Provides the first standardized dataset of the Philippines' 
    Roll-on/Roll-off (RoRo) shipping network, reflecting the 2024-2026 
    operational state. It digitizes fragmented records from the Maritime 
    Industry Authority (MARINA) and Philippine Ports Authority (PPA) 
    into a unified framework for transport modeling. The package includes 
    108 bidirectional provincial links across the Western, Central, and 
    Eastern Nautical Highways, complete with GADM-standardized naming, 
    geospatial coordinates, and metrics such as distance, travel time, 
    and vessel frequency. Methodology follows Anselin (1988, 
    ISBN:9024737354) and LeSage and Pace (2009) <doi:10.1201/9781420064254> 
    for spatial weight construction. Data sources include "MARINA 
    Inventory of RoRo Routes" <https://marina.gov.ph> and "PPA Port 
    Statistics" <https://www.ppa.com.ph/ppa_statistics>. Designed to 
    support research in economic geography and disaster-response 
    logistics.

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.

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>.