hsstan 0.8 (29 June 2020)
Major Changes
- Add the
sub.idx
option to posterior_performance()
to select the observations to be used in the computation of the performance measures.
- Add the
start.from
option to run projsel()
to start the selection procedure from a submodel different from the set of unpenalized covariates.
- Allow interaction terms in the formula for unpenalized covariates.
- Speed up matrix multiplications in
posterior_linpred()
and projsel()
: this also benefits all other functions that use posterior_linpred()
, such as log_lik()
, posterior_predict()
, posterior_performance()
and others.
Smaller Changes and Bug Fixes
- Fix parallelized loop boundaries in
posterior_performance()
for Windows.
- Speed up
posterior_performance()
for gaussian models.
- Handle correctly the case in which a variable is mentioned both among the unpenalized covariates and the penalized predictors.
- Fix bug in handling of a factor variable with multiple levels in the set of penalized predictors.
- Use the correct sigma term in the computation of the elpd for gaussian models.
- Allow running
projsel()
on models with no penalized predictors.
hsstan 0.7 (1 May 2020)
Major Changes
- Speed up all models up to 4-5 times by using Stan’s
normal_id_glm()
and bernoulli_logit_glm()
.
- Use a simpler parametrization of the regularized horseshoe prior.
Smaller Changes and Bug Fixes
- Allow using the
iter
and warmup
options in kfold()
.
- Switch to
rstantools
2.0.0.
- Fix bug in the use of the
slab.scale
parameter of hsstan()
, as it was not squared in the computation of the slab component of the regularized horseshoe prior. The default value of 2 in the current version corresponds to using the value 4 in versions 0.6 and earlier.
hsstan 0.6 (14 September 2019)
Major Changes
- First version to be available on CRAN.
- Add the
kfold()
and posterior_summary()
functions.
- Implement parallelization on Windows using
parallel::parLapply()
.
- Remove the deprecated
sample.stan()
and sample.stan.cv()
.
- Replace
get.cv.performance()
with posterior_performance()
.
- Report the intercept-only results from
projsel()
.
- Add options to
plot.projsel()
for choosing the number of points to plot and whether to show a point for the null model.
Smaller Changes and Bug Fixes
- Cap to 4 the number of cores used by default when loading the package.
- Don’t change an already set
mc.cores
option when loading the package.
- Drop the internal horseshoe parameters from the stanfit object by default.
- Speed up the parallel loops in the projection methods.
- Evaluate the full model in
projsel()
only if selection stopped early.
- Rename the
max.num.pred
argument of projsel()
to max.iters
.
- Validate the options passed to
rstan::sampling()
.
- Expand the documentation and add examples.
Notes
- This version was used in:
- M. Colombo, S.J. McGurnaghan, L.A.K. Blackbourn et al., Comparison of serum and urinary biomarker panels with albumin creatinin ratio in the prediction of renal function decline in type 1 diabetes, Diabetologia (2020): 63 (4) 788-798. https://doi.org/10.1007/s00125-019-05081-8
hsstan 0.5 (11 August 2019)
Major Changes
- Update the interface of
hsstan()
.
- Don’t standardize the data inside
hsstan()
.
- Implement the thin QR decomposition and use it by default.
- Replace uses of
foreach()
/%dopar%
with parallel::mclapply()
.
- Add the
posterior_interval()
, posterior_linpred()
, posterior_predict()
log_lik()
, bayes_R2()
, loo_R2()
and waic()
functions.
- Change the folds format from a list of indices to a vector of fold numbers.
Smaller Changes and Bug Fixes
- Add the
nsamples()
and sampler.stats()
functions.
- Use
crossprod()
/tcrossprod()
instead of matrix multiplications.
- Don’t return the posterior mean of sigma in the hsstan object.
- Store covariates and biomarkers in the hsstan object.
- Remove option for using variational Bayes.
- Add option to control the number of Markov chains run.
- Fix computation of fitted values for logistic regression.
- Fix two errors in the computation of the elpd in
fit.submodel()
.
- Store the original data in the hsstan object.
- Use
log_lik()
instead of computing and storing the log-likelihood in Stan.
- Allow the use of regular expressions for
pars
in summary.hsstan()
.
hsstan 0.4 (24 July 2019)
Major Changes
- Merge
sample.stan()
and sample.stan.cv()
into hsstan()
.
- Implement the regularized horseshoe prior.
- Add a
loo()
method for hsstan objects.
- Change the default
adapt.delta
argument for base models from 0.99 to 0.95.
- Decrease the default
scale.u
from 20 to 2.
Smaller Changes and Bug Fixes
- Add option to set the seed of the random number generator.
- Add computation of log-likelihoods in the generated quantities.
- Use
scale()
to standardize the data in sample.stan.cv()
.
- Remove the standardize option so that data is always standardized.
- Remove option to create a png file from
plot.projsel()
.
- Make
get.cv.performance()
work also on a non-cross-validated hsstan object.
- Add
print()
and summary()
functions for hsstan objects.
- Add options for horizontal and vertical label adjustment in
plot.projsel()
.
hsstan 0.3 (4 July 2019)
Major Changes
- Add option to set the
adapt_delta
parameter and change the default for all models from 0.95 to 0.99.
- Allow to control the prior scale for the unpenalized variables.
Smaller Changes and Bug Fixes
- Add option to control the number of iterations.
- Compute the elpd instead of the mlpd in the projection.
- Fix bug in the assignment of readable variable names.
- Don’t compute the predicted outcome in the generated quantities block.
hsstan 0.2 (13 November 2018)
Major Changes
- Switch to
doParallel
since doMC
is not packaged for Windows.
Smaller Changes and Bug Fixes
- Enforce the direction when computing the AUC.
- Check that there are no missing values in the design matrix.
- Remove code to disable clipping of text labels from
plot.projsel()
.
Notes
- This version was used in:
- M. Colombo, E. Valo, S.J. McGurnaghan et al., Biomarkers associated with progression of renal disease in type 1 diabetes, Diabetologia (2019) 62 (9): 1616-1627. https://doi.org/10.1007/s00125-019-4915-0
- A. Spiliopoulou, M. Colombo, D. Plant et al., Association of response to TNF inhibitors in rheumatoid arthritis with quantitative trait loci for CD40 and CD39, Annals of the Rheumatic Diseases (2019) 78: 1055-1061. https://doi.org/10.1136/annrheumdis-2018-214877
hsstan 0.1 (14 June 2018)