holiglm-package {holiglm}R Documentation

Holistic Generalized Linear Models Package

Description

The holistic generalized linear models package simplifies estimating generalized linear models under constraints. The constraints can be used to,

This sophisticated constraints are internally implemented via conic optimization. However, the package is designed such that the user, is not required to be familiar with conic optimization but is only required to have basic R knowledge.

Author(s)

References

Holistic regression
Schwendinger, B., Schwendinger, F., & Vana, L. (2024). Holistic Generalized Linear Models. doi:10.18637/jss.v108.i07.

Bertsimas, D., & King, A. (2016). OR Forum-An Algorithmic Approach to Linear Regression Operations Research 64(1):2-16. doi:10.1287/opre.2015.1436

Bertsimas, D., & Li, M. L. (2020). Scalable Holistic Linear Regression. Operations Research Letters 48 (3): 203–8. doi:10.1016/j.orl.2020.02.008.

Constrained regression
McDonald, J. W., & Diamond, I. D. (1990). On the Fitting of Generalized Linear Models with Nonnegativity Parameter Constraints. Biometrics, 46 (1): 201–206. doi:10.2307/2531643

Slawski, M., & Hein, M. (2013). Non-negative least squares for high-dimensional linear models: Consistency and sparse recovery without regularization. Electronic Journal of Statistics, 7: 3004-3056. doi:10.1214/13-EJS868

Carrizosa, E., Olivares-Nadal, A. V., & Ramírez-Cobo, P. (2020). Integer Constraints for Enhancing Interpretability in Linear Regression. SORT. Statistics and Operations Research Transactions, 44: 67-98. doi:10.2436/20.8080.02.95.

Lawson, C. L., & Hanson, R. J. (1995). Solving least squares problems. Society for Industrial and Applied Mathematics. Society for Industrial and Applied Mathematics. doi:10.1137/1.9781611971217

Generalized Linear Models
McCullagh, P., & Nelder, J. A. (2019). Generalized Linear Models (2nd ed.) Routledge. doi:10.1201/9780203753736.

Conic Optimization
Boyd, S., & Vandenberghe, L. (2004). Convex Optimization (1st ed.) Cambridge University Press. https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf. doi:10.1017/cbo9780511804441

Theußl, S., Schwendinger, F., & Hornik, K. (2020). ROI: An Extensible R Optimization Infrastructure. Journal of Statistical Software 94 (15): 1–64. doi:10.18637/jss.v094.i15.

See Also

hglm, holiglm


[Package holiglm version 1.0.0 Index]