Oracle Database interface (DBI) driver for R.
   This is a DBI-compliant Oracle driver based on the OCI.

Client for the 'Orcid.org' API (<https://orcid.org/>).
    Functions included for searching for people, searching by 'DOI',
    and searching by 'Orcid' 'ID'.

Predicts morphological parameters of rorquals (e.g. body mass, 
    flipper length, maximum engulfment capacity) from body length using 
    allometric equations from Kahane-Rapport and Goldbogen (2018) 
    <doi:10.1002/jmor.20846>.

Implements a null model analysis to quantify concurrent temporal niche overlap (i.e., activity or phenology) among biological identities (e.g., individuals, populations, species) using the Rosario randomization algorithm Castro-Arellano et al. (2010) <doi:10.1111/j.2041-210X.2010.00031.x>.

Functions to deal with binary classification
  problems in the presence of imbalanced classes. Synthetic balanced samples are  
  generated according to ROSE (Menardi and Torelli, 2013).  
  Functions that implement more traditional remedies to the class imbalance
  are also provided, as well as different metrics to evaluate a learner accuracy.
  These are estimated by holdout, bootstrap or cross-validation methods.

New Markov chain Monte Carlo (MCMC) samplers new to be thoroughly tested
    and their performance accurately assessed. This requires densities
    that offer challenging properties to the novel sampling algorithms.
    One such popular problem is the Rosenbrock function. However, while its
    shape lends itself well to a benchmark problem, no codified multivariate expansion
    of the density exists. We have developed an extension to this class of distributions
    and supplied densities and direct sampler functions to assess the performance
    of novel MCMC algorithms. The functions are introduced in "An n-dimensional Rosenbrock 
    Distribution for MCMC Testing" by Pagani, Wiegand and Nadarajah (2019) <arXiv:1903.09556>.

ROSE (RObust Semiparametric Efficient) random forests for robust 
  semiparametric efficient estimation in partially parametric models (containing 
  generalised partially linear models).
  Details can be found in the paper by Young and Shah (2024) <doi:10.48550/arXiv.2410.03471>.