Man pages for hsstan
Hierarchical Shrinkage Stan Models for Biomarker Selection
| bayes_R2.hsstan | Bayesian and LOO-adjusted R-squared |
| choose.next | Next variable to enter the current submodel |
| diabetes | Diabetes data with interaction terms |
| fit.submodel | Compute the fit of a submodel |
| hsstan | Hierarchical shrinkage models |
| hsstan-package | Hierarchical shrinkage Stan models for biomarker selection |
| kfold.hsstan | K-fold cross-validation |
| lm_proj | Compute projections of full predictors on to subspace of... |
| log_lik.hsstan | Pointwise log-likelihood |
| loo.hsstan | Predictive information criteria for Bayesian models |
| nsamples.hsstan | Number of posterior samples |
| plot.projsel | Plot of relative explanatory power of predictors |
| posterior_interval.hsstan | Posterior uncertainty intervals |
| posterior_linpred.hsstan | Posterior distribution of the linear predictor |
| posterior_performance | Posterior measures of performance |
| posterior_predict.hsstan | Posterior predictive distribution |
| posterior_summary | Posterior summary |
| print.hsstan | Print a summary for the fitted model |
| projsel | Forward selection minimizing KL-divergence in projection |
| sampler.stats | Sampler statistics |
| summary.hsstan | Summary for the fitted model |
