOKAug 19 2024
R-4.4-macOKAug 19 2024
R-4.3-winOKAug 19 2024
R-4.3-macOKAug 19 2024

Exports:allCombbestmodelbestpartbeta2ibsmoothcheck_stratacheckCombcol2grayfix_levelsgetMLEiquantilekComblcplotlorenzmulticutmulticut1occolorsoCombocoptionsopticutopticut1optilevelsrankCombstratauncertaintywplot

Dependencies:betaregflexmixFormulalatticelmtestMASSMatrixmefa4modeltoolsnnetpbapplypsclResourceSelectionsandwichzoo

Readme and manuals

Help Manual

Help pageTopics
Likelihood Based Optimal Partitioning and Indicator Species Analysisopticut-package
Finding All Possible Binary PartitionsallComb checkComb kComb
Best model, Partition, and MLEbestmodel bestpart getMLE
Scaling for the Indicator Potentialbeta2i
Bird Species Detectionsbirdrec
Land Snail Data Setdolina
Lorenz Curve Based Thresholds and Partitionsiquantile iquantile.lorenz lorenz plot.lorenz print.summary.lorenz quantile.lorenz summary.lorenz
Multi-level Response Modelas.data.frame.multicut as.data.frame.summary.multicut bestmodel.multicut bestpart.multicut fitted.multicut getMLE.multicut lcplot lcplot.multicut1 multicut multicut.default multicut.formula multicut1 plot.multicut plot.multicut1 predict.multicut print.multicut print.multicut1 print.summary.multicut strata.multicut subset.multicut summary.multicut
Color Palettes for the opticut Packagecol2gray occolors
Options for the opticut Packageocoptions
Optimal Binary Response Modelas.data.frame.opticut as.data.frame.summary.opticut bestmodel.opticut bestpart.opticut fitted.opticut fix_levels getMLE.opticut opticut opticut.default opticut.formula opticut1 plot.opticut predict.opticut print.opticut print.opticut1 print.summary.opticut strata strata.opticut subset.opticut summary.opticut wplot wplot.opticut wplot.opticut1
Optimal Number of Factor Levelsbestmodel.optilevels optilevels
Ranking Based Binary PartitionsoComb rankComb
Quantifying Uncertainty for Fitted Objectsas.data.frame.summary.uncertainty as.data.frame.uncertainty bestpart.uncertainty bestpart.uncertainty1 bsmooth bsmooth.uncertainty bsmooth.uncertainty1 check_strata print.summary.uncertainty print.uncertainty print.uncertainty1 strata.uncertainty subset.uncertainty summary.uncertainty uncertainty uncertainty.multicut uncertainty.opticut
Warblers Data Setwarblers