An algorithm for nonlinear global optimization based on the 
    variable neighbourhood trust region search (VNTRS) algorithm proposed by 
    Bierlaire et al. (2009) "A Heuristic for Nonlinear Global Optimization" 
    <doi:10.1287/ijoc.1090.0343>. The algorithm combines variable neighbourhood 
    exploration with a trust-region framework to efficiently search the solution 
    space. It can terminate a local search early if the iterates are converging 
    toward a previously visited local optimum or if further improvement within 
    the current region is unlikely. In addition to global optimization, the 
    algorithm can also be applied to identify multiple local optima.

Create adjacency matrices of vocalisation graphs from
  dataframes containing sequences of speech and silence intervals,
  transforming these matrices into Markov diagrams, and generating
  datasets for classification of these diagrams by 'flattening' them
  and adding global properties (functionals) etc.  Vocalisation
  diagrams date back to early work in psychiatry (Jaffe and Feldstein,
  1970) and social psychology (Dabbs and Ruback, 1987) but have only
  recently been employed as a data representation method for machine
  learning tasks including meeting segmentation (Luz, 2012)
  <doi:10.1145/2328967.2328970> and classification (Luz,
  2013) <doi:10.1145/2522848.2533788>.

Methods to calculate the expected value of information from a decision-analytic model.  This includes the expected value of perfect information (EVPI), partial perfect information (EVPPI) and sample information (EVSI), and the expected net benefit of sampling (ENBS).  A range of alternative computational methods are provided under the same user interface.  See Heath et al. (2024) <doi:10.1201/9781003156109>, Jackson et al. (2022) <doi:10.1146/annurev-statistics-040120-010730>.

Provides tools for audio data analysis, including feature extraction, pitch detection, and speaker identification. Designed for voice research and signal processing applications.