R: Hierarchical Shrinkage Stan Models for Biomarker Selection

# Hierarchical Shrinkage Stan Models for Biomarker Selection

## Documentation for package ‘hsstan’ version 0.8.2

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

hsstan-package | Hierarchical shrinkage Stan models for biomarker selection
bayes_R2 | Bayesian and LOO-adjusted R-squared
bayes_R2.hsstan | Bayesian and LOO-adjusted R-squared
diabetes | Diabetes data with interaction terms
hsstan | Hierarchical shrinkage models
kfold | K-fold cross-validation
kfold.hsstan | K-fold cross-validation
log_lik | Pointwise log-likelihood
log_lik.hsstan | Pointwise log-likelihood
loo | Predictive information criteria for Bayesian models
loo.hsstan | Predictive information criteria for Bayesian models
loo_R2 | Bayesian and LOO-adjusted R-squared
loo_R2.hsstan | Bayesian and LOO-adjusted R-squared
nsamples | Number of posterior samples
nsamples.hsstan | Number of posterior samples
plot.projsel | Plot of relative explanatory power of predictors
posterior_interval | Posterior uncertainty intervals
posterior_interval.hsstan | Posterior uncertainty intervals
posterior_linpred | Posterior distribution of the linear predictor
posterior_linpred.hsstan | Posterior distribution of the linear predictor
posterior_performance | Posterior measures of performance
posterior_predict | Posterior predictive distribution
posterior_predict.hsstan | Posterior predictive distribution
posterior_summary | Posterior summary
posterior_summary.default | Posterior summary
posterior_summary.hsstan | 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
waic | Predictive information criteria for Bayesian models
waic.hsstan | Predictive information criteria for Bayesian models