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gp terms.
(#234)cox models via the new addition term
bhaz. (#1489)cmdstanr
backend. (#1684)loo_epred thanks to Aki Vehtari.
(#1641)create_priorsense_data.brmsfit thanks to Noa Kallioinen.
(#1354)force of function threading.
(#1549)loo prediction methods.
(#1674)loo optional in
loo_moment_match.loo_predict and
loo_linpred to be more consistent with other
post-processing functions.pathfinder and
laplace algorithms in the cmdstanr backend.
(#1591)constant
priors.kfold via
argument joint.make_stancode and make_standata to
be aliases of stancode and standata,
respectively. Change get_prior to be an alias of a new
generic method default_prior. This enable other packages to
define new stancode, standata and
default_prior methods to generate Stan code and data, and
extract the default priors, for their own objects building on brms.
Thanks to Ven Popov for helping with this. (#1604)shape parameter of
negbinomial models to inv_gamma(0.4, 0.3)
thanks to Aki Vehtari. (#1614)read_csv_as_stanfit thanks to Ven Popov.
(#1619)shinystan optional. This means
that the package has to be loaded, via library(shinystan),
before launch_shinystan can be used. (#1595)summary
output.plot
method by default.N in the plot method in
favor of argument nvariables.exact_loo in method
kfold.combine_models with moment
matching. (#1603)splines2 package
version. (#1580)rmulti_normal thanks to Ven Popov.
(#1588)kfold or
reloo in parallel.horseshoe and R2D2 priors
globally, that is, for all additive predictor terms specified in the
same formula. (#1492)as.brmsprior to transform objects into a
brmsprior. (#1491)lasso prior as it is not a good
shrinkage prior and incompatible with the newly implemented global
shrinkage prior framework.newdata of
get_refmodel.brmsfit(). (#1502)trials argument after
several years of deprecation. (#1501)restructure. Special thanks to
Simon Wood, Ruben Arslan, Marta Kołczyńska, Patrick Hogan, and Urs
Kalbitzer. (#1465)unstr term thanks to the help of Sebastian Weber.
(#1435)hurdle_cumulative family thanks to Stephen Wild.
(#1448)recompile in post-processing methods that require a
compiled Stan model.point_estimate feature in
prepare_predictions via the new argument
ndraws_point_estimate.lasso
priors. (#1427)cauchit or softplus.newdata than necessary. (#1457,
#1459, #1460)s(..., fx = TRUE).update and related methods. (#1373, #1378)drop_unused_levels = FALSE in brm and related
functions. (#1346)update.brmsfit. (#1380)dirichlet priors for more parameter types.
(#1165)backend = "cmdstanr" to stanfit objects thanks
to Simon Mills and Jacob Socolar. (#1331)O1 optimization of brms-generated Stan
models thanks to Aki Vehtari. (#1382)sdme parameters
in models with known response standard errors thanks to Solomon Kurz.
(#1348)gamma models with
softplus link.brm_multiple.
(#1383)control_params returns the right values for
models fitted with the cmdstanr backend. (#1390)subset addition term. (#1385)lb and ub arguments of set_prior
and related functions. (#878, #1094)logistic_normal for simplex responses.
(#1274)future_args to kfold and
reloo for additional control over parallel execution via
futures.beta_binomial &
zero_inflated_beta_binomial for potentially over-dispersed
and zero-inflated binomial response models thanks to Hayden Rabel.
(#1319 & #1311)ppd_* plots in pp_check via
argument prefix. (#1313)log link in binomial and beta type
families. (#1316)brms_seed has been added to
get_refmodel.brmsfit(). (#1287)inits in favor of init
for consistency with the Stan backends.summary method for
high-dimensional models. (#1330)int_conditions in
conditional_smooths thanks to Urs Kalbitzer. (#1280)projpred’s K-fold CV. (#1286)make_standata for
bernoulli families when only 1s are present thanks to
Facundo Munoz. (#1298)pp_check for censored responses to work for all
plot types thanks to Hayden Rabel. (#1327)overwrite in
add_criterion works as expected for all criteria thanks to
Andrew Milne. (#1323)launch_shinystan occurring when warmup
draws were saved thanks to Frank Weber. (#1257, #1329)log_lik for ordinal
models. (#1192)projpred from Imports: to
Suggests:. This has the important implication that users
need to load or attach projpred themselves if they want to
use it (the more common case is probably attaching, which is achieved by
library(projpred)). (#1222)overwrite in
add_criterion is working as intended thanks to Ruben
Arslan. (#1219)get_refmodel.brmsfit() (i.e., when using
projpred for a "brmsfit") causing offsets not
to be recognized. (#1220)cmdstanr backend thanks
to Riccardo Fusaroli. (#1218)posterior package.
