Computation of second-generation p-values as described in Blume et al. (2018) <doi:10.1371/journal.pone.0188299> and Blume et al. (2019) <doi:10.1080/00031305.2018.1537893>. There are additional functions which provide power and type I error calculations, create graphs (particularly suited for large-scale inference usage), and a function to estimate false discovery rates based on second-generation p-value inference.

Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.

Implementation of Sparse-group SLOPE: Adaptive bi-level with FDR-control (Feser et al. (2023) <arXiv:2305.09467>). 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.

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>.