Title: Collection of Functions for Gaussian Quadrature

Title: Multi-Trait Dissimilarity with more Uniform Contributions
Authors@R: c(person("Francesco", "de Bello", role = "aut",
person("Zoltan", "Botta-Dukat", role = "aut"),
Description: R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. The approach is based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function.

Description: R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. de Bello et al. (2021) <doi:10.1111/2041-210X.13537> presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function.

Title: Gazepath Transforms Eye-Tracking Data into Fixations and
Maintainer: Daan van Renswoude <D.R.vanRenswoude@uva.nl>
Description: Eye-tracking data must be transformed into fixations and saccades before it can be analyzed. This package provides a non-parametric speed-based approach to do this on a trial basis. The method is especially useful when there are large differences in data quality, as the thresholds are adjusted accordingly. The same pre-processing procedure can be applied to all participants, while accounting for individual differences in data quality.

Title: Parse Eye-Tracking Data into Fixations
Author: Daan van Renswoude & Ingmar Visser
Imports: SDMTools, jpeg, zoo, scales, shiny

Description: A collection of tools for processing and classifying eye
Imports: rgeos, maptools, deldir, sp, modeest, signal, zoo, Rmisc,
'voronoi.R' 'chull_area.R' 'nni.R' 'bcea.R' 'gridded_rois.R'

Title: Generalize Lambda Distribution and Generalized Bootstrapping
Description: This package collects algorithms and functions for fitting data to a generalized lambda distribution via moment matching methods, and generalized bootstrapping.