(#1204)brms models with
emmeans thanks to Mattan S. Ben-Shachar. (#907, #1134)mi) terms with
subset addition terms. (#1063)get_dpar for use in the post-processing
of custom families thank to Martin Modrak. (#1131)squareplus link function in all families
and distributional parameters that also allow for the log
link function.incl_thres to
posterior_linpred.brmsfit() allowing to subtract the
threshold-excluding linear predictor from the thresholds in case of an
ordinal family. (#1137)"mock" backend option to facilitate testing
thanks to Martin Modrak. (#1116)file_refit = "always" to always overwrite
models stored via the file argument. (#1151)robust in method
hypothesis. (#1170)loop of custom_family. (#1084)cumulative models. (#1060)regenerate of method stancode.expose_functions for models fitted with the
cmdstanr backend thanks to Sebastian Weber. (#1176)log_prob and related functionality in models
fitted with the cmdstanr backend via function
add_rstan_model. (#1184)cbind to express multivariate models
after over two years of deprecation (please use mvbind
instead).posterior_linpred(transform = TRUE) is now equal
to posterior_epred(dpar = "mu") and no longer
deprecated.NA values in interval censored boundaries as
long as they are unused. (#1070)me) terms in favor of
the more general and consistent missing value (mi) terms.
(#698)cox
models thanks to Malcolm Gillies. (#1143)file_refit = "on_change"
if factor level names have changed thanks to Martin Modrak. (#1128)validate_newdata even when they are
simultaneously used as predictors and grouping variables thanks to
Martin Modrak. (#1141)horseshoe
prior thanks to Max Joseph. (#1167)normalize. to increase sampling efficiency thanks to Andrew
Johnson. (#1017, #1053)posterior_predict for truncated continuous
models even if the required CDF or quantile functions are
unavailable.validate_prior to validate priors
supplied by the user.rstan (Stan >= 2.25) backend.R2D2 to be used in set_prior.arma correlation structures in
non-normal families.data2 for use in
the evaluation of most model terms.file_refit. (#1058)brm via the silent argument. (#1076)stanvars to alter distributional parameters.
(#1061)stanvars to be used inside threaded likelihoods.
(#1111)sratio and cratio) thanks to Andrew Johnson.
(#1087)multinomial models with the
cmdstanr backend thanks to Andrew Johnson. (#1033): operator in autocorrelation
terms.wiener drift
diffusion models thanks to the GitHub user yanivabir. (#1085)by variables thanks to Reece Willoughby. (#1081)emmeans related methods thanks to
Russell V. Lenth. (#1096)projpred version 2.0 for variable selection in
generalized linear and additive multilevel models thanks to Alejandro
Catalina.by variables in multi-membership terms.loo_R2.se addition terms in threaded models.categorical families in threaded models.loo_moment_match.conditional_effects thanks to Isaac
Petersen. (#1014)reduce_sum using argument threads in
brm thanks to Sebastian Weber. (#892)fixed_param to sample from fixed
parameter values. (#973)NA values in data if
there are unused because of the subset addition argument.
(#895)by variables and within-group correlation
matrices in group-level terms. (#674)robust to the summary method.
(#976)posterior_predict and
log_lik methods via argument cores.
(#819)kfold.print
output of brmsprior objects. (#761)unused of function brmsformula.emmeans via
dpar = "mean" thanks to Russell V. Lenth. (#993)save_pars and corresponding argument in brm.
(#746)posterior_smooths to computing predictions
of individual smooth terms. (#738)conditional_effects using the effects
argument. (#1012)probs in the
conditional_effects method in favor of argument
prob.pp_check inducing wronger observation
orders in time series models thanks to Fiona Seaton. (#1007)loo_moment_match that
prevented it from working for some more complex models.cox. (#230, #962)loo_moment_match, which can be used to
update a loo object when Pareto k estimates are large.sample_new_levels = "uncertainty". (#956)id in function mo to ensure conditionally
monotonic effects. (#924)rtdists as additional backend of
wiener distribution functions thanks to the help of Henrik
Singmann. (#385)constant priors on some coefficients thanks to Frank Weber.
(#919)conditional_effects occurring for
categorical models with matrix predictors thanks to Jamie Cranston.
