Provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters.
amap, dbscan, cowplot, fastICA, fpc, ggrepel, grid,
grDevices, gtable, knitr, parallel, plyr, reshape2, stats,
testthat, utils

Likelihood Based Optimal Partitioning for Indicator Species
Analysis
Peter Solymos [cre, aut], Ermias T. Azeria [ctb]
Likelihood based optimal partitioning for indicator
species analysis. Finding the best binary partition for each species
based on model selection, possibly controlling for modifying/confounding
variables as described in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>.
The package also implements various measures of uncertainty based on
binary partitions, optimal multinomial partitioning, and exploratory
suitability indices, with native support for parallel computations.
MASS, pscl, betareg, ResourceSelection (>= 0.3-0), parallel,
mefa4

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.
MASS, pscl, betareg, ResourceSelection (>= 0.3-2), parallel,
mefa4

A single function 'options.ifunset(...)' is contained herewith, which allows the user to set a global option ONLY if it is not already set. By this token, for package maintainers this function can be used in preference to the standard 'options(...)' function, making provision for THEIR end user to place 'options(...)' directives within their '.Rprofile' file, which will not be overridden at the point when a package is loaded.