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.
|Author||Robert B. Gramacy <[email protected]>|
|Maintainer||Robert B. Gramacy <[email protected]>|
|Package repository||View on CRAN|
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