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
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