bbqr 0.1.0

First release.

The package went through three internal revisions before this one. None was released anywhere, so there is no upgrade path to describe – but the decisions taken along the way are what set the defaults, and a default that was chosen against a measured alternative is worth knowing about when interpreting a fit. That is what the rest of this file records.

Continuous-response quantile regression

• The continuous adaptive lasso’s omega hyperprior now defaults to Gamma(0.1, 0.1), not Gamma(2, 2). Gamma(a, a) has mean 1 for every a, so this holds the prior mean fixed and only widens it – but the shape also fixes the density at the origin, and that is the operative change: shape > 1 sends the density to zero as omega -> 0, shape < 1 sends it to infinity. Since small omega means strong shrinkage, the old default forbade exactly the region the data want. Measured in Experiment C1’s 6 x 2 factorial (3,200 paired scenarios per cell, n = 1000): the posterior places ~23% of omega’s mass below 1e-3 and Gamma(2, 2) permitted none of it. Loosening moves RMSE 0.0895 -> 0.0843 (-5.8%), coverage 0.8008 -> 0.8234 and Winkler 0.7074 -> 0.6664; it wins at every tau and never trades one quantile against another.

  Propriety is unaffected. Proposition 1C requires only that the hyperparameters be strictly positive – there is no lower bound, and its normalising bound does not involve alpha_omega or beta_omega at all. Gamma(0.1, 0.1) is exactly as proper as Gamma(2, 2).

  Scope. This changes cbqr_alasso() only. The continuous lasso layer’s tau hyperprior and the binary hierarchy in prior() are unchanged: the factorial covered the continuous adaptive lasso alone, and the binary sensitivity study reached a different conclusion about what the binding costs. Results from earlier versions will differ; pass prior = list(alpha_omega = 2, beta_omega = 2) to reproduce them.

• New cbqr(), with cbqr_alasso(), cbqr_lasso() and cbqr_none(), fitting the three penalty layers to an observed continuous response, against three new Fortran kernels. The binary path is unchanged.

• There is no anchor and no sigma_fixed. A continuous response identifies the scale, so sigma is sampled every sweep and the anchors are misspecifications rather than competing conventions. The unpenalised kernel therefore gains a sigma draw, which its binary counterpart does not have at all.

• New q argument: the exponent on sigma in the local-scale prior rate, sigma^q / (2 lambda_j^2). The layer defaults differ, and this is inherited rather than chosen – the published lasso penalty is lambda|beta_j| (q = 0) and the published adaptive penalty is (sqrt(sigma)/lambda_j)|beta_j| (q = 1). In the binary model sigma is anchored, so the two give the same sampler and the difference is invisible; it is not invisible here. q = 2 is the response-scale equivariant choice, and draws sigma by slice sampling rather than conjugately.

• standardize defaults to TRUE and now scales y as well as X. No proper hierarchy is exactly response-scale equivariant, so fixing the scale is part of the specification rather than a preprocessing convenience. Draws are mapped back to the original units exactly.

• predict() on a cbqr fit returns the fitted conditional quantile. Note that the binary predict() returns P(y = 1 | x) from the ALD distribution function, which on a continuous fit saturates at 1 – cbqr objects get their own method for exactly this reason.

• plot() accepts sigma as a plottable parameter, which the binary model has nothing to show for.

The samplers are now tested against the posterior they claim to target

• Geweke’s joint-distribution test (Geweke 2004, JASA 99, 799-804) is added for the unpenalised binary kernel and for all three continuous kernels. Every other test in the suite checks a consequence of the sampler being right – constraints hold, coefficients are recovered, draws are finite. A kernel with a wrong full conditional passes all of them and still converges to the wrong distribution. This one draws the joint p(theta, y) twice, once ancestrally from the prior and once by a two-block Gibbs chain that uses the package’s own sweep as its theta transition, and the two agree only if that sweep leaves the posterior invariant.

• Largest z over eleven to fifteen moments at tau = 0.3, 0.5, 0.75: bbqr_none 1.36 / 1.25 / 1.18; cbqr_none 2.05 / 2.78 / 2.37; cbqr_lasso 1.66 / 1.67 / 1.61; cbqr_alasso 1.42 / 1.59 / 2.62. The last is the one the V5 correction lives in.