(#933)rate addition term so that it
also affects the shape parameter in
negbinomial models thanks to Edward Abraham. (#915)threshold in ordinal family functions thanks to the help of
Marta Kołczyńska.posterior_linpred as method in
conditional_effects.std_normal in the Stan code for improved
efficiency.cor, id, and
cov to the functions gr and mm
for easy specification of group-level correlation structures.int_conditions in
conditional_effects to work for all predictors not just
interactions.data2 in
brm_multiple. (#886)emmeans package thanks to the help of
Russell V. Lenth. (#418)stanvar using the position argument.me terms
thanks to Chris Chatham. (#855, #856)std_normal in set_prior
thanks to Ben Goodrich. (#867)weibull,
frechet, or inverse.gaussian families thanks
to Brian Huey and Jack Caster. (#879)gp for increased
efficiency.parse_bf to brmsterms and deprecate
the former function.extract_draws to
prepare_predictions and deprecate the former function.rescor default.cov_ranef in brm and
related functions.prior argument.
(#783)sigma in combination with fixed correlation
matrices via autocorrelation term fcor.data2 in brm and related
functions to pass data objects which cannot be passed via
data. The usage of data2 will be extended in
future versions.log_lik for
non-factorizable Student-t models. (#705)posterior_predict for
multinomial models thanks to Ivan Ukhov.re_formula in
multivariate models thanks to Maxime Dahirel. (#834)re_formula thanks
to @ferberkl.
(#844)posterior_predict again thanks to Mattew Kay. (#838)NA values more consistently in
posterior_table thanks to Anna Hake. (#845)offset variables to offsets in the
generated Stan code as the former will be reserved in the new stanc3
compiler.loo package.summary output.
(#824)newdata thanks to
Andrew Milne. (#830)resp_thres. (#675)loo_subsample for performing approximate
leave-one-out cross-validation for large data.add_criterion. (#793)sample_new_levels = "uncertainty" thanks to Dominic Magirr.
(#779)pp_check on censored models
thanks to Andrew Milne. (#744)zero_inflated_binomial models thanks to Raoul Wolf.
(#756)subset thanks to Ruben Arslan.reloo or kfold
with CAR models.fitted(..., scale = "linear")
with multinomial models thanks to Santiago Olivella. (#770)as.mcmc method for thinned models
thanks to @hoxo-m.
(#811)marginal_effects to
conditional_effects and marginal_smooths to
conditional_smooths. (#735)stanplot to mcmc_plot.pp_expect as an alias of
fitted. (#644)add_criterion are now
stored in the brmsfit$criteria slot.resp_cat in favor of
resp_thres.model_weights.intercept in favor of
Intercept.exact_match in favor of
fixed.add_loo and add_waic
in favor of add_criterion.summary output.
(#712)vreal and vint. (#707)cor_cosy.
(#403)sigma in combination with several
autocorrelation structures. (#403)rate to conveniently handle
denominators of rate responses in log-linear models.cor_car thanks to the case
study and help of Mitzi Morris.marginal_effects if not specified otherwise. (#718)me terms with
grouping factors thanks to the GitHub user tatters. (#706)horseshoe prior in categorical and related
models thanks to the Github user tatters. (#678)prior_samples thanks to Jonas Kristoffer Lindelov.
(#696)marginal_smooths thanks to Gavin Simpson. (#740)softplus link function in various families.
(#622)decomp of brmsformula thanks to the help of
Ben Goodrich. (#640)sparse separately for each model
formula.bayes_R2 and loo_R2 with
ordinal models. (#639)cor_arma in non-normal models. (#648)cor_arr and cor_bsts
correlation structures after a year of deprecation.marginal_effects to
measurement error models thanks to Jonathan A. Nations. (#636)marginal_effects.brm_multiple without
sampling thanks to Will Petry. (#671)multinomial.
(#463)dirichlet. (#463)categorical
and multinomial families together with non-linear formula
syntax. (#560)categorical and
related families via argument refcat of the corresponding
family functions.subset.
(#360)center of brmsformula and related
functions.update method for brmsfit_multiple
objects. (#615)group in the kfold
method. (#619)compare_ic and instead recommend
loo_compare for the comparison of loo objects
to ensure consistency between packages. (#414)mvbind to eventually replace
cbind in the formula syntax of multivariate models.brm
before compiling the Stan model. (#576)get_y which is used to extract response
values from brmsfit objects.re_formula
in bayes_R2 thanks to the GitHub user emieldl. (#592)resp of marginal_effects
in univariate models thanks to Vassilis Kehayas. (#589)ndt in drift diffusion
models.kfold thanks to the
GitHub user gcolitti. (#602)VarCorr method to
meta-analytic models thanks to Michael Scharkow. (#616)gp. (#540)brm_multiple
via the future package. (#364)kfold_predict. (#468)oos of extract_draws. (#539)marginal_effects more robust to the
usage of non-standard variable names.fitted(..., scale = "linear") with ordinal models thanks to
Andrew Milne. (#557)marginal_smooths with ordinal models thanks
to Andrew Milne. (#570)me terms thanks
to the GitHub user hlluik. (#571)warmup samples when using
update.brmsfit.rstan::stan_model via
argument stan_model_args in brm. (#525)file in
add_ic after adding model fit criteria. (#478)density_ratio.offset.update_adterms.marginal_smooths.marginal_effects to better display ordinal and
categorical models via argument categorical. (#491,
#497)kfold to offer more options for
specifying omitted subsets. (#510)nlpar in method fitted.cmc of brmsformula and
related functions thanks to Marie Beisemann.bridge_sampler method even if prior
samples are drawn within the model. (#485)custom_family.fixef,
ranef, and coef via argument
pars. (#520)overwrite already stored fit indices when
using add_ic.resp when post-processing univariate
models thanks to Ruben Arslan. (#488)ordinal of
marginal_effects. (#491)exact_loo of kfold.
