Releases: paul-buerkner/brms
Releases · paul-buerkner/brms
brms 2.14.0
New Features
- Experimentally support within-chain parallelizaion via
reduce_sum
using argumentthreads
inbrm
thanks to Sebastian Weber. (#892) - Add algorithm
fixed_param
to sample from fixed parameter values. (#973) - No longer remove
NA
values indata
if there are unused because of
thesubset
addition argument. (#895) - Combine
by
variables and within-group correlation matrices
in group-level terms. (#674) - Add argument
robust
to thesummary
method. (#976) - Parallelize evaluation of the
posterior_predict
andlog_lik
methods via argumentcores
. (#819) - Compute effective number of parameters in
kfold
. - Show prior sources and vectorization in the
print
output
ofbrmsprior
objects. (#761) - Store unused variables in the model's data frame via
argumentunused
of functionbrmsformula
. - Support posterior mean predictions in
emmeans
via
dpar = "mean"
thanks to Russell V. Lenth. (#993) - Improve control of which parameters should be saved via
functionsave_pars
and corresponding argument inbrm
. (#746) - Add method
posterior_smooths
to computing predictions
of individual smooth terms. (#738) - Allow to display grouping variables in
conditional_effects
using theeffects
argument. (#1012)
Other Changes
- Improve sampling efficiency for a lot of models by using Stan's
GLM-primitives even in non-GLM cases. (#984) - Improve sampling efficiency of multilevel models with
within-group covariances thanks to David Westergaard. (#977) - Deprecate argument
probs
in theconditional_effects
method
in favor of argumentprob
.
Bug Fixes
- Fix a problem in
pp_check
inducing wronger observation
orders in time series models thanks to Fiona Seaton. (#1007) - Fix multiple problems with
loo_moment_match
that prevented
it from working for some more complex models.
brms 2.13.5
New Features
- Support the Cox proportional hazards model for
time-to-event data via familycox
. (#230, #962) - Support method
loo_moment_match
, which can be used to
update aloo
object when Pareto k estimates are large.
Other Changes
- Improve the prediction behavior in post-processing methods
when sampling new levels of grouping factors via
sample_new_levels = "uncertainty"
. (#956)
Bug Fixes
- Fix minor problems with MKL on CRAN.
brms 2.13.3
brms 2.13.3
New Features
- Fix shape parameters across multiple monotonic terms via argument
id
in functionmo
to ensure conditionally monotonic effects. (#924) - Support package
rtdists
as additional backend ofwiener
distribution functions thanks to the help of Henrik Singmann. (#385)
Bug Fixes
- Fix generated Stan Code of models with improper global priors and
constant
priors on some coefficients thanks to Frank Weber. (#919) - Fix a bug in
conditional_effects
occuring for categorical
models with matrix predictors thanks to Jamie Cranston. (#933)
Other Changes
- Adjust behavior of the
rate
addition term so that it also
affects theshape
parameter innegbinomial
models thanks to
Edward Abraham. (#915) - Adjust the default inverse-gamma prior on length-scale parameters
of Gaussian processes to be less extreme in edge cases thanks
to Topi Paananen.
brms 2.13.0
New Features
- Constrain ordinal thresholds to sum to zero via argument
threshold
in ordinal family functions thanks to the help of
Marta Kołczyńska. - Support
posterior_linpred
as method inconditional_effects
. - Use
std_normal
in the Stan code for improved efficiency. - Add arguments
cor
,id
, andcov
to the functionsgr
and
mm
for easy specification of group-level correlation structures. - Improve workflow to feed back brms-created models which were
fitted somewhere else back into brms. (#745) - Improve argument
int_conditions
inconditional_effects
to
work for all predictors not just interactions. - Support multiple imputation of data passed via
data2
in
brm_multiple
. (#886) - Fully support the
emmeans
package thanks to the help
of Russell V. Lenth. (#418) - Control the within-block position of Stan code added via
stanvar
using theposition
argument.
Bug Fixes
- Fix issue in Stan code of models with multiple
me
terms
thanks to Chris Chatham. (#855, #856) - Fix scaling problems in the estimation of ordinal models with
multiple threshold vectors thanks to Marta Kołczyńska and
Rok Češnovar. - Allow usage of
std_normal
inset_prior
thanks to Ben Goodrich. (#867) - Fix Stan code of distributional models with
weibull
,frechet
,
orinverse.gaussian
families thanks to Brian Huey and Jack Caster. (#879) - Fix Stan code of models which are truncated and weighted at the
same time thanks to Michael Thompson. (#884) - Fix Stan code of multivariate models with custom families and
data variables passed to the likelihood thanks to Raoul Wolf. (#906)
Other Changes
- Reduce minimal scale of several default priors from 10 to 2.5.
