# Current development version

### Smaller Changes and Bug Fixes

# hsstan 0.8.2 (13 January 2024)

### Smaller Changes and Bug Fixes

- Update deprecated syntax for future rstan compatibility (thanks to

  Andrew Johnson for the patch).

# hsstan 0.8.1 (16 September 2021)

### Smaller Changes and Bug Fixes

- Fix bug in `projsel()` if the number of observations in the dataset is

  smaller than both the number of available predictors and the maximum

  number of iterations in the selection procedure.

- Add workaround for

  [rstantools issue #77](https://github.com/stan-dev/rstantools/issues/77)

  to make the base models run again correctly with the compilation changes

  introduced in `rstan` 2.21.

- Add `RcppParallel` to Imports and LinkingTo, as future versions of

  `rstan` require to link to the Intel TBB library.

- Improve validation of scalar inputs.

# 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.

### Notes

- This version was used in:

  - [M. Colombo][mcol], A. Asadi Shehni, I. Thoma et al.,

    Quantitative levels of serum N-glycans in type 1 diabetes and their

    association with kidney disease,

    [_Glycobiology_ (2021) 31 (5): 613-623](https://doi.org/10.1093/glycob/cwaa106).

# 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][mcol], 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][mcol], 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][athina], [M. Colombo][mcol], 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)

- First release.

[mcol]:   https://github.com/mcol

[athina]: https://precmed.cphs.mvm.ed.ac.uk/athina/