Convert R Markdown documents into a variety of formats.

For the calculation of sample size or power in a two-group repeated measures design, accounting for attrition and accommodating a variety of correlation structures for the repeated measures; details of the method can be found in the scientific paper: Donald Hedeker, Robert D. Gibbons, Christine Waternaux (1999) <doi:10.3102/10769986024001070>.

Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions.

Read and write 'Matlab' MAT files from R. The 'rmatio'
    package supports reading MAT version 4, MAT version 5 and MAT
    compressed version 5. The 'rmatio' package can write version 5 MAT
    files and version 5 files with variable compression.

S3 and S4 functions are implemented for spatial multi-site
    stochastic generation of daily time series of temperature and
    precipitation. These tools make use of Vector AutoRegressive models (VARs).
    The weather generator model is then saved as an object and is calibrated by
    daily instrumental "Gaussianized" time series through the 'vars' package
    tools. Once obtained this model, it can it can be used for weather
    generations and be adapted to work with several climatic monthly time
    series.

A Bayesian credible interval is interpreted with respect to posterior probability, 
    and this interpretation is far more intuitive than that of a frequentist confidence interval. 
    However, standard highest-density intervals can be wide due to between-subjects variability and tends 
    to hide within-subject effects, rendering its relationship with the Bayes factor less clear 
    in within-subject (repeated-measures) designs. 
    This urgent issue can be addressed by using within-subject intervals in within-subject designs, 
    which integrate four methods including the Wei-Nathoo-Masson (2023) <doi:10.3758/s13423-023-02295-1>, 
    the Loftus-Masson (1994) <doi:10.3758/BF03210951>, 
    the Nathoo-Kilshaw-Masson (2018) <doi:10.1016/j.jmp.2018.07.005>, 
    and the Heck (2019) <doi:10.31234/osf.io/whp8t> interval estimates.