Provides a comprehensive suite of genome-wide association study (GWAS) methods 
    specifically designed for biobank-scale data, including but not limited to, robust approaches 
    for time-to-event traits (Li et al., 2025 <doi:10.1038/s43588-025-00864-z>) and ordinal 
    categorical traits (Bi et al., 2021 <doi:10.1016/j.ajhg.2021.03.019>). The package also offers 
    general frameworks for GWAS of any trait type (Bi et al., 2020 <doi:10.1016/j.ajhg.2020.06.003>), 
    while accounting for sample relatedness (Xu et al., 2025 <doi:10.1038/s41467-025-56669-1>) or 
    population structure (Ma et al., 2025 <doi:10.1186/s13059-025-03827-9>). By accurately 
    approximating score statistic distributions using saddlepoint approximation (SPA), these 
    methods can effectively control type I error rates for rare variants and in the presence of 
    unbalanced phenotype distributions. Additionally, the package includes functions for simulating 
    genotype and phenotype data to support research and method development.

Functions for obtaining the probability of detection, for grab samples selection by using two different methods such as systematic or random based on two-state Markov chain model. For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988) <doi:10.2307/1427041>.

Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package implemented a granularity-based dimension-agnostic tool for the identification of spatially variable genes. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).

Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016 <doi:10.1111/biom.12418>) to estimate graph-guided linear regression coefficients and use the Grace/GraceI/GraceR tests to perform graph-guided hypothesis tests on the association between the response and the predictors.