(#510)binomial families without specifying
trials.update on
brmsfit objects thanks to Emmanuel Charpentier. (#490)Post.Prob = 1 if Evid.Ratio = Inf in
method hypothesis thanks to Andrew Milne. (#509)file in
brm_multiple.stanvar. (#459)gp. This may lead to a considerable increase
in sampling efficiency. (#300)loo_R2.loop in brmsformula.horseshoe and lasso priors to be set
on special population-level effects.set_prior.brm
via argument file. (#472)hypothesis.stan_funs in brm in
favor of using the stanvars argument for the specification
of custom Stan functions.flist and ... in
nlf.dpar in lf and
nlf.lognormal models
(#460).cumulative, sratio, and cratio.
(#433)kfold. (#441)launch_shinystan due to which the
maximum treedepth was not correctly displayed thanks to Paul Galpern.
(#431)cor_car to support intrinsic CAR models in
pairwise difference formulation thanks to the case study of Mitzi
Morris.loo and related methods for non-factorizable
normal models.posterior_summary. This
affects the output of predict and related methods if
summary = TRUE. (#425)pointwise dynamically in loo
and related methods. (#416)cor_car in multivariate models with residual
correlations thanks to Quentin Read. (#427)beta models thanks to Hans van Calster. (#404)launch_shinystan.brmsfit so that all parameters are
now shown correctly in the diagnose tab. (#340)custom_family. (#381)mi addition term. (#27, #343)mi terms on
the right-hand side of model formulas. (#27)mo, me, and mi. (#313)model_weights and
loo_model_weights providing several options to compute
model weights. (#268)posterior_average to extract posterior
samples averaged across models. (#386)by in function
gr. (#365)stanvar. (#219,
#357)mmc terms. (#353)shifted_lognormal. (#218)make_conditions to ease preparation
of conditions for marginal_effects.weibull and
exgaussian models to be consistent with other model
classes. Post-processing of related models fitted with earlier version
of brms is no longer possible.ordinal models as directly
indicating categories even if the lowest integer is not one.hypothesis method thanks to the
ideas of Matti Vuorre. (#362)by variables as facets in
marginal_smooths.cor_bsts correlation structure.: operator to combine groups in
multi-membership terms thanks to Gang Chen.LOO with
argument reloo = TRUE thanks to Peter Konings. (#348)predict when applied to categorical
models thanks to Lydia Andreyevna Krasilnikova and Thomas Vladeck.
(#336, #345)weibull
and frechet models thanks to the GitHub user philj1s.
(#375)binomial models
thanks to the GitHub user SeanH94. (#382)model.frame thanks to Daniel Luedecke. (#393)brm_multiple thanks to Ruben Arslan. (#27)brmsfit objects via function
combine_models.pp_average. (#319)ordinal to
marginal_effects to generate special plots for ordinal
models thanks to the idea of the GitHub user silberzwiebel. (#190)scope in method hypothesis.
(#327)Stan functions exported via
export_functions using argument
vectorize.me
terms thanks to Ruben Arslan. As a side effect, it is no longer possible
to define priors on noise-free Xme variables directly, but
only on their hyper-parameters meanme and
sdme.cor_bsts
structure thanks to Joshua Edward Morten. (#312)posterior_summary and
posterior_table both being used to summarize posterior
samples and predictions.acat and
cratio models thanks to Peter Phalen. (#302)pointwise computation of LOO and
WAIC in multivariate models with estimated residual
correlation structure.newdata.This is the second major release of brms. The main new
feature are generalized multivariate models, which now support
everything already possible in univariate models, but with multiple
response variables. Further, the internal structure of the package has
been improved considerably to be easier to maintain and extend in the
future. In addition, most deprecated functionality and arguments have
been removed to provide a clean new start for the package. Models fitted
with brms 1.0 or higher should remain fully compatible with
brms 2.0.
