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bb() to sample from the Bayesian bootstrap (BB)
posterior more efficiently.fixedX case for when the covariates are fixed
(not random), which also improves computing time for all semiparametric
regression functions.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.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().sblm_hs() for semiparametric regression with
horseshoe priors.blm_bc_hs() for Box-Cox transformed regression
with horseshoe priors.sblm_ssvs() for stochastic search variable
selection for semiparametric regression with sparsity priors.sblm_modelsel() for model/variable selection for
semiparametric regression with sparsity priors.hbb() function to sample from the hierarchical BB
(HBB) posterior. concen_hbb() samples from the marginal
posterior distribution of the HBB concentration parameters.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.