NEWS.md

In SeBR: Semiparametric Bayesian Regression Analysis

SeBR 1.1.0

Improvements to previous functionality

• Added bb() to sample from the Bayesian bootstrap (BB) posterior more efficiently.
• Added a fixedX case for when the covariates are fixed (not random), which also improves computing time for all semiparametric regression functions.
• Since location (intercept) and scale (error standard deviation) are not identifiable in the general transformed regression model, these are no longer reported as coefficients/parameters.
• The posterior draws of the transformation post_g now report (g - intercept)/scale instead of g, which properly corresponds to the transformation under the location-scale identified model. Now, post_g can be compared directly to the "true" transformations from simulated data without any further location-scale matching.

Fewer dependencies

• fields and GpGp are only needed for sbgp() and bgp_bc().
• plyr is only needed for sblm_modelsel().
• statmod is only needed for sbqr() and bqr().
• quantreg is only needed for sbqr().
• spikeSlabGAM is only needed for sbsm() and bsm_bc().

New functions

• Added sblm_hs() for semiparametric regression with horseshoe priors.
• Added blm_bc_hs() for Box-Cox transformed regression with horseshoe priors.
• Added sblm_ssvs() for stochastic search variable selection for semiparametric regression with sparsity priors.
• Added sblm_modelsel() for model/variable selection for semiparametric regression with sparsity priors.
• Added hbb() function to sample from the hierarchical BB (HBB) posterior. concen_hbb() samples from the marginal posterior distribution of the HBB concentration parameters.

SeBR documentation built on June 17, 2025, 1:07 a.m.