gaussian and student models. All features
supported in univariate models are now also available in multivariate
models. (#3)categorical models.Intercept to improve convergence of more complex
distributional models.summary
output. (#280)re.form as an alias of
re_formula to the methods posterior_predict,
posterior_linpred, and predictive_error for
consistency with other packages making use of these methods. (#283)summary output. (#277)predict and related methods thanks to Fanyi
Zhang. (#224)disp from the
package.fixef,
ranef, coef, and VarCorr.brms < 1.0,
which used the multivariate 'trait' syntax originally
deprecated in brms 1.0.summary method
cleaner and less error prone.brm to avoid unexpected behavior in simulation
studies.stan_funs in brmsfit objects to
allow using update on models with user-defined Stan
functions thanks to Tom Wallis. (#288)intercept in group-level
terms thanks to the GitHub user ASKurz. (#279)predict and related methods
when setting sample_new_levels = "gaussian" in models with
only one group-level effect. Thanks to Timothy Mastny. (#286)me.Ksub, exact_loo and
group to method kfold for defining omitted
subsets according to a grouping variable or factor.se in skew_normal
models.identity links on all parameters of the
wiener family thanks to Henrik Singmann. (#276)fitted when
returning linear predictors of ordinal models thanks to the GitHub user
atrolle. (#274)marginal_smooths occurring for
multi-membership models thanks to Hans Tierens.posterior_linpred and
posterior_interval for consistency with other model fitting
packages based on Stan.theme_black providing a black
ggplot2 theme.prob to summary, which allows
to control the width of the computed uncertainty intervals. (#259)newdata to the kfold
method.plot method of
marginal_effects to improve control over the appearences of
the plots.cor_bsts
structure more informative.autocor argument within
brmsformula objects.hypothesis.ggplot2 when
attaching brms. (#256)summary.brmsfit. (#263)extract_draws and
linear_predictor to be more consistent with the rest of the
package.Stan parser when calling
brm to get informative error messages about invalid
priors.set_prior.data.frame objects correctly in
hypothesis.default.marginal_effects.bridge_sampler, bayes_factor, and
post_prob all powered by the bridgesampling
package.bayes_R2 method.+ operator
and the helper functions lf, nlf, and
set_nl.+ operator.nlpar argument of set_prior into
the three arguments resp, dpar, and
nlpar to allow for more flexible prior specifications.bridge_sampler to be working correctly.stanfit
object.auxpar of fitted.brmsfit
to dpar.launch_shinystan generic provided by the
shinystan package.bayesplot::theme_default() as the default
ggplot2 theme when attaching brms.brms overview paper as
published in the Journal of Statistical Software.fitted with
hurdle_lognormal models thanks to Meghna Krishnadas.sigma in
asym_laplace models thanks to Anna Josefine Sorensen.cor_car thanks to the case study of Max Joseph.cor_sar. Currently works for families gaussian
and student.skew_normal.
Thanks to Stephen Martin for suggestions on the parameterization.reloo to perform exact cross-validation for
problematic observations and kfold to perform k-fold
cross-validation thanks to the Stan Team.horseshoe prior
thanks to Juho Piironen and Aki Vehtari.new_objects to various post-processing
methods to allow for passing of data objects, which cannot be passed via
newdata.future
package.threshold in brm and
instead recommend passing threshold directly to the ordinal
family functions.autocor slot in
brmsfit objects to an empty cor_brms
object.Stan code by combining declarations and
definitions where possible.pp_check when the variable specified in
argument x has attributes thanks to Paul Galpern.summary.brmsfit for models with only a single
observation.gp specified in the model formula (#221).fixef, ranef,
coef, and VarCorr to be more flexible and
consistent with other post-processing methods (#200).hypothesis to be applicable on all
objects coercible to a data.frame (#198).spaghetti in marginal_effects and
marginal_smooths.add_ic to store and reuse information
criteria in fitted model objects (#220).as.array method for brmsfit
objects.exgaussian models
thanks to Alex Forrence (#222).transform in marginal_effects thanks to Markus
Gesmann.marginal_effects occurring
for some models with autocorrelation terms thanks to Markus
Gesmann.cor_bsts structure thanks to Andrew Ellis.zero_one_inflated_beta.bayesplot version
1.2.0.disp.mixture.pp_mixture to compute posterior
probabilities of mixture component memberships thanks to a discussion
with Stephen Martin.predict and related methods through argument
sample_new_levels. Thanks to Tom Wallis and Jonah Gabry for
a detailed discussion about this feature.loo_predict, loo_linpred, and
loo_predictive_interval for computing LOO predictions
thanks to Aki Vehtari and Jonah Gabry.offset in formulas of non-linear and
auxiliary parameters.identity link for all auxiliary
parameters.negative_rt in predict
and posterior_predict to distinguish responses on the upper
and lower boundary in wiener diffusion models thanks to
Guido Biele.control_params to conveniently extract
control parameters of the NUTS sampler.int_conditions in
marginal_effects for enhanced plotting of two-way
interactions thanks to a discussion with Thomas Kluth.conditions argument of
marginal_effects.stanplot to correctly handle some new
mcmc_ plots of the bayesplot package.update method to only recompile models when
the Stan code changes.summary
or print on brmsfit objects.conditions when
calling marginal_effects.pp_check when specifying argument
