Datasets of the International Chess Federation's player ratings and country information analysed in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics".

Applies the change-in-effect estimate method to assess confounding 
    effects in medical and epidemiological research (Greenland & Pearce (2016) 
    <doi:10.1146/annurev-publhealth-031914-122559> ). It starts with a crude model 
    including only the outcome and exposure variables. At each of the subsequent 
    steps, one variable which creates the largest change among the remaining variables 
    is selected. This process is repeated until all variables have been entered into 
    the model (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183–196). Currently, the 'chest' 
    package has functions for linear regression, logistic regression, negative 
    binomial regression, Cox proportional hazards model and conditional logistic 
    regression.

Provides tools to process and analyze chest expansion using 3D marker data from motion capture systems. Includes functions for data processing, marker position adjustment, volume calculation using convex hulls, and visualization in 2D and 3D. Barber et al. (1996) <doi:10.1145/235815.235821>. TAMIYA Hiroyuki et al. (2021) <doi:10.1038/s41598-021-01033-8>.

Provide standard tables, listings, and graphs (TLGs)
    libraries used in clinical trials. This package implements a structure
    to reformat the data with 'dunlin', create reporting tables using
    'rtables' and 'tern' with standardized input arguments to enable quick
    generation of standard outputs.  In addition, it also provides
    comprehensive data checks and script generation functionality.

Light weight implementation of the standard distribution 
  functions for the chi distribution, wrapping those for the chi-squared 
  distribution in the stats package.

Toolkit for processing and calling interactions in capture Hi-C data. Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model.