Provides the facility to calculate non-isotropic accumulated cost surface, least-cost paths, least-cost corridors, least-cost networks using a number of human-movement-related cost functions that can be selected by the user. It just requires a Digital Terrain Model, a start location and (optionally) destination locations. See Alberti (2019) <doi:10.1016/j.softx.2019.100331>.

Offers an easy and automated way to scale up individual-level space use analysis to 
    that of groups. Contains a function from the 'move' package to calculate a dynamic Brownian 
    bridge movement model 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:
  - 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 curve);
  - transforming the marker by a function either defined by the user or resulting from a logistic 
  regression model (hROC curve);
  - considering a linear transformation with some fixed parameters introduced by the user, 
  dynamic parameters or empirically maximizing TPR for each FPR for a bivariate marker.
  Also a quadratic transformation with particular coefficients or a function fitted by a logistic 
  regression model can be considered (biROC curve);
  - considering a linear transformation with some fixed parameters introduced by the user, 
  dynamic parameters or a function fitted by a logistic regression model (multiROC curve).
  The classification regions behind each point of the ROC curve are displayed in both fixed 
  graphics (plot.buildROC(), plot.regions() or plot.funregions() function) or videos (movieROC() 
  function).