Interactive visualizations of graphs created with the 'igraph' package using a 'htmlwidgets' wrapper for the 'sigma.js' network visualization v2.4.0 <https://www.sigmajs.org/>, enabling to display several thousands of nodes. While several 'R' packages have been developed to interface 'sigma.js', all were developed for v1.x.x and none have migrated to v2.4.0 nor are they planning to. This package builds upon the 'sigmaNet' package, and users familiar with it will recognize the similar design approach. Two extensions have been added to the classic 'sigma.js' visualizations by overriding the underlying 'JavaScript' code, enabling to draw a frame around node labels, and to display labels on multiple lines by parsing line breaks. Other additional functionalities that did not require overriding 'sigma.js' code include toggling node visibility when clicked using a node attribute and highlighting specific edges. 'sigma.js' is currently preparing a stable release v3.0.0, and this package plans to update to it when it is available.

Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) <doi:10.48550/arXiv.2305.09467>) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) and group-based OSCAR models (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) for computational speed-up.

Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS; LIDAR). Primary functions provide means 
    to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information 
    about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management
    practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data
    distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used, 
    enabling data-driven stratifications and sampling approaches.