glmmLasso

Groll, A.: glmmLasso: Variable Selection for Generalized Linear Mixed Models by L1-penalized Estimation. R package

Keywords for this software

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References in zbMATH (referenced in 8 articles )

Showing results 1 to 8 of 8.
Sorted by year (citations)

  1. Nestler, Steffen; Humberg, Sarah: A Lasso and a regression tree mixed-effect model with random effects for the level, the residual variance, and the autocorrelation (2022)
  2. Bonner, S., Kim, H.-N., Westneat, D., Mutzel, A., Wright, J., Schofield, M.: dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble (2021) not zbMATH
  3. Chauvet, Jocelyn; Trottier, Catherine; Bry, Xavier: Component-based regularization of multivariate generalized linear mixed models (2019)
  4. Xie, Yimeng; Xu, Li; Li, Jie; Deng, Xinwei; Hong, Yili; Kolivras, Korine; Gaines, David N.: Spatial variable selection and an application to Virginia Lyme disease emergence (2019)
  5. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  6. Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
  7. Hou, Jiayi; Archer, Kellie J.: Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data (2015)
  8. Groll, Andreas; Tutz, Gerhard: Variable selection for generalized linear mixed models by (L_1)-penalized estimation (2014)