A system of functions and data aiming to apply quantitative analyses to forest 
    ecology, silviculture and decision-making. Besides, the package helps to carry out data 
    management, exploratory analysis, and model assessment.

Provides functions to aid in the design and analysis of
    agronomic and agricultural experiments through easy access to
    documentation and helper functions, especially for users who are
    learning these concepts. While not required for most functionality,
    this package enhances the `asreml` package which provides a 
    computationally efficient algorithm for fitting mixed models 
    using Residual Maximum Likelihood. It is a commercial package 
    that can be purchased as 'asreml-R' from 'VSNi' 
    <https://vsni.co.uk/>, who will supply a zip file for local 
    installation/updating (see <https://asreml.kb.vsni.co.uk/>).

Functions for species distribution modeling, calibration and evaluation, 
  ensemble of models, ensemble forecasting and visualization. The package permits to run
  consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences)
  dataset and to combine them in ensemble models and ensemble projections. Some bench of other 
  evaluation and visualization tools are also available within the package.

An aid for manipulating data associated with biomonitoring and bioassessment. Calculations include metric calculation, marking of excluded taxa, 
    subsampling, and multimetric index calculation.  Targeted communities are benthic macroinvertebrates, fish, periphyton, and coral.  As described in the Revised Rapid Bioassessment Protocols (Barbour et al. 1999) <https://archive.epa.gov/water/archive/web/html/index-14.html>.

Tools for bioinformatics modeling using recursive transformer-inspired 
    architectures, autoencoders, random forests, XGBoost, and stacked ensemble models. 
    Includes utilities for cross-validation, calibration, benchmarking, and threshold 
    optimization in predictive modeling workflows. The methodology builds on ensemble 
    learning (Breiman 2001 <doi:10.1023/A:1010933404324>), gradient boosting (Chen and 
    Guestrin 2016 <doi:10.1145/2939672.2939785>), autoencoders (Hinton and Salakhutdinov 
    2006 <doi:10.1126/science.1127647>), and recursive transformer efficiency approaches 
    such as Mixture-of-Recursions (Bae et al. 2025 <doi:10.48550/arXiv.2507.10524>).