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Specify custom response distributions with
function custom_family. (#381)
Model missing values and measurement error in
responses using the mi addition term. (#27, #343)
Allow missing values in predictors using mi
terms on the right-hand side of model formulas. (#27)
Model interactions between the special predictor
terms mo, me, and mi. (#313)
Introduce methods model_weights and
loo_model_weights providing several options
to compute model weights. (#268)
Introduce method posterior_average to
extract posterior samples averaged across models. (#386)
Allow hyperparameters of group-level effects
to vary over the levels of a categorical covariate
using argument by in function gr. (#365)
Allow predictions of measurement-error models with new data. (#335)
Pass user-defined variables to Stan via
stanvar. (#219, #357)
Allow ordinal families in mixture models. (#389)
Model covariates in multi-membership structures
that vary over the levels of the grouping factor
via mmc terms. (#353)
Fit shifted log-normal models via
family shifted_lognormal. (#218)
Specify nested non-linear formulas.
Introduce function make_conditions to ease
preparation of conditions for marginal_effects.
Change the parameterization of 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.
Treat integer responses in ordinal models
as directly indicating categories even if the lowest
integer is not one.
Improve output of the hypothesis method
thanks to the ideas of Matti Vuorre. (#362)
Always plot by variables as facets
in marginal_smooths.
Deprecate the cor_bsts correlation structure.
Allow the : operator to combine
groups in multi-membership terms thanks to
Gang Chen.
Avoid an unexpected error when calling
LOO with argument reloo = TRUE
thanks to Peter Konings. (#348)
Fix problems in predict when applied to
categorical models thanks to Lydia Andreyevna Krasilnikova
and Thomas Vladeck. (#336, #345)
Allow truncation in multivariate models with missing values thanks to Malte Lau Petersen. (#380)
Force time points to be unique within groups in autocorrelation structures thanks to Ruben Arslan. (#363)
Fix problems when post-processing multiple uncorrelated group-level terms of the same grouping factor thanks to Ivy Jansen. (#374)
Fix a problem in the Stan code of multivariate
weibull and frechet models thanks to
the GitHub user philj1s. (#375)
Fix a rare error when post-processing binomial
models thanks to the GitHub user SeanH94. (#382)
Keep attributes of variables when preparing the
model.frame thanks to Daniel Luedecke. (#393)
Fit models on multiple imputed
datasets via brm_multiple thanks
to Ruben Arslan. (#27)
Combine multiple brmsfit objects
via function combine_models.
Compute model averaged posterior
predictions with method pp_average. (#319)
Add new argument ordinal to
marginal_effects to generate special
plots for ordinal models thanks to the idea
of the GitHub user silberzwiebel. (#190)
Use informative inverse-gamma priors for length-scale parameters of Gaussian processes. (#275)
Compute hypotheses for all levels of a
grouping factor at once using argument scope
in method hypothesis. (#327)
Vectorize user-defined Stan functions
exported via export_functions using
argument vectorize.
Allow predicting new data in models with ARMA autocorrelation structures.
Correctly recover noise-free coefficients
through 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.
Fix problems in renaming parameters of the
cor_bsts structure thanks to Joshua Edward Morten. (#312)
Fix some unexpected errors when predicting from ordinal models thanks to David Hervas and Florian Bader. (#306, #307, #331)
Fix problems when estimating and predicting multivariate ordinal models thanks to David West. (#314)
Fix various minor problems in autocorrelation structures thanks to David West. (#320)
Export the helper functions posterior_summary
and posterior_table both being used to
summarize posterior samples and predictions.
Fix incorrect computation of intercepts
in acat and cratio models
thanks to Peter Phalen. (#302)
Fix pointwise computation of LOO
and WAIC in multivariate models with estimated
residual correlation structure.
Fix problems in various S3 methods sometimes
requiring unused variables to be specified in
newdata.
Fix naming of Stan models thanks to Hao Ran Lai.
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.
Add support for generalized multivariate models,
where each of the univariate models may have a different
family and autocorrelation structure.
Residual correlations can be estimated for multivariate
gaussian and student models. All features
supported in univariate models are now also available in
multivariate models. (#3)
Specify different formulas for different
categories in categorical models.
Add weakly