Référentiel des packages R installés sur la plateforme Migale
Documentation : https://cran.r-project.org/web/packages/abcrf/index.html
anMC : Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors.
Wrapper Algorithm for All Relevant Feature Selection An all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies.
Documentation : https://cran.r-project.org/web/packages/Boruta/Boruta.pdf
C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0).
package permettant d'avoir accès à des distributions circulaires, tels que des "wrapped normal distributions"
Documentation : https://cran.r-project.org/src/contrib/CircStats_0.2-6.tar.gz
package permettant de circularisé des mesures par de la trigonométrie
Documentation : https://cran.r-project.org/web/packages/circular/circular.pdf
Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006)
. This method proposes to stratify sampling
in presence of ancillary data.
Documentation : https://cran.r-project.org/web/packages/clhs/clhs.pdf
An R package that provides a suite of tools to evaluate clustering algorithms, clusterings, and individual clusters.
Computes conditional multivariate normal probabilities, random deviates and densities
This R package applies the probabilistic model of species co-occurrence (Veech 2013) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.