Application of Variational Mode Decomposition based different Machine Learning models for univariate time series forecasting. For method details see (i) K. Dragomiretskiy and D. Zosso (2014) <doi:10.1109/TSP.2013.2288675>; (ii)  Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.

Forecasting univariate time series with Variational Mode Decomposition (VMD) based time delay neural network models.For method details see Konstantin, D.and Dominique, Z. (2014). <doi:10.1109/TSP.2013.2288675>.

A variety of tools to allow the quantification of videos of the 
    lymphatic vasculature taken under an operating microscope. Lymphatic vessels
    that have been injected with a variety of blue dyes can be tracked throughout
    the video to determine their width over time. Code is optimised
    for efficient processing of multiple large video files. Functions to calculate
    physiologically relevant parameters and generate graphs from these values 
    are also included.

Provides fast sampling from von Mises-Fisher distribution using the method proposed by Andrew T.A Wood (1994) <doi:10.1080/03610919408813161>.

Manage, provision and use Virtual Machines pre-configured for R.
             Develop, test and build package in a clean environment.
             'Vagrant' tool and a provider (such as 'Virtualbox') have to be installed.

An implementation of the variable neighborhood trust region 
    algorithm Bierlaire et al. (2009) "A Heuristic for Nonlinear Global 
    Optimization" <doi:10.1287/ijoc.1090.0343>.

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>.