Provides a higher-level interface to the 'torch' package for defining,
    training, and fine-tuning neural networks through code generation.
    The package supports several architectures, including feedforward
    (multi-layer perceptron) and recurrent neural networks (RNN, LSTM,
    GRU), while reducing boilerplate 'torch' code. Model training
    methods also bridge to machine learning frameworks in R,
    particularly the 'tidymodels' ecosystem, including '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).

Comparative evaluation of families and candidate variants in
    rare-variant association studies. The package can be used for
    two methodologically overlapping but distinct purposes. First, the prior to any genetic or genomic
    evaluation, evaluation of relative detection power of pedigrees, can direct recruitment
    efforts by showing which individuals not yet sampled would be the most meaningful additions to a study.
    Second, after sequencing and analysis,  variants based on association with disease status
    and familial relationships of individuals, aids in variant prioritization.
    Methodology is described in 
    Nugent (2025) <doi:10.1101/2025.10.06.25337426>.

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