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.