Provides a higher-level interface to the 'torch' package for defining,
    training, and fine-tuning neural networks, including its depth, powered by code generation. 
    This package currently supports few to several architectures, namely feedforward (multi-layer perceptron) 
    and recurrent neural networks (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)), 
    while also reduces boilerplate 'torch' code while enabling seamless integration with 'torch'. The model methods 
    to train neural networks from this package also bridges to titanic ML frameworks in R, namely
    'tidymodels' ecosystem, which enables the 'parsnip' model specifications, workflows, recipes, 
    and tuning tools.

Allows analyzing time series representing two-dimensional movements.
    It accepts a data frame with a time (t), horizontal (x) and vertical (y) coordinate as columns,
    and returns several dynamical properties such as speed, acceleration or curvature.

A collection of 'shiny' applications for the 'tesselle'
    packages <https://www.tesselle.org/>. This package provides
    applications for archaeological data analysis and visualization. These
    mainly, but not exclusively, include applications for chronological
    modelling (e.g. matrix seriation, aoristic analysis) and count data
    analysis (e.g. diversity measures, compositional data analysis).

Data on houses in and around Seattle WA are included. Basic 
    characteristics are given along with sale prices.

Methods for inference about/under complex relationships using
  peak height data from DNA mixtures: the most basic example would be testing whether a contributor to 
  a mixture is the father of a child of known genotype. This provides most of the functionality
  of the 'KinMix' package, but with some loss of efficiency and restriction on problem size,
  as the latter uses 'RHugin' as the Bayes net engine, while this package uses 'gRain'.
  The package implements the methods introduced in 
  Green, P. J. and Mortera, J. (2017) <doi:10.1016/j.fsigen.2017.02.001> and 
  Green, P. J. and Mortera, J. (2021) <doi:10.1111/rssc.12498>.