Functions for transforming and viewing 2-D and 3-D (oceanographic) data and model output.

Calculate ocean wave height summary statistics and process data 
    from bottom-mounted pressure sensor data loggers. Derived primarily from 
    MATLAB functions provided by U. Neumeier at 
    <http://neumeier.perso.ch/matlab/waves.html>. Wave number 
    calculation based on the algorithm in Hunt, J. N. (1979, ISSN:0148-9895) 
    "Direct Solution of Wave Dispersion Equation", American Society of Civil 
    Engineers Journal of the Waterway, Port, Coastal, and Ocean Division, 
    Vol 105, pp 457-459.

Estimates win ratio or Mann-Whitney parameter for two group
 comparisons using ordered composite endpoints with right censoring
 as described in Follmann, Fay, Hamasaki, and Evans (2020)<doi:10.1002/sim.7890>.

Several Oceanographic data sets are provided for use
    by the 'oce' package and for other purposes.

Picks the suitable cell types in spatial and scRNA-seq data using shrinkage methods.
    The package includes curated reference gene expression profiles for human and mouse cell types, 
    facilitating immediate application to common spatial transcriptomics or scRNA datasets. 
    Additionally, users can input custom reference data to support tissue- or experiment-specific analyses.

Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. 'ocf' provides forest-based estimation of the 
    conditional choice probabilities and the covariates’ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal 
    and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025)
    <doi:10.1080/07474938.2024.2429596>.

Extends 'flexclust' with an R implementation of order constrained
  solutions in k-means clustering (Steinley and Hubert, 2008, <doi:10.1007/s11336-008-9058-z>).

Provides a function to detect and trim outliers in Gaussian mixture model-based clustering using methods described in Clark and McNicholas (2024) <doi:10.1007/s00357-024-09473-3>.