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:
opticut_0.1-3.tar.gz
opticut_0.1-3.zip(r-4.5)opticut_0.1-3.zip(r-4.4)opticut_0.1-3.zip(r-4.3)
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')) |
Bug tracker:https://github.com/psolymos/opticut/issues
ecologyindicator-species-analysislikelihoodoptimal-partitioningspecies
Last updated 6 months agofrom:5e8e4fe774. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 17 2024 |
| R-4.5-win | OK | Nov 17 2024 |
| R-4.5-linux | OK | Nov 17 2024 |
| R-4.4-win | OK | Nov 17 2024 |
| R-4.4-mac | OK | Nov 17 2024 |
| R-4.3-win | OK | Nov 17 2024 |
| R-4.3-mac | OK | Nov 17 2024 |
Exports:allCombbestmodelbestpartbeta2ibsmoothcheck_stratacheckCombcol2grayfix_levelsgetMLEiquantilekComblcplotlorenzmulticutmulticut1occolorsoCombocoptionsopticutopticut1optilevelsrankCombstratauncertaintywplot
Dependencies:betaregflexmixFormulalatticelmtestMASSMatrixmefa4modeltoolsnnetpbapplypsclResourceSelectionsandwichzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Likelihood Based Optimal Partitioning and Indicator Species Analysis | opticut-package |
| Finding All Possible Binary Partitions | allComb checkComb kComb |
| Best model, Partition, and MLE | bestmodel bestpart getMLE |
| Scaling for the Indicator Potential | beta2i |
| Bird Species Detections | birdrec |
| Land Snail Data Set | dolina |
| Lorenz Curve Based Thresholds and Partitions | iquantile iquantile.lorenz lorenz plot.lorenz print.summary.lorenz quantile.lorenz summary.lorenz |
| Multi-level Response Model | as.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 Package | col2gray occolors |
| Options for the opticut Package | ocoptions |
| Optimal Binary Response Model | as.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 Levels | bestmodel.optilevels optilevels |
| Ranking Based Binary Partitions | oComb rankComb |
| Quantifying Uncertainty for Fitted Objects | as.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 Set | warblers |
