*The package is deprecated. It uses the standard drivers
  on R >= 4.6.0 since they incorporate all the functionalities below.*
  Weave and tangle drivers for Sweave extending the standard drivers.
  RweaveExtraLatex and RtangleExtra provide options to completely
  ignore code chunks on weaving, tangling, or both. Chunks ignored on
  weaving are not parsed, yet are written out verbatim on tangling.
  Chunks ignored on tangling may be evaluated as usual on weaving, but
  are completely left out of the tangled scripts. The driver
  RtangleExtra also provides options to control the separation between
  code chunks in the tangled script, and to specify the extension of
  the file name (or remove it entirely) when splitting is selected.

An R interface to Weka (Version 3.9.3).
   Weka is a collection of machine learning algorithms for data mining
   tasks written in Java, containing tools for data pre-processing,
   classification, regression, clustering, association rules, and
   visualization.  Package 'RWeka' contains the interface code, the
   Weka jar is in a separate package 'RWekajars'.  For more information
   on Weka see <https://www.cs.waikato.ac.nz/ml/weka/>.

External jars required for package 'RWeka'.

Toolbox that provides a streamlined, end-to-end workflow for propensity
    score analysis in generating real-world evidence from real-world data.
    The package covers the full analytic pipeline - from estimating propensity scores
    via logistic regression, to calculating weights or creating a matched cohort, 
    to generating publication-ready Table 1s with standardized mean
    differences and weighted balance diagnostics. It also estimates incidence rates,
    hazard ratios, risk ratios, and risk differences with support for stratified and
    direct-standardized analyses. All core functions produce formatted 'Excel' reports
    with embedded 'README' documentation, making results immediately shareable with
    collaborators and stakeholders. Methods are based on Rosenbaum and Rubin (1983)
    <doi:10.1093/biomet/70.1.41>, Austin (2011) <doi:10.1080/00273171.2011.568786>,
    and Desai et al. (2017) <doi:10.1097/EDE.0000000000000595>.