- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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

- 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,

- 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.

- 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()`

.

- 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.

- 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()`

.

- 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.

- 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.

- Switch to
`doParallel`

since`doMC`

is not packaged for Windows.

- 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()`

.

- 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

- M. Colombo, E. Valo, S.J. McGurnaghan et al., Biomarkers associated with progression of renal disease in type 1 diabetes,

- First release.