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.

Variational Autoencoded Multivariate Spatial Fay-Herriot models are designed to efficiently estimate population parameters in small area estimation. This package implements the variational generalized multivariate spatial Fay-Herriot model (VGMSFH) using 'NumPyro' and 'PyTorch' backends, as demonstrated by Wang, Parker, and Holan (2025) <doi:10.48550/arXiv.2503.14710>. The 'vmsae' package provides utility functions to load weights of the pretrained variational autoencoders (VAEs) as well as tools to train custom VAEs tailored to users specific applications.

Data version management on the file system for smaller projects.
    Manage data pipeline outputs with symbolic folder links, structured logging
    and reports, using 'R6' classes for encapsulation and 'data.table' for
    speed. Directory-specific logs used as source of truth to allow portability
    of versioned data folders.