Provides a flexible tool for calculating carbon-equivalent emissions. Mostly using data from the UK Government's Greenhouse Gas Conversion Factors report <https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2024>, it facilitates transparent emissions calculations for various sectors, including travel, accommodation, and clinical activities. The package is designed for easy integration into R workflows, with additional support for 'shiny' applications and community-driven extensions.

The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.

A collection of cardiovascular research datasets and analytical
		tools, including methods for cardiovascular procedural data, such as 
		electrocardiography, echocardiography, and catheterization data. Additional
		methods exist for analysis of procedural billing codes.

Responsive and modern 'HTML' card essentials for 'shiny' applications and dashboards. This novel card component in 'Bootstrap' provides a flexible and extensible content container with multiple variants and options for building robust 'R' based apps e.g for graph build or machine learning projects. The features rely on a combination of 'JQuery' <https://jquery.com> and 'CSS' styles to improve the card functionality.

Datasets to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage (2019).

Identification of cardinal dates
             (begin, time of maximum, end of mass developments)
             in ecological time series using fitted Weibull functions.

A collection of functions to generate a large variety of
    structures in high dimensions. These data structures are useful for
    testing, validating, and improving algorithms used in dimensionality
    reduction, clustering, machine learning, and visualization.