Likelihood Based Optimal Partitioning and Indicator Species
Analysis
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.