Tools to make it easier to work with "tables" of 'grobs'. The
    'gtable' package defines a 'gtable' grob class that specifies a grid
    along with a list of grobs and their placement in the grid. Further
    the package makes it easy to manipulate and combine 'gtable' objects
    so that complex compositions can be built up sequentially.

Computes the  probability density, survival function,
   the hazard rate functions and generates random samples from the 
   GTDL distribution given by Mackenzie, G. (1996) <doi:10.2307/2348408>. 
   The likelihood estimates, the randomized quantile (Louzada, F., et al. 
   (2020) <doi:10.1109/ACCESS.2020.3040525>)
   residuals and the normally transformed randomized survival 
   probability (Li,L., et al. (2021) <doi:10.1002/sim.8852>)
   residuals are obtained for the GTDL model.

Generalized Turnbull's estimator proposed by Dehghan and Duchesne
    (2011).

Four graph-based tests are provided for testing whether two samples are from the same distribution.  It works for both continuous data and discrete data.

New multi-sample tests for testing whether multiple samples are from the same distribution. They work well particularly for high-dimensional data.
    Song, H. and Chen, H. (2022) 
    <arXiv:2205.13787>.

Provides additional functions for creating beautiful tables
    with 'gt'. The functions are generally wrappers around boilerplate or
    adding opinionated niche capabilities and helpers functions.

Generalizes application of gray-level co-occurrence matrix
    (GLCM) metrics to objects outside of images. The current focus is to
    apply GLCM metrics to the study of biological networks and fitness
    landscapes that are used in studying evolutionary medicine and
    biology, particularly the evolution of cancer resistance. The package was
    developed as part of the author's publication in Physics in Medicine and Biology
    Barker-Clarke et al. (2023) <doi:10.1088/1361-6560/ace305>. 
    A general reference to learn more about mathematical oncology can be found at
    Rockne et al. (2019) <doi:10.1088/1478-3975/ab1a09>.