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NEWS

• Fix bug where loo() with k_threshold argument specified would error if the model formula was a string instead of a formula object. (#454)

• Fix bug where loo() with k_threshold argument specified would error for models fit with stan_polr(). (#450)

• Fix bug where stan_aov() would use the wrong singular.ok logic. (#448)

• Fix bug where contrasts info was dropped when subsetting the model matrix in stan_glm(). (#459)

• Fix bug where stan_glmer() would error if prior_aux=NULL. (#482)

• posterior_predict() and posterior_epred() don’t error with newdata for intercept only models by allowing data frames with 0 columns and multiple rows. (#492)

• New vignette on AB testing. (#409)

• stan_jm() gains an offset term for the longitudinal submodel. (#415, @pamelanluna)

• Effective number of parameters are computed for K-fold CV not just LOO CV. (#462)

• stan_clogit() now allows outcome variable to be a factor. (#520)

• Compatible with rstan v2.21.1
• Consistent with new book Regression and Other Stories

• stan_jm() is not available for 32bit Windows

• Some improvements to prior distributions, as described in detail in the vignette Prior Distributions for rstanarm Models and book Regression and Other Stories. These changes shouldn’t cause any existing code to error, but default priors have changed in some cases:

  • default prior on intercept is still Gaussian but the way the location and scale are determined has been updated (#432)
  • autoscale argument to functions like normal(), student_t(), etc., now defaults to FALSE except when used by default priors (default priors still do autoscalinng). This makes it simpler to specify non-default priors. (#432)

• Fixed error in kfold() for stan_gamm4() models that used random argument (#435)
• Fixed error in posterior_predict() and posterior_linpred() when using newdata with family = mgcv::betar (#406, #407)
• singular.ok now rules out singular design matrices in stan_lm() (#402)
• Fix a potential error when data is a data.table object (#434, @danschrage)

• New method posterior_epred() returns the posterior distribution of the conditional expectation, which is equivalent to (and may eventually entirely replace) setting argument transform=TRUE with posterior_linpred(). (#432)

• Added convenience functions logit() and invlogit() that are just wrappers for qlogis() and plogis(). These were previously provided by the arm package. (#432)

• Allow the vignettes to knit on platforms that do not support version 2 of RMarkdown

• src/Makevars{.win} now uses a more robust way to find StanHeaders

• Fixed bug where ranef() and coef() methods for glmer-style models printed the wrong output for certain combinations of varying intercepts and slopes.

• Fixed a bug where posterior_predict() failed for stan_glmer() models estimated with family = mgcv::betar.

• Fixed bug in bayes_R2() for bernoulli models. (Thanks to @mcol)

• loo_R2() can now be called on the same fitted model object multiple times with identical (not just up to rng noise) results. (Thanks to @mcol)

• New vignette on doing MRP using rstanarm. (Thanks to @lauken13)

• 4x speedup for most GLMs (stan_glm()) and GAMs (stan_gamm4() without random argument). This comes from using Stan’s new compound _glm functions (normal_id_glm, bernoulli_logit_glm, poisson_log_glm, neg_binomial_2_log_glm) under the hood whenever possible. (Thanks to @avehtari and @VMatthijs)

• compare_models() is deprecated in favor of loo_compare() to keep up with the loo package (loo::loo_compare())

• The kfold() method now has a cores argument and parallelizes by fold rather than by Markov chain (unless otherwise specified), which should be much more efficient when many cores are available.

• For stan_glm() with algorithm='optimizing', Pareto smoothed importance sampling (arxiv.org/abs/1507.02646, mc-stan.org/loo/reference/psis.html) is now used to diagnose and improve inference (see https://avehtari.github.io/RAOS-Examples/BigData/bigdata.html). This also now means that we can use PSIS-LOO also when algorithm='optimizing'. (Thanks to @avehtari)

• For stan_glm() the "meanfield" and "fullrank" ADVI algorithms also include the PSIS diagnostics and adjustments, but so far we have not seen any example where these would be better than optimzation or MCMC.

• stan_clogit() now works even when there are no common predictors
• prior.info() works better with models produced by stan_jm() and stan_mvmer()

• stan_glm() (only) gets a mean_PPD argument that when FALSE avoids drawing from the posterior predictive distribution in the Stan code
• posterior_linpred() now works even if the model was estimated with algorithm = "optimizing"

• stan_jm() and stan_mvmer() now correctly include the intercept in the longitudinal submodel

• Compatible with loo package version >= 2.0

• QR = TRUE no longer ignores the autoscale argument and has better behavior when autoscale = FALSE

• posterior_linpred() now has a draws argument like for posterior_predict()

• Dynamic predictions are now supported in posterior_traj() for stan_jm models.

• More options for K-fold CV, including manually specifying the folds or using helper functions to create them for particular model/data combinations.

Minor release for build fixes for Solaris and avoiding a test failure

Lots of good stuff in this release.

• stan_polr() and stan_lm() handle the K = 1 case better

• The prior_aux arguments now defaults to exponential rather than Cauchy. This should be a safer default.

