Methods for manipulating regression models and for describing these in a style adapted for medical journals.
  Contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model
  estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots.
  In addition to the descriptive methods, there are functions for the robust covariance matrix provided by the 'sandwich'
  package, a function for adding non-linearities to a model, and a wrapper around the 'Epi' package's Lexis() functions for
  time-splitting a dataset when modeling non-proportional hazards in Cox regressions.

Computes Gregory weights for a given number nodes and function order. Anthony Ralston and Philip Rabinowitz (2001) <ISBN:9780486414546>.

Functions which make using the Generalized Regression Estimator(GREG)
    J.N.K. Rao, Isabel Molina, (2015) <doi:10.3390/f11020244> 
    and the Generalized Regression Estimator Operating on Resolutions of Y (GREGORY) easier. 
    The functions are designed to work well within a forestry context, and estimate multiple 
    estimation units at once. Compared to other survey estimation packages, this function has greater flexibility when 
    describing the linear model.

We propose two distribution-free test statistics based on between-sample edge counts and measure the degree of relevance by standardized counts. Users can set edge costs in the graph to compare the parameters of the distributions. Methods for comparing distributions are as described in: Xiaoping Shi (2021) <arXiv:2107.00728>.

Fit linear mixed-effects models using restricted (or residual)
    maximum likelihood (REML) and with generalized inverse matrices to specify
    covariance structures for random effects. In particular, the package is
    suited to fit quantitative genetic mixed models, often referred to as
    'animal models'. Implements the average information algorithm as the main
    tool to maximize the restricted log-likelihood, but with other algorithms
    available.