reglogit

reglogit: Simulation-Based Regularized Logistic Regression. Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface.

Keywords for this software

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References in zbMATH (referenced in 9 articles , 1 standard article )

Showing results 1 to 9 of 9.
Sorted by year (citations)

  1. Gregor Zens, Sylvia Frühwirth-Schnatter, Helga Wagner: Efficient Bayesian Modeling of Binary and Categorical Data in R: The UPG Package (2021) arXiv
  2. Durmus, Alain; Moulines, Éric: High-dimensional Bayesian inference via the unadjusted Langevin algorithm (2019)
  3. Ghosh, Joyee; Li, Yingbo; Mitra, Robin: On the use of Cauchy prior distributions for Bayesian logistic regression (2018)
  4. Hashem, Hussein; Vinciotti, Veronica; Alhamzawi, Rahim; Yu, Keming: Quantile regression with group Lasso for classification (2016)
  5. Storlie, Curtis; Anderson, Blake; Vander Wiel, Scott; Quist, Daniel; Hash, Curtis; Brown, Nathan: Stochastic identification of malware with dynamic traces (2014)
  6. Gramacy, Robert B.; Taddy, Matt; Wild, Stefan M.: Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning (2013)
  7. Polson, N. G.; Scott, J. G.: Data augmentation for non-Gaussian regression models using variance-mean mixtures (2013)
  8. Taddy, Matt: Multinomial inverse regression for text analysis (2013)
  9. Gramacy, Robert B.; Polson, Nicholas G.: Simulation-based regularized logistic regression (2012)