A programmatic interface to the web service methods
    provided by Global Biotic Interactions (GloBI)
    (<https://www.globalbioticinteractions.org/>). GloBI provides 
    access to spatial-temporal species interaction records from 
    sources all over the world. rglobi provides methods to search 
    species interactions by location, interaction type, and 
    taxonomic name.

R interface to the GNU Linear Programming Kit.
  'GLPK' is open source software for solving large-scale linear programming (LP),
  mixed integer linear programming ('MILP') and other related problems.

Provides 3D plotting routines that facilitate the use of the 'rgl' package and extend its functionality. For example, the routines allow the user to directly control the camera position & orientation, as well as to generate 3D movies with a moving observer.

Implements state-of-the-art Random Graphical Models (RGMs) for multivariate data analysis across multiple environments, offering tools for exploring network interactions and structural relationships. Capabilities include joint inference across environments, integration of external covariates, and a Bayesian framework for uncertainty quantification. Applicable in various fields, including microbiome analysis. Methods based on Vinciotti, V., Wit, E., & Richter, F. (2023). "Random Graphical Model of Microbiome Interactions in Related Environments." <arXiv:2304.01956>.

Algorithms for estimating robustly the parameters of a Gaussian, Student, or Laplace Mixture Model.

Implementation of the Robust Gauss-Newton (RGN) algorithm,
    designed for solving optimization problems with a sum of least squares
    objective function. For algorithm details please refer to Qin et. al. (2018) 
    <doi:10.1029/2017WR022488>.

Traditional noise filtering methods aim at removing noisy samples from a classification dataset. This package adapts classic and recent filtering techniques for use in regression problems, and it also incorporates methods specifically designed for regression data. In order to do this, it uses approaches proposed in the specialized literature, such as Martin et al. (2021) [<doi:10.1109/ACCESS.2021.3123151>] and Arnaiz-Gonzalez et al. (2016) [<doi:10.1016/j.eswa.2015.12.046>]. Thus, the goal of the implemented noise filters is to eliminate samples with noise in regression datasets.