Provides an implementation of the Virtual Noise algorithm
 for D-optimal experimental designs under correlated observations. The
 package supports flexible covariance structures, multi-dimensional
 candidate sets, and analytical or numerical computation of regression
 gradients. It offers a unified framework for constructing design matrices,
 defining covariance models, and computing optimal design measures.

Implements the variable neighborhood trust region search (VNTRS)
    algorithm for nonlinear global optimization, following Bierlaire et al.
    (2009) "A Heuristic for Nonlinear Global Optimization"
    <doi:10.1287/ijoc.1090.0343>. The method combines neighborhood
    exploration with a trust-region framework to search the solution space
    efficiently. It can terminate a local search early when the iterates
    converge toward a previously visited local optimum or when further
    improvement within the current region is unlikely. The algorithm can also
    be used 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>.