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 forsbgp() and bgp_bc().
plyr is only needed forsblm_modelsel().
statmod is only needed for sbqr() andbqr().
quantreg is only needed for sbqr().
spikeSlabGAM is only needed for sbsm() andbsm_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.