The resulting priors should remain weakly informative. - Automatically group observations in
gp
for increased efficiency. - Rename
parse_bf
tobrmsterms
and deprecate the former function. - Rename
extract_draws
toprepare_predictions
and deprecate
the former function. - Deprecate using a model-dependent
rescor
default. - Deprecate argument
cov_ranef
inbrm
and related functions. - Improve several internal interfaces. This should not have any
user-visible changes. - Simplify the parameterization of the horseshoe prior thanks
to Aki Vehtari. (#873) - Store fixed distributional parameters as regular draws so that
they behave as if they were estimated in post-processing methods.
brms 2.12.0
New Features
- Fix parameters to constants via the
prior
argument. (#783) - Specify autocorrelation terms directly in the model formula. (#708)
- Translate integer covariates in non-linear formulas to integer
arrays in Stan. - Estimate
sigma
in combination with fixed correlation matrices
via autocorrelation termfcor
. - Use argument
data2
inbrm
and related functions to pass
data objects which cannot be passed viadata
. The usage ofdata2
will be extended in future versions. - Compute pointwise log-likelihood values via
log_lik
for
non-factorizable Student-t models. (#705)
Bug Fixes
- Fix output of
posterior_predict
formultinomial
models
thanks to Ivan Ukhov. - Fix selection of group-level terms via
re_formula
in
multivariate models thanks to Maxime Dahirel. (#834) - Enforce correct ordering of terms in
re_formula
thanks to @ferberkl. (#844) - Fix post-processing of multivariate multilevel models
when multiple IDs are used for the same grouping factor
thanks to @lott999. (#835) - Store response category names of ordinal models in the
output ofposterior_predict
again thanks to Mattew Kay. (#838) - Handle
NA
values more consistently inposterior_table
thanks to Anna Hake. (#845) - Fix a bug in the Stan code of models with multiple monotonic
varying effects across different groups thanks to Julian Quandt.
Other Changes
- Rename
offset
variables tooffsets
in the generated Stan
code as the former will be reserved in the new stanc3 compiler.
brms 2.11.1
Bug Fixes
- Fix version requirement of the
loo
package. - Fix effective sample size note in the
summary
output. (#824) - Fix an edge case in the handling of covariates in
special terms thanks to Andrew Milne. (#823) - Allow restructuring objects multiple times with different
brms versions thanks to Jonathan A. Nations. (#828) - Fix validation of ordered factors in
newdata
thanks to Andrew Milne. (#830)
brms 2.11.0
New Features
- Support grouped ordinal threshold vectors via addition
argumentresp_thres
. (#675) - Support method
loo_subsample
for performing approximate
leave-one-out cross-validation for large data. - Allow storing more model fit critera via
add_criterion
. (#793)
Bug Fixes
- Fix prediction uncertainties of new group levels for
sample_new_levels = "uncertainty"
thanks to Dominic Magirr. (#779) - Fix problems when using
pp_check
on
censored models thanks to Andrew Milne. (#744) - Fix error in the generated Stan code of multivariate
zero_inflated_binomial
models thanks to Raoul Wolf. (#756) - Fix predictions of spline models when using addition
argumentsubset
thanks to Ruben Arslan. - Fix out-of-sample predictions of AR models when predicting
more than one step ahead. - Fix problems when using
reloo
orkfold
with CAR models. - Fix problems when using
fitted(..., scale = "linear")
with
multinomial models thanks to Santiago Olivella. (#770) - Fix problems in the
as.mcmc
method for thinned models
thanks to @hoxo-m. (#811) - Fix problems in parsing covariates of special effects terms
thanks to Riccardo Fusaroli (#813)
Other Changes
- Rename
marginal_effects
toconditional_effects
and
marginal_smooths
toconditional_smooths
. (#735) - Rename
stanplot
tomcmc_plot
. - Add method
pp_expect
as an alias offitted
. (#644) - Model fit criteria computed via
add_criterion
are now
stored in thebrmsfit$criteria
slot. - Deprecate
resp_cat
in favor ofresp_thres
. - Deprecate specifying global priors on regression coefficients
in categorical and multivariate models. - Improve names of weighting methods in
model_weights
. - Deprecate reserved variable
intercept
in favor ofIntercept
. - Deprecate argument
exact_match
in favor offixed
. - Deprecate functions
add_loo
andadd_waic
in favor ofadd_criterion
.
brms 2.10.0
New Features
- Improve convergence diagnostics in the
summary
output. (#712) - Use primitive Stan GLM functions whenever possible. (#703)
- Pass real and integer data vectors to custom families via
the addition argumentsvreal
andvint
. (#707) - Model compound symmetry correlations via
cor_cosy
. (#403) - Predict
sigma
in combination with several
autocorrelation structures. (#403) - Use addition term
rate
to conveniently handle
denominators of rate responses in log-linear models. - Fit BYM2 CAR models via
cor_car
thanks to the case study
and help of Mitzi Morris.
