Tools for fitting self-validated ensemble models (SVEM; Lemkus et al. (2021) <doi:10.1016/j.chemolab.2021.104439>) in small-sample design-of-experiments and related workflows, using elastic net and relaxed elastic net regression via 'glmnet' (Friedman et al. (2010) <doi:10.18637/jss.v033.i01>). Fractional random-weight bootstraps with anti-correlated validation copies are used to tune penalty paths by validation-weighted AIC/BIC. Supports Gaussian and binomial responses, deterministic expansion helpers for shared factor spaces, prediction with bootstrap uncertainty, and a random-search optimizer that respects mixture constraints and combines multiple responses via desirability functions. Also includes a permutation-based whole-model test for Gaussian SVEM fits (Karl (2024) <doi:10.1016/j.chemolab.2024.105122>). Package code was drafted with assistance from generative AI tools.

Obtain parameters of Svensson's Method, including percentage agreement, 
    systematic change and individual change. Also, the contingency table can be generated. 
    Svensson's Method is a rank-invariant nonparametric method for the analysis of ordered scales 
    which measures the level of change both from systematic and individual aspects. For the details, 
    please refer to Svensson E. Analysis of systematic and random differences between paired ordinal 
    categorical data [dissertation]. Stockholm: Almqvist & Wiksell International; 1993.

A unified framework for detecting spatially variable genes (SVGs) 
    in spatial transcriptomics data. This package integrates multiple 
    state-of-the-art SVG detection methods including 'MERINGUE' (Moran's I based 
    spatial autocorrelation), 'Giotto' binSpect (binary spatial enrichment test), 
    'SPARK-X' (non-parametric kernel-based test), and 'nnSVG' (nearest-neighbor 
    Gaussian processes). Each method is implemented with optimized performance 
    through vectorization, parallelization, and 'C++' acceleration where applicable.
    Methods are described in Miller et al. (2021) <doi:10.1101/gr.271288.120>,
    Dries et al. (2021) <doi:10.1186/s13059-021-02286-2>, 
    Zhu et al. (2021) <doi:10.1186/s13059-021-02404-0>, and
    Weber et al. (2023) <doi:10.1038/s41467-023-39748-z>.