The systemPipeShiny (SPS) framework comes with many UI and server components. However, installing the whole framework is heavy and takes some time. If you would like to use UI and server components from SPS in your own Shiny apps, do not hesitate to try this package.

Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models.  All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale.

Provides basic functionality for labeling 
        iso- & anisotropic percolation clusters on 2D & 3D square lattices 
        with various lattice sizes, occupation probabilities, von Neumann & 
        Moore (1,d)-neighborhoods, and random variables weighting 
        the percolation lattice sites.

An implementation of the feature Selection procedure by Partitioning the entire Solution Paths
            (namely SPSP) to identify the relevant features rather than using a single tuning parameter. 
            By utilizing the entire solution paths, this procedure can obtain better selection accuracy than 
            the commonly used approach of selecting only one tuning parameter based on existing criteria, 
            cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) 
            <doi:10.1214/18-EJS1434>). It is more stable and accurate (low false positive and 
            false negative rates) than other variable selection approaches. In addition, it can be flexibly 
            coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized 
            estimators.

Fits Bayesian hierarchical spatial and spatial-temporal process
    models for point-referenced Gaussian, Poisson, binomial, and binary data
    using stacking of predictive densities. It involves sampling from
    analytically available posterior distributions conditional upon candidate
    values of the spatial process parameters and, subsequently assimilate
    inference from these individual posterior distributions using Bayesian
    predictive stacking. Our algorithm is highly parallelizable and hence, much
    faster than traditional Markov chain Monte Carlo algorithms while delivering
    competitive predictive performance. See Zhang, Tang, and Banerjee (2025)
    <doi:10.1080/01621459.2025.2566449>, and, Pan, Zhang, Bradley, and Banerjee
    (2025) <doi:10.1214/25-BA1582> for details.