Likelihood Based Optimal Partitioning for Indicator Species Analysis

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Documentation for package ‘opticut’ version 0.1-0

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opticut-package Likelihood Based Optimal Partitioning for Indicator Species Analysis
allComb Finding all possible binary partitions
as.data.frame.opticut Likelihood based optimal partitioning for indicator species analysis
as.data.frame.summary.opticut Likelihood based optimal partitioning for indicator species analysis
as.data.frame.summary.uncertainty Quantifying uncertainty for fitted objects
as.data.frame.uncertainty Quantifying uncertainty for fitted objects
bestmodel Best model, partition, and MLE
bestmodel.opticut Likelihood based optimal partitioning for indicator species analysis
bestmodel.optilevels Optimal number of factor levels
bestpart Best model, partition, and MLE
bestpart.opticut Likelihood based optimal partitioning for indicator species analysis
bestpart.uncertainty Quantifying uncertainty for fitted objects
bestpart.uncertainty1 Quantifying uncertainty for fitted objects
beta2i Indicator values
bsmooth Quantifying uncertainty for fitted objects
bsmooth.uncertainty Quantifying uncertainty for fitted objects
bsmooth.uncertainty1 Quantifying uncertainty for fitted objects
checkComb Finding all possible binary partitions
check_strata Quantifying uncertainty for fitted objects
col2gray Color palettes for the opticut package
dolina Land snail data set
fix_levels Likelihood based optimal partitioning for indicator species analysis
getMLE Best model, partition, and MLE
getMLE.opticut Likelihood based optimal partitioning for indicator species analysis
kComb Finding all possible binary partitions
lorenz Lorenz curve bases thresholds and partitions
occolors Color palettes for the opticut package
oComb Ranking based binary partitions
ocoptions Options for the opticut package
opticut Likelihood based optimal partitioning for indicator species analysis
opticut.default Likelihood based optimal partitioning for indicator species analysis
opticut.formula Likelihood based optimal partitioning for indicator species analysis
opticut1 Likelihood based optimal partitioning for indicator species analysis
optilevels Optimal number of factor levels
plot.lorenz Lorenz curve bases thresholds and partitions
plot.opticut Likelihood based optimal partitioning for indicator species analysis
print.opticut Likelihood based optimal partitioning for indicator species analysis
print.opticut1 Likelihood based optimal partitioning for indicator species analysis
print.summary.lorenz Lorenz curve bases thresholds and partitions
print.summary.opticut Likelihood based optimal partitioning for indicator species analysis
print.summary.uncertainty Quantifying uncertainty for fitted objects
print.uncertainty Quantifying uncertainty for fitted objects
print.uncertainty1 Quantifying uncertainty for fitted objects
quantile.lorenz Lorenz curve bases thresholds and partitions
rankComb Ranking based binary partitions
sindex Weighted relative suitability index
strata Likelihood based optimal partitioning for indicator species analysis
strata.opticut Likelihood based optimal partitioning for indicator species analysis
strata.uncertainty Quantifying uncertainty for fitted objects
summary.lorenz Lorenz curve bases thresholds and partitions
summary.opticut Likelihood based optimal partitioning for indicator species analysis
summary.uncertainty Quantifying uncertainty for fitted objects
uncertainty Quantifying uncertainty for fitted objects
uncertainty.opticut Quantifying uncertainty for fitted objects
wplot Likelihood based optimal partitioning for indicator species analysis
wplot.opticut Likelihood based optimal partitioning for indicator species analysis
wplot.opticut1 Likelihood based optimal partitioning for indicator species analysis
wrsi Weighted relative suitability index