Imports: graphics, grDevices, stats, methods

Imports: graphics, grDevices, stats, methods, terra

Description: Analyses species distribution models and evaluates their performance. It includes functions for performing variation partitioning, calculating several measures of model discrimination and calibration, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots. Initially described in Barbosa et al. (2013) <doi:10.1111/ddi.12100>.

Title: Community Structure Detection via Modularity Maximization
Description: The algorithms implemented here are used to detect the community structure of a network.
These algorithms follow different approaches, but are all based on the concept of modularity maximization.

Title: Multiple-Output Directional Quantile Regression
Copyright: The Institute of Information Theory and Automation of the
Czech Academy of Sciences Pod Vodárenskou věží 4, CZ-182 08
Prague 8, Czech Republic email: utia@utia.cas.cz, phone: +420
Description: Contains basic tools for performing
multiple-output quantile regression and computing
regression quantile contours by means of directional
regression quantiles. In the location case, one can thus
obtain halfspace depth contours in two to six dimensions.
Classification/MSC: 62H05, 62J99, 62G08, 65C60
Keywords: quantile regression, multivariate regression, semiparametric
regression, nonparametric regression, data depth, regression
depth, halfspace depth, Tukey depth, depth contour,
multivariate quantile, regression quantile, quantile-based
Collate: addItem.R findItem.R delItem.R addRow.R findRow.R checkArray.R

Author: Miroslav Šiman [aut], Pavel Boček [aut, cre]
Czech Academy of Sciences, Pod Vodárenskou věží 4, CZ-18208
Prague 8, Czech Republic, email: utia@utia.cas.cz, phone: +420

Czech Academy of Sciences, Pod Vodarenskou vezi 4, CZ-18208
Hallin, M., Paindaveine, D. and Siman, M. (2010)
Multivariate quantiles and multiple-output regression quantiles:
from L1 optimization to halfspace depth. Annals of Statistics 38, 635-669
For more references about the method, see Help pages.