• The Stan programs do not drop any constants and should now be safe to use with the bridgesampling package

• hs() and hs_plus() priors have new defaults based on a new paper by Aki Vehtari and Juho Piironen

• stan_gamm4() is now more closely based on mgcv::jagam(), which may affect some estimates but the options remain largely the same

• The product_normal() prior permits df = 1, which is a product of … one normal variate

• The build system is more conventional now. It should require less RAM to build from source but it is slower unless you utilize parallel make and LTO

• stan_jm() and stan_mvmer() contributed by Sam Brilleman

• bayes_R2() method to calculate a quantity similar to \(R^2\)

• stan_nlmer(), which is similar to lme4::nlmer but watch out for multimodal posterior distributions

• stan_clogit(), which is similar to survival::clogit but accepts lme4-style group-specific terms

• The mgcv::betar family is supported for the lme4-like modeling functions, allowing for beta regressions with lme4-style group terms and / or smooth nonlinear functions of predictors

• Fix to stan_glmer() Bernoulli models with multiple group-specific intercept terms that could result in draws from the wrong posterior distribution

• Fix bug with contrasts in stan_aov() (thanks to Henrik Singmann)

• Fix bug with na.action in stan_glmer() (thanks to Henrik Singmann)

Minor release with only changes to allow tests to pass on CRAN

• Fix for intercept with identity or square root link functions for the auxiliary parameter of a beta regression

• Fix for special case where only the intercepts vary by group and a non-default prior is specified for their standard deviation

• Fix for off-by-one error in some lme4-style models with multiple grouping terms

• New methods loo_linpred(), loo_pit(), loo_predict(), and loo_predictive_interval()

• Support for many more plotfuns in pp_check() that are implemented in the bayesplot package

• Option to compute latent residuals in stan_polr() (Thanks to Nate Sanders)

• The pairs plot now uses the ggplot2 package

• VarCorr() could return duplicates in cases where a stan_{g}lmer model used grouping factor level names with spaces

• The pairs() function now works with group-specific parameters

• The stan_gamm4() function works better now

• Fix a problem with factor levels after estimating a model via stan_lm()

• New model-fitting function(s) stan_betareg() (and stan_betareg.fit()) that uses the same likelihoods as those supported by the betareg() function in the betareg package (Thanks to Imad Ali)

• New choices for priors on coefficients: laplace(), lasso(), product_normal()

• The hs() and hs_plus() priors now have new global_df and global_scale arguments

• stan_{g}lmer() models that only have group-specific intercept shifts are considerably faster now

• Models with Student t priors and low degrees of freedom (that are not 1, 2, or 4) may work better now due to Cornish-Fisher transformations

• Many functions for priors have gained an autoscale argument that defaults to TRUE and indicates that rstanarm should make internal changes to the prior based on the scales of the variables so that they default priors are weakly informative

• The new compare_models() function does more extensive checking that the models being compared are compatible

• The prior_ops argument to various model fitting functions is deprecated and replaced by a the prior_aux argument for the prior on the auxiliary parameter of various GLM-like models

• Fix bug in reloo() if data was not specified
• Fix bug in pp_validate() that was only introduced on GitHub

• Uses the new bayesplot and rstantools R packages

• The new prior_summary() function can be used to figure out what priors were actually used

• stan_gamm4() is better implemented, can be followed by plot_nonlinear(), posterior_predict() (with newdata), etc.

• Hyperparameters (i.e. covariance matrices in general) for lme4 style models are now returned by as.matrix() and as.data.frame()

• pp_validate() can now be used if optimization or variational Bayesian inference was used to estimate the original model

• Fix for bad bug in posterior_predict() when factor labels have spaces in lme4-style models

• Fix when weights are used in Poisson models

• posterior_linpred() gains an XZ argument to output the design matrix

• Requiring manually specifying offsets when model has an offset and newdata is not NULL

• stan_biglm() function that somewhat supports biglm::biglm

• as.array() method for stanreg objects

• Works with devtools now

• k_threshold argument to loo() to do PSIS-LOO+

• kfold() for K-fold CV

• Ability to use sparse X matrices (slowly) for many models if memory is an issue

• posterior_predict() with newdata now works correctly for ordinal models

• stan_lm() now works when intercept is omitted

• stan_glmer.fit() no longer permit models with duplicative group-specific terms since they don’t make sense and are usually a mistake on the user’s part

• posterior_predict() with lme4-style models no longer fails if there are spaces or colons in the levels of the grouping variables

• posterior_predict() with ordinal models outputs a character matrix now

• pp_validate() function based on the BayesValidate package by Sam Cook

• posterior_vs_prior() function to visualize the effect of conditioning on the data

• Works (again) with R versions back to 3.0.2 (untested though)

• Fix problem with models that had group-specific coefficients, which were mislabled. Although the parameters were estimated correctly, users of previous versions of rstanarm should run such models again to obtain correct summaries and posterior predictions. Thanks to someone named Luke for pointing this problem out on stan-users.

• Vignettes now view correctly on the CRAN webiste thanks to Yihui Xie

• Fix problem with models without intercepts thanks to Paul-Christian Buerkner

• Fix problem with specifying binomial ‘size’ for posterior_predict using newdata

• Fix problem with lme4-style formulas that use the same grouping factor multiple times

• Fix conclusion in rstanarm vignette thanks to someone named Michael

• Group-specific design matrices are kept sparse throughout to reduce memory consumption

• The log_lik() function now has a newdata argument

• New vignette on hierarchical partial pooling

Initial CRAN release

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