spikeslab

R package spikeslab: Prediction and variable selection using spike and slab regression. Spike and slab for prediction and variable selection in linear regression models. Uses a generalized elastic net for variable selection.

References in zbMATH (referenced in 11 articles )

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Page, Garritt L.; Quintana, Fernando A.; Rosner, Gary L.: Discovering interactions using covariate informed random partition models (2021)
Tai, An-Shun; Tseng, George C.; Hsieh, Wen-Ping: BayICE: a Bayesian hierarchical model for semireference-based deconvolution of bulk transcriptomic data (2021)
Canhong Wen, Aijun Zhang, Shijie Quan, Xueqin Wang: BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models (2020) not zbMATH
Piironen, Juho; Paasiniemi, Markus; Vehtari, Aki: Projective inference in high-dimensional problems: prediction and feature selection (2020)
Pazira, Hassan; Augugliaro, Luigi; Wit, Ernst: Extended differential geometric LARS for high-dimensional GLMs with general dispersion parameter (2018)
Vivo, Juana-María; Franco, Manuel; Vicari, Donatella: Rethinking an ROC partial area index for evaluating the classification performance at a high specificity range (2018)
Latouche, Pierre; Mattei, Pierre-Alexandre; Bouveyron, Charles; Chiquet, Julien: Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high-dimensional regression (2016)
Chang, Jing; Lee, Herbert K. H.: Variable selection via a multi-stage strategy (2015)
Bleich, Justin; Kapelner, Adam; George, Edward I.; Jensen, Shane T.: Variable selection for BART: an application to gene regulation (2014)
Ishwaran, Hemant; Rao, J. Sunil: Geometry and properties of generalized ridge regression in high dimensions (2014)
Narisetty, Naveen Naidu; He, Xuming: Bayesian variable selection with shrinking and diffusing priors (2014)