newdata together with arguments x or
group.hypothesis to
"star" in order to avoid problems with zero length column
names thanks to the GitHub user puterleat.summary output thanks to Thomas Kluth.horseshoe and lasso priors to be
applied on population-level effects of non-linear and auxiliary
parameters.Stan models in
update.brmsfit via argument recompile.Beta models thanks to Vivian Lam.brms thanks to Vivian Lam.group in method
pp_check thanks to Thomas K.subset and nsamples working
correctly in marginal_smooths.gen_extreme_value.horseshoe prior thanks to
Juho Piironen.mu as an alternative to
specifying effects within the formula argument in function
brmsformula.auxpar of method fitted."brms_multilevel", in which the advanced
formula syntax of brms is explained in detail using several
examples.rstan in element
version of brmsfit objects.von_mises models
thanks to John Kirwan.asym_laplace
(asymmetric Laplace distribution).brmsformula.brmsformula.family to be specified in
brmsformula.frechet for modelling strictly
positive responses.prior_ allowing to specify priors
using one-sided formulas or quote.Stan directly without performing any
checks by setting check = FALSE in
set_prior.nsamples to extract the number of
posterior samples.parse_bf.marginal_effects or marginal_smooths.brmsformula objects to be more
reliable and easier to extend.nu never falls below
1 to reduce convergence problems when using family
student.nonlinear.geometric.cov_fixed to cor_fixed.fitted method to be
easier to extend in the future.nlme instead of
lme4 to remove dependency on the latter one.structure to NULL anymore to
get rid of warnings in R-devel.by variables thanks to Milani Chaloupka.Stan code thanks to the GitHub user bschneider.Stan code.algorithm correctly in
update.brmsfit.marginal_effects when using family wiener
thanks to Andrew Ellis.fitted when applied to
zero_inflated_beta models thanks to Milani Chaloupka.brms <
1.0.0.disc
(‘discrimination’) to be used in ordinal models. By default it is not
estimated but fixed to one.marginal_effects plots of two-way interactions
of variables that were not explicitely modeled as interacting.rstan to ‘Imports’ and Rcpp to
‘Depends’ in order to avoid loading rstan into the global
environment automatically.me in the
model formulae.mm in
grouping terms.exgaussian (exponentially modified
Gaussian distribution) and wiener (Wiener diffusion model
distribution) specifically suited to handle for response times.lasso prior as an alternative to the
horseshoe prior for sparse models.log_posterior,
nuts_params, rhat, and neff_ratio
for brmsfit objects to conveniently access quantities used
to diagnose sampling behavior.as.mcmc using argument
combine_chains.sigma in models with
known standard errors of the response by setting argument
sigma to TRUE in addition function
se.marginal_smooths method.data to be explicitely specified in
all user facing functions.stanplot method to use
bayesplot on the backend.bayesplot theme as the default in all plotting
functions.mo and cs to specify
monotonic and category specific effects respectively.marginal_effects to avoid potential naming conflicts.cluster and use the native
cores argument of rstan instead.cluster_type as it is no longer
required to apply forking.partial argument.hurdle_lognormal specifically suited
for zero-inflated continuous responses.pp_check method to perform various
posterior predictive checks using the bayesplot
package.marginal_smooths method to better
visualize smooth terms.horseshoe prior.prior and prior_string as
aliases of set_prior, the former allowing to pass arguments
without quotes "" using non-standard evaluation.coef method to better handle category
specific group-level effects.prior_summary method for
brmsfit objects to obtain a summary of prior distributions
applied.sample_prior = TRUE even in models with an internal
temporary intercept used to improve sampling efficiency.posterior_predict,
predictive_error and log_lik as (partial)
aliases of predict, residuals, and
logLik respectively.hypothesis
method to be less influenced by MCMC error.bayesplot package as the new backend of
plot.brmsfit.mgcv when parsing smooth terms to make
sure all arguments are correctly handled.marginal_effects to consistently produce plots for
all covariates in non-linear models thanks to David Auty.update method to better recognize
situations where recompliation of the Stan code is
necessary thanks to Raphael P.H.update the sample_prior
argument to value "only".t2 smooth terms based on multiple
covariates.cens in the model formula.residuals also based on predicted
values instead of fitted values.bcs in parameter names of category
specific effects and the prefix bm in parameter names of
monotonic effects (instead of the prefix b) to simplify
their identification.ggplot2 version
2.2.cumulative and sratio models thanks to Peter
Congdon.gamma
models from being compiled thanks to Tim Beechey.predict and related
methods when two-level factors or logical variables were used as
covariates in non-linear models thanks to Martin Schmettow.prior_samples method
for models with multiple group-level terms that refer to the same
grouping factor thanks to Marco Tullio Liuzza.marginal_effects for weighted models.\subsection{MINOR CHANGES
make_standata.This is one of the largest updates of brms since its
initial release. In addition to many new features, the multivariate
'trait' syntax has been removed from the package as it was
confusing for users, required much special case coding, and was hard to
maintain. See help(brmsformula) for details of the formula
syntax applied in brms.