• The test is shown to have power rather than merely to pass: a prior shape perturbed from 4 to 5 reads 19.6 to 27.4, a quantile off by 0.05 reads 24.7, against controls of 1.8 to 2.9.

• The binary penalised kernels are still not covered. Their sweeps carry lambda^2, s, omega and delta between sweeps with no argument to initialise them, so a run cannot continue a chain. cbqr takes sigma_init, delta_init and omega_init/tau_init as prior components, which is why the continuous penalised kernels can be tested and their binary twins cannot.

• Both test files are skip_on_cran(). benchmarks/geweke_joint.R and benchmarks/geweke_joint_continuous.R are the full-power versions.

One package, one description

• The release decision is taken: continuous and binary stay in one package rather than splitting into siblings, because they share prior(), the hierarchies, the derivation and the kernels’ structure. DESCRIPTION’s Title drops “Binary” and its Description now covers both response types; ?bbqr-package gains a Two response families section; the README opens with the two entry points and gains a continuous section; and the vignette gains When the response is observed. Four of the package’s eight exported fitters used to appear nowhere outside NEWS.md.

• An audit of every help page against that change found sixteen statements that were wrong rather than merely thin, and they are corrected here. Among them: plot.cbqr() had no alue; the continuous kernels were described as having “no latent draw” when they still draw z and s every sweep (it is the latent response that goes); prior() was said to serve both families when cbqr() rejects it; and the continuous adaptive lasso at its default q = 1 reproduces Alhamzawi, Yu and Benoit (2012), not the 2023 thesis. The four cbqr pages gained examples, references and cross-links, so R CMD check now exercises the continuous code it previously skipped.

The default hierarchy is the one whose posterior exists

• prior() defaults to model = "v5" for every penalty. The published adaptive-lasso and lasso hierarchies (model = "v3") have no posterior: the log-uniform hyperprior on the global shrinkage scale (omega, or tau_h for the lasso) is not integrable along the ray where the scale, the lambda_j^2 and the latent s_j vanish together, for every prior on delta and at either anchor. "v5" replaces every improper component with a proper one – Gamma(2, 2) on the global scale and, for the lasso, on delta; a tau-calibrated normal prior on the intercept – so the posterior is proper with no condition on the design, on k, or on the anchor. "v3" remains available for reproducing earlier results and is labelled IMPROPER wherever it is printed.

• The default anchor is "sigma1" for every penalty (previously "free" for the adaptive lasso and the lasso). Holding sigma at 1 closes the scale ray the likelihood never identifies, gives the coefficients a stated scale, keeps the credible intervals from inheriting the width of an unidentified direction, and is the convention under which the "v5" intercept prior is exact. "free" – sigma sampled from its full conditional, as the derivations also allow – is one argument away and is a derived posterior under every model. bbqr_alasso() and bbqr_lasso() changed accordingly.

• A "v5" lasso prior now requires beta_delta > 0, as the adaptive lasso already did: a flat prior on delta is admissible only under "v3", where it is the published specification.

Provenance on the fitted object

• print() and summary() report which hierarchy a fit sampled and flag a chain that does not target a derived posterior (a projection anchor combined with "v4" or "v5"). Objects saved by earlier builds carry no model and print no hierarchy line.

Documentation

• prior() documents model for all three penalties and the lasso’s alpha_tau/beta_tau; bbqr_alasso() documents kappa_lam; bbqr() gains a Derivation version section and documents the model, derived and b0_prior components of the fit. The clip-site count is eighteen, not fifteen.

• The README and the vignette describe the two hierarchies and the new defaults.

• The help pages now render their code spans, emphasis and cross-links (roxygen markdown was never switched on, so these appeared as literal backticks, asterisks and brackets), and the Kozumi and Kobayashi (2011) DOI is corrected.

Benchmarks

• The drivers that predate the hierarchy split (Experiment_v4.R through Experiment_v9.R, the real-data and churn benchmarks, and diagnose_hyperparams.R) now pass model = "v3" explicitly, so re-running them reproduces what they reported rather than picking up the new default.

Where it started

• The first working version fitted adaptive-lasso, lasso and unpenalised binary quantile regression, with five identification anchors behind one interface. Everything above was built on top of that, and none of it reached CRAN before this release.