pense - Penalized Elastic Net S/MM-Estimator of Regression
Robust penalized (adaptive) elastic net S and M estimators for linear regression. The adaptive methods are proposed in Kepplinger, D. (2023) <doi:10.1016/j.csda.2023.107730> and the non-adaptive methods in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) <doi:10.1214/19-AOAS1269>. The package implements robust hyper-parameter selection with robust information sharing cross-validation according to Kepplinger & Wei (2025) <doi:10.1080/00401706.2025.2540970>.
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linear-regressionpenseregressionrobust-regresssionrobust-statisticsopenblascppopenmp
6.63 score 5 stars 71 scripts 441 downloadspyinit - Pena-Yohai Initial Estimator for Robust S-Regression
Deterministic Pena-Yohai initial estimator for robust S estimators of regression. The procedure is described in detail in Pena, D., & Yohai, V. (1999) <doi:10.2307/2670164>.
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openblas
6.21 score 1 stars 10 dependents 19 scripts 14k downloadsgaselect - Genetic Algorithm (GA) for Variable Selection from High-Dimensional Data
Provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution.
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openblascpp
3.60 score 4 stars 9 scripts 219 downloadscomplmrob - Robust Linear Regression with Compositional Data as Covariates
Robust regression methods for compositional data. The distribution of the estimates can be approximated with various bootstrap methods. These bootstrap methods are available for the compositional as well as for standard robust regression estimates. This allows for direct comparison between them.
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3.00 score 20 scripts 349 downloads