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) 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.

A set of function that implements for seasonal multivariate time series analysis based on Seasonal Generalized Space
            Time Autoregressive with Seemingly Unrelated Regression (S-GSTAR-SUR) Model by Setiawan(2016)<https://www.researchgate.net/publication/316517889_S-GSTAR-SUR_model_for_seasonal_spatio_temporal_data_forecasting>.

Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation.