Provides functions to download, parse, and tidy statistical
    data published by 'HM Revenue and Customs' ('HMRC') on 'GOV.UK'.
    Returns annotated 'hmrc_tbl' data frames with provenance metadata
    (source URL, fetch time, vintage, cell methods) for reproducible
    fiscal research. Covers monthly tax receipts (41 tax heads from 2008),
    'VAT' (from 1973), fuel duties (from 1990), tobacco duties (from 1991),
    annual 'Corporation Tax' receipts, stamp duty, research and development
    tax credit statistics (from 2000), tax gap estimates, 'Income Tax'
    liabilities by income range, and monthly property transaction counts.
    File URLs are resolved at runtime via the 'GOV.UK' Content API
    <https://www.gov.uk/api/content>, so data is always current without
    hardcoded URLs. Files are cached locally between sessions.

Implements an S3 class for storing and formatting time-of-day
    values, based on the 'difftime' class.

Hierarchical Modelling of Species Communities (HMSC) is
   a model-based approach for analyzing community ecological data. 
   This package implements it in the Bayesian framework with Gibbs
   Markov chain Monte Carlo (MCMC) sampling (Tikhonov et al. (2020)
   <doi:10.1111/2041-210X.13345>).

A collection of datasets and supporting functions accompanying Health Metrics and the Spread of Infectious Diseases by Federica Gazzelloni (2024). This package provides data for health metrics calculations, including Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLLs), and Years Lived with Disability (YLDs), as well as additional tools for analyzing and visualizing health data. Federica Gazzelloni (2024) <doi:10.5281/zenodo.10818338>.

The HMS (Hierarchic Memetic Strategy) is a composite global optimization
  strategy consisting of a multi-population evolutionary strategy and some
  auxiliary methods. The HMS makes use of a dynamically-evolving data structure
  that provides an organization among the component populations. It is a tree
  with a fixed maximal height and variable internal node degree. Each component
  population is governed by a particular evolutionary engine. This package
  provides a simple R implementation with examples of using different genetic
  algorithms as the population engines. References: J. Sawicki, M. Łoś,
  M. Smołka, J. Alvarez-Aramberri (2022) <doi:10.1007/s11047-020-09836-w>.