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).

Fit and simulate mixtures of von Mises-Fisher distributions.

Multidimensional projection techniques are used to create two
    dimensional representations of multidimensional data sets.

An 'R' implementation of the 'python' program Metabolomics Peak Analysis Computational Tool ('MPACT') (Robert M. Samples, Sara P. Puckett, and Marcy J. Balunas (2023) <doi:10.1021/acs.analchem.2c04632>). Filters in the package serve to address common errors in tandem mass spectrometry preprocessing, including: (1) isotopic patterns that are incorrectly split during preprocessing, (2) features present in solvent blanks due to carryover between samples, (3) features whose abundance is greater than user-defined abundance threshold in a specific group of samples, for example media blanks, (4) ions that are inconsistent between technical replicates, and (5) in-source fragment ions created during ionization before fragmentation in the tandem mass spectrometry workflow.