Introduction and overview | What problem does WSwavelet solve? | WSwavelet in one picture | Installation and first example | Installation | A reproducible synthetic signal | Fitting the primary Gaussian version | Statistical model | Wavelet-domain observation model | Wendland and semicircle slab densities | Spike-and-slab prior | Resolution-dependent slab weights | Data-adaptive support scale | Posterior shrinkage | The coefficient-level posterior | Inspecting one shrinkage curve | Using level_posterior() independently | Likelihood options and model variants | Laplace working likelihood | Constant and endpoint slab specifications | Independent utility functions | Estimating the noise scale | Numerical checks | Practical guidance and troubleshooting | Choosing the likelihood | Choosing the quadrature order | Input length and boundaries | Interpreting the support scale | Reproducibility | Citation

Introduction and overview | What problem does WSwavelet solve? | WSwavelet in one picture | Installation and first example | Installation | A reproducible synthetic signal | Fitting the primary Gaussian version | Statistical model | Wavelet-domain observation model | Wendland and semicircle slab densities | Spike-and-slab prior | Resolution-dependent slab weights | Data-adaptive support scale | Posterior shrinkage | The coefficient-level posterior | Inspecting one shrinkage curve | Using level_posterior() independently | Likelihood options and model variants | Laplace working likelihood | Constant and endpoint slab specifications | Independent utility functions | Estimating the noise scale | Numerical checks | Practical guidance and troubleshooting | Choosing the likelihood | Choosing the quadrature order | Input length and boundaries | Interpreting the support scale | Reproducibility | Citation

Introduction and overview | What problem does S3VS solve? | Model families supported | S3VS in one picture | Use of package functions with examples | Installation | The main function S3VS() | Example 1: Linear model | Example 2: Binary classification model | Example 3: Survival model | Advanced usage: building blocks and customization | Choosing leading variables: get_leadvars*() | Constructing leading sets: get_leadsets() | Selection within a set: VS_method*() | Aggregating selections: select_vars() | Removing variables: remove_vars() | Response updating: update_y*() | Stopping rule: looprun() | Practical guidance and troubleshooting | Choosing method_sel and method_rem | Highly correlated predictors: | Computational tips for AFT: | Reproducibility