lme4 syntax.zi and hu defining zero-inflation / hurdle
probabilities.von_mises family to model circular
responses.brmsfamily function for convenient
specification of family objects.t2 smoothing terms for new
data.trunc in order to model varying truncation points.cauchy family after several months of
deprecation.predict method now returns predicted probabilities
instead of absolute frequencies of samples for ordinal and categorical
models.marginal_effects plots if
sensible.robust argument to
TRUE in marginal_effects.brmsfit.logLik.brmsfit thanks to Tom Wallis.ranef and
coef methods with non-linear models.dplyr datasets thanks to the GitHub user Atan1988.s and t2
functions in the model formula.as.data.frame and as.matrix
methods for brmsfit objects.gaussian("log") family no longer implies a
log-normal distribution, but a normal distribution with log-link to
match the behavior of glm. The log-normal distribution can
now be specified via family lognormal.Stan models to match the recommended
syntax of Stan 2.10.ngrps method should now always return the correct
result for non-linear models.marginal_effects for models using the
reserved variable intercept thanks to Frederik Aust.print method of
brmshypothesis objects that could lead to duplicated and
thus invalid row names.summary
method.brms while having rstan >= 2.10.0 installed
thanks to the GitHub user cwerner87.formula argument to indicate nested grouping
structures.WAIC and LOO based on the
pointwise log-likelihood using argument pointwise to
substantially reduce memory requirements.marginal_effects plots for factors.formula using the
update method.marginal_effects for predictors that were generated with
the base::scale function thanks to Tom Wallis.marginal_effects to be passed to the effects
argument in any order.predict and related methods when called with
newdata in models using the poly function
thanks to Brock Ferguson.monotonic effects allowing to use
ordinal predictors without assuming their categories to be
equidistant.disp to define multiplicative
factors on dispersion parameters. For linear models, disp
applies to the residual standard deviation sigma so that it
can be used to weight observations.sparse argument of brm. This can considerably
reduce working memory requirements if the predictors contain many
zeros.cor_fixed correlation structure to allow for
fixed user-defined covariance matrices of the response variable.Stan functions via argument
stan_funs of brm.expose_functions method allowing to expose
self-defined Stan functions in R.update method to allow
all model parts to be updated.Stan code and data generating functions to be
more consistent and easier to extent.marginal_effects method are always smooth.formula argument.Stan code when using very
long non-linear model formulas thanks to Emmanuel Charpentier.R,
occurring for ordinal models with multiple category specific effects.
This could lead to incorrect outputs of predict,
fitted, and logLik for these models."contrasts" option is not
used when post-processing a model.nonlinear argument in
brm.marginal_effects method thanks to the help of Ruben
Arslan.zero_inflated_beta thanks to the idea of Ali Roshan
Ghias.lb and ub in
function set_prior thanks to the idea of Joel Gombin.as.mcmc method for compatibility with the
coda package.WAIC, LOO, and
logLik methods with new data.brms is fully compatible with
loo version 0.1.5.summary by default anymore
to reduce computation time of the method for larger models.cauchy family is now deprecated and will be removed
soon as it often has convergence issues and not much practical
application anyway.rstan (i.e.,
chains = 4 and warmup = iter / 2).theme argument in all
plotting functions.plot method.Stan functions to
inst/chunks and incorporate them into the models using
rstan::stanc_builder. Also, add unit tests for these
functions.newdata for
zero-inflated and hurdle models thanks to Ruben Arslan.newdata if it is a
subset of the data stored in a brmsfit object thanks to
Ruben Arslan.NA thanks to Raphael Royaute.predict method occurring for some
multivariate models so that it now always returns the predictions of all
response variables, not just the first one.hurdle_poisson and hurdle_negbinomial models.
