Holistic Generalized Linear Models

Abstract

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 R package holiglm provides functionality to model and fit holistic generalized linear models. By making use of state-of-the-art mixed-integer conic solvers, the package can reliably solve generalized linear models 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 stats::glm() function.