Bayes Logistic Regression

This package will fit Bayesian logistic regression models with arbitrary prior means and covariance matrices, although we work with the inverse covariance matrix which is the log-likelihood Hessian.

Either the full Hessian or a diagonal approximation may be used.

Individual data points may be weighted in an arbitrary manner.

Finally, p-values on each fitted parameter may be calculated and this can be used for variable selection of sparse models.