SVEMnet is an R package for fitting Self-Validated Ensemble Models (SVEM) with elastic-net base learners and performing multi-response optimization in small-sample mixture-process design-of-experiments (DOE) studies with numeric, categorical, and mixture factors. SVEMnet wraps elastic-net and relaxed elastic-net models for Gaussian and binomial responses from glmnet in a fractional random-weight (FRW) resampling scheme with anti-correlated train/validation weights; penalties are selected by validation-weighted AIC- and BIC-type criteria, and predictions are averaged across replicates to stabilize fits near the interpolation boundary. In addition to the core SVEM engine, the package provides deterministic high-order formula expansion, a permutation-based whole-model test heuristic, and a mixture-constrained random-search optimizer that combines Derringer-Suich desirability functions, bootstrap-based uncertainty summaries, and optional mean-level specification-limit probabilities to generate scored candidate tables and diverse exploitation and exploration medoids for sequential fit-score-run-refit workflows. A simulated lipid nanoparticle (LNP) formulation study illustrates these tools in a small-sample mixture-process DOE setting, and simulation experiments based on sparse quadratic response surfaces benchmark SVEMnet against repeated cross-validated elastic-net baselines.

翻译：SVEMnet是一个R软件包，用于在小样本混合-过程实验设计（DOE）研究中拟合具有弹性网络基础学习器的自验证集成模型（SVEM），并执行包含数值、类别和混合因子的多响应优化。该软件包将glmnet中适用于高斯和二项响应的弹性网络及松弛弹性网络模型封装在具有反相关训练/验证权重的分数随机权重（FRW）重抽样框架中；通过验证加权AIC与BIC类准则选择惩罚参数，并通过重复样本的预测平均来稳定插值边界附近的拟合。除核心SVEM引擎外，该软件包还提供确定性高阶公式展开、基于置换的整体模型检验启发式方法，以及结合Derringer-Suich合意性函数、基于自助法的不确定性汇总与可选的均值水平规格限概率的混合约束随机搜索优化器，以生成评分候选表及用于序列化“拟合-评分-运行-再拟合”工作流的多样化开发与探索中心点。一项模拟脂质纳米颗粒（LNP）配方研究在小样本混合-过程DOE场景中展示了这些工具的功能；基于稀疏二次响应面的模拟实验将SVEMnet与重复交叉验证的弹性网络基线进行了性能对比。