bolasso vs Tailscale

bolasso implements the bootstrapped lasso, refitting a regularized regression across bootstrap replicates and selecting variables by how consistently they survive. The 0.3.0 release reshaped it: a fast argument computes one cross-validated lambda on the full dataset instead of cross-validating inside every replicate, and selected_variables() gained a choice between the variable inclusion probability rule and a quantile rule based on bootstrap confidence intervals. Since then 0.4.0 exposed the bootstrap indices through bootstrap_samples(), and 0.5.0 extended the whole surface to multinomial responses, returning one list element per outcome level.

The package spent 2022 dormant after its initial releases and has been actively developed since late 2024, moving from a single algorithm toward a workbench. The additions cluster around inspection rather than estimation: tidy() for bootstrap-level coefficients, plot_selection_thresholds() for selection stability across thresholds, plot_selected_variables() for the surviving covariates, and now the extracted bootstrap indices. Documented gaps remain, with mgaussian unsupported and multinomial prediction limited to class output.

The two stated limitations in 0.5.0 - no mgaussian family and class-only multinomial prediction - are the most likely next targets, since the maintainer flagged both as possible later additions.