Offers an easy and automated way to scale up individual-level space use analysis to 
    that of groups. Contains functions from the 'move' package to calculate either a dynamic Brownian 
    bridge movement model or dynamic Bivariate Gaussian bridges from movement data for individual animals, as well as functions to 
    visualize and quantify space use for individuals aggregated in groups. Originally written with 
    passive acoustic telemetry in mind, this package also provides functionality to account for
    unbalanced acoustic receiver array designs, and satellite tag data.

Provides tools for animal movement modelling using hidden Markov
    models. These include processing of tracking data, fitting hidden Markov models
    to movement data, visualization of data and fitted model, decoding of the state
    process, etc. <doi:10.1111/2041-210X.12578>.

Analysis and visualisation of synchrony, interaction, and joint
    movements from audio and video movement data of a group of music performers. The demo is data described in Clayton, Leante, and 
    Tarsitani (2021) <doi:10.17605/OSF.IO/KS325>, while example analyses 
    can be found in Clayton, Jakubowski, and Eerola (2019) 
    <doi:10.1177/1029864919844809>. Additionally, wavelet analysis 
    techniques have been applied to examine movement-related 
    musical interactions, as shown in Eerola et al. (2018)
    <doi:10.1098/rsos.171520>.

Estimating wind speed from trajectories of individually tracked birds using a maximum likelihood approach.

Visualization of decision rules for binary classification and Receiver Operating Characteristic (ROC) curve estimation under different generalizations proposed in the literature:
  - making the classification subsets flexible to cover those scenarios where both extremes of the
  marker are associated with a higher risk of being positive, considering two thresholds 
  (gROC() function);
  - transforming the marker by a proper function trying to improve the classification performance 
  (hROC() function);
  - when dealing with multivariate markers, considering a proper transformation to univariate space 
  trying to maximize the resulting AUC of the TPR for each FPR (multiROC() function).
  The classification regions behind each point of the ROC curve are displayed in both static 
  graphics (plot_buildROC(), plot_regions() or plot_funregions() function) or 
  videos (movieROC() function).