Simple result caching in R based on R.cache. The global environment is not 
    considered when caching results simplifying moving files between multiple instances 
    of R. Relies on more base functions than R.cache (e.g. cached results are saved using 
    saveRDS() and readRDS()).

Runs R-code present in a pandoc markdown file and 
  includes the resulting output in the resulting markdown file. This
  file can then be converted into any of the output formats 
  supported by pandoc. The package can also be used as an engine
  for writing package vignettes.

When working across multiple machines and, similarly for
    reproducible research, it can be time consuming to ensure that you have
    all of the needed packages installed and loaded and that the correct working
    directory is set. 'simpleSetup' provides simple functions for making these
    tasks more straightforward.

By calling the 'SimpleTex' <https://simpletex.cn/> open API implements 
    text and mathematical formula recognition on the image, and the output formula 
    can be used directly with 'Markdown' and 'LaTeX'.

Create Upset plots using a combination of 'ggplot2' and 'patchwork'.

Implements the bootstrap-calibrated local-influence
    goodness-of-fit test for simplex regression models with constant or
    varying dispersion, following the local influence approach of
    Zhu and Zhang (2004) <doi:10.1093/biomet/91.3.579> and the simplex
    regression model of Barndorff-Nielsen and Jorgensen (1991)
    <doi:10.1016/0047-259X(91)90008-P>. The test statistic aggregates
    individual local-influence measures under case-weight perturbation.
    Because the first-order asymptotic normal calibration is severely
    liberal in finite samples, a parametric bootstrap calibration is
    provided that restores accurate size control and delivers high power
    against omitted covariates, neglected dispersion, and distributional
    misspecification. Plotting functions reproduce the figures and tables
    of the companion methodological paper. Computational kernels are
    implemented in 'C++' via 'Rcpp' and 'RcppArmadillo' for speed, and two
    real datasets are bundled.