Holistic linear regression extends the classical best subset selection problem by adding additional constraints designed to improve the model quality. These constraints include sparsity-inducing constraints, sign-coherence constraints and linear constraints. The $\textsf{R}$ package $\texttt{holiglm}$ provides functionality to model and fit holistic generalized linear models. By making use of state-of-the-art conic mixed-integer solvers, the package can reliably solve GLMs for Gaussian, binomial and Poisson responses with a multitude of holistic constraints. The high-level interface simplifies the constraint specification and can be used as a drop-in replacement for the $\texttt{stats::glm()}$ function.

翻译：全线回归通过增加旨在改进模型质量的额外限制来扩展经典最佳子集选择问题。 这些限制包括聚变诱导限制、符号一致性限制和线性限制。 $\ textsf{R}$\ textt{holiglm} 软件包为模型提供了功能,并适合整体通用线性模型。 通过使用最先进的锥形混合整数解答器, 软件包可以可靠地解决高斯、 二流和普瓦森的GLMs, 并使用多种整体限制。 高级界面简化了约束性规范, 并可以用作 $\ textt{ stat: glm( )} 功能的倒置替换 。