Determine minimal protein set explaining
    peptide spectrum matches. Utility functions for creating fasta amino acid databases with decoys and contaminants.
    Peptide false discovery rate estimation for target decoy search results on psm, precursor, peptide and protein
    level. Computing dynamic swath window sizes based on MS1 or MS2 signal distributions.

Utilize the Bayesian prior and posterior predictive checking
   approach to provide a statistical assessment of replication success 
   and failure. The package is based on the methods proposed in 
   Zhao,Y., Wen X.(2021) <arXiv:2105.03993>.

Provides a function to convert 'PRQL' strings to 'SQL' strings.
    Combined with other R functions that take 'SQL' as an argument,
    'PRQL' can be used on R.

Reverse depends for a given package are queued such that multiple
 workers can run the reverse-dependency tests in parallel.

Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) <doi:10.1145/1143844.1143874>; Keilwagen et al. (2014) <doi:10.1371/journal.pone.0092209>; Grau et al. (2015) <doi:10.1093/bioinformatics/btv153>.

Tests periodicity in short time series using response surface regression.

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>.