Other Changes
- Substantially improve the sampling efficiency of SAR models
thanks to the GitHub user aslez. (#680) - No longer allow changing the boundaries
of autocorrelation parameters. - Set the number of trials to 1 by default in
marginal_effects
if not specified otherwise. (#718) - Use non-standard evaluation for addition terms.
- Name temporary intercept parameters more consistently
in the Stan code.
Bug Fixes
- Fix problems in the post-processing of
me
terms with
grouping factors thanks to the GitHub user tatters. (#706) - Allow grouping variables to start with a dot
thanks to Bruno Nicenboim. (#679) - Allow the
horseshoe
prior in categorical and
related models thanks to the Github user tatters. (#678) - Fix extraction of prior samples for overall intercepts in
prior_samples
thanks to Jonas Kristoffer Lindelov. (#696) - Allow underscores to be used in category names
of categorical responses thanks to Emmanuel Charpentier. (#672) - Fix Stan code of multivariate models with multi-membership
terms thanks to the Stan discourse user Pia. - Improve checks for non-standard variable names
thanks to Ryan Holbrook. (#721) - Fix problems when plotting facetted spaghetti plots
viamarginal_smooths
thanks to Gavin Simpson. (#740)
brms 2.9.0
New Features
- Specify non-linear ordinal models. (#623)
- Allow to fix thresholds in ordinal mixture models (#626)
- Use the
softplus
link function in various families. (#622) - Use QR decomposition of design matrices via argument
decomp
ofbrmsformula
thanks to the help of Ben Goodrich. (#640) - Define argument
sparse
separately for each model formula. - Allow using
bayes_R2
andloo_R2
with ordinal models. (#639) - Support
cor_arma
in non-normal models. (#648)
Other Changes
- Change the parameterization of monotonic effects to improve their interpretability. (#578)
- No longer support the
cor_arr
andcor_bsts
correlation structures after a year of deprecation. - Refactor internal evaluation of special predictor terms.
- Improve penality of splines thanks to Ben Goodrich and Ruben Arslan.
Bug Fixes
- Fix a problem when applying
marginal_effects
to measurement error models thanks to Jonathan A. Nations. (#636) - Fix computation of log-likelihood values for weighted mixture models.
- Fix computation of fitted values for truncated lognormal and weibull models.
- Fix checking of response boundaries for models with missing values thanks to Lucas Deschamps.
- Fix Stan code of multivariate models with both residual correlations and missing value terms thanks to Solomon Kurz.
- Fix problems with interactions of special terms when extracting variable names in
marginal_effects
. - Allow compiling a model in
brm_multiple
without sampling thanks to Will Petry. (#671)
brms 2.8.0
New Features
- Fit multinomial models via family
multinomial
. (#463) - Fit Dirichlet models via family
dirichlet
. (#463) - Fit conditional logistic models using the
categorical
and
multinomial
families together with non-linear formula syntax. (#560) - Choose the reference category of
categorical
and related
families via argumentrefcat
of the corresponding family functions. - Use different subsets of the data in different univariate parts
of a multivariate model via addition argumentsubset
. (#360) - Control the centering of population-level design matrices
via argumentcenter
ofbrmsformula
and related functions. - Add an
update
method forbrmsfit_multiple
objects. (#615) - Split folds after
group
in thekfold
method. (#619)
Other changes
- Deprecate
compare_ic
and instead recommendloo_compare
for the
comparison ofloo
objects to ensure consistency between packages. (#414) - Use the glue package in the Stan code generation. (#549)
- Introduce
mvbind
to eventually replacecbind
in the formula syntax of multivariate models. - Validate several sampling-related arguments in
brm
before compiling the Stan model. (#576) - Show evaluated vignettes on CRAN again. (#591)
- Export function
get_y
which is used to extract response
values frombrmsfit
objects.
Bug fixes
- Fix an error when trying to change argument
re_formula
inbayes_R2
thanks to the GitHub user emieldl. (#592) - Fix occasional problems when running chains in parallel
via the future package thanks to Jared Knowles. (#579) - Ensure correct ordering of response categories in ordinal
models thanks to Jonas Kristoffer Lindelov. (#580) - Ignore argument
resp
ofmarginal_effects
in
univariate models thanks to Vassilis Kehayas. (#589) - Correctly disable cell-mean coding in varying effects.
- Allow to fix parameter
ndt
in drift diffusion models. - Fix Stan code for t-distributed varying effects
thanks to Ozgur Asar. - Fix an error in the post-processing of monotonic effects
occuring for multivariate models thanks to James Rae. (#598) - Fix lower bounds in truncated discrete models.
- Fix checks of the original data in
kfold
thanks to
the GitHub user gcolitti. (#602) - Fix an error when applying the
VarCorr
method to
meta-analytic models thanks to Michael Scharkow. (#616)