This may lead to minor changes in the values obtained by
WAIC and LOO for these models.algorithm in the
brm function.Beta.zero_inflated_binomial.bernoulli
to fit (among others) 2PL IRT models.formula argument for zero-inflated and
hurdle models so that predictors can be included in only one of the two
model parts thanks to the idea of Wade Blanchard.coef method.residuals method with newdata
thanks to the idea of Friederike Holz-Ebeling.predict, fitted, and residuals
methods using argument allow_new_levels.predict,
fitted, and residuals methods using argument
re_formula.plot method for objects returned by method
hypothesis to visualize prior and posterior distributions
of the hypotheses being tested.formula
argument to reliably allow terms with more than one variable (e.g.,
y/x ~ 1).(random || group) terms in
formula thanks to Ali Roshan Ghias.Stan code of
ordinal models to improve readability as well as sampling
efficiency.LOO or
WAIC are only performed when models are based on the same
responses.lme4 package to avoid
unnecessary function masking. This leads to a change in the argument
order of method VarCorr.ggplot theme in the plot method
through argument theme.n. prefix in arguments n.iter,
n.warmup, n.thin, n.chains, and
n.cluster of the brm function. The old
argument names remain usable as deprecated aliases.hypothesis method that could cause
valid model parameters to be falsely reported as invalid.prior_samples method that could cause
prior samples of parameters of the same class to be artificially
correlated.Stan code of linear models with moving-average
effects and non-identity link functions so that they no longer contain
code related solely to autoregressive effects.formula that could cause
complicated random effects terms to be falsely treated as fixed
effects.fitted and
predict methods with newdata thanks to Ali
Roshan Ghias.inverse.gaussian.cor_ar and cor_arma
functions.cauchit
link function.family
argument.rstan plotting functions using
the stanplot method.loo package when
comparing multiple fitted models.Stan code to slightly improve sampling efficiency.cor_ar to the cor_arr function as the result
of implementing AR effects of residuals.newdata used in the
fitted and predict method.standata is now the only way to extract data
that was passed to Stan from a brmsfit
object.Stan code for models containing no
random effects.student family to gamma(2,0.1).VarCorr.make_stancode function to give users direct
access to Stan code generated by brms.brmdata function to
make_standata. The former remains usable as a deprecated
alias.predict method was called with newdata.rstan compilation routines
that could occasionally cause R to crash.brms work correctly with loo version
0.1.3 thanks to Mauricio Garnier Villarreal and Jonah Gabry.gaussian models with log
link.loo
package.shinystan with S3 method
launch_shiny.get_prior and set_prior to
make prior specifications easier.predict.fitted and residuals to
compute fitted values and residuals, respectively.WAIC and predict are removed
from the brm function, as they are no longer
necessary.cluster_type in function brm
allowing to choose the cluster type created by the parallel
package.VarCorr now always returns covariance
matrices regardless of whether correlations were estimated.hypothesis related to the
calculation of Bayes-factors for point hypotheses.hypothesis.||-syntax for random effects allowing for
the estimation of random effects standard deviations without the
estimation of correlations.:.hypothesis to be used with all
parameter classes not just fixed effects. In addition, one-sided
hypothesis testing is now possible.multigaussian allowing for
multivariate normal regression.bernoulli for dichotomous response
variables as a more efficient alternative to families
binomial or categorical in this special
case.rstan is finally on CRAN.Stan.__ to avoid naming conflicts.poly(x,3)) in the formula argument of function
brm.ranef around zero.JAGS code from the
package.hypothesis leading to an error
when numbers with decimal places were used in the formulation of the
hypotheses.ranef that caused an error for
grouping factors with only one random effect.parnames and
posterior_samples for class ‘brmsfit’ to extract parameter
names and posterior samples for given parameters, respectively.hypothesis for class
brmsfit allowing to test non-linear hypotheses concerning
fixed effects.addition in function brm to get
a more flexible approach in specifying additional information on the
response variable (e.g., standard errors for meta-analysis).
Alternatively, this information can also be passed to the
formula argument directly.addition of function brm.cov.ranef in the
brm function allowing for customized covariance structures
of random effects thanks to the idea of Boby Mathew.autocor in function brm allowing
for autocorrelation of the response variable.cor.ar, cor.ma,
and cor.arma, to be used with argument autocor
for modeling autoregressive, moving-average, and
autoregressive-moving-average models.predict = TRUE.silent = TRUE.brmsfit to be returned by the
brm function.brmsfit: summary,
print, plot, predict,
fixef, ranef, VarCorr,
nobs, ngrps, and formula.silent in the brm
function, allowing to suppress most of Stan’s intermediate
output.negbinomial (negative binomial)
and geometric to allow for more flexibility in modeling
count data.cumulative.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.