Intended for larger-than-memory tabular data, 'prt' objects provide an interface to read row and/or column subsets into memory as data.table objects. Data queries, constructed as 'R' expressions, are evaluated using the non-standard evaluation framework provided by 'rlang' and file-backing is powered by the fast and efficient 'fst' package.

Implementation of Probabilistic Regression Trees (PRTree),
  providing functions for model fitting and prediction, with specific adaptations
  to handle missing values. The main computations are implemented in 'Fortran'
  for high efficiency. The package is based on the PRTree methodology described in
  Alkhoury et al. (2020), "Smooth and Consistent Probabilistic Regression Trees"
  <https://proceedings.neurips.cc/paper_files/paper/2020/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf>.
  Details on the treatment of missing data and implementation aspects are presented in
  Prass, T.S.; Neimaier, A.S.; Pumi, G. (2025), "Handling Missing Data in Probabilistic Regression Trees:
  Methods and Implementation in R" <doi:10.48550/arXiv.2510.03634>.

Compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations, as well as Mardia-Dryden distributions and self-similar distributions of landmarks, as described in Mitteroecker et al. (2020) <doi:10.1093/sysbio/syaa007>. Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) <doi:10.5061/dryad.j6q573n8s> and Grunstra et al. (2020) <doi:10.5061/dryad.zkh189373>.

Data sets and functions used in the polish book 
    "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). 
    See more at <http://biecek.pl/R>. Among others you will find here 
    data about housing prices, cancer patients, running times and many others.