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)
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:
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)kfold() for stan_gamm4()
models that used random argument (#435)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)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)
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
predictorsprior.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 codeposterior_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 submodelCompatible 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 betterThe 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
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
modelsreloo() if data was not specifiedpp_validate() that was only introduced on
GitHubUses 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 matrixstan_biglm() function that somewhat supports
biglm::biglm
as.array() method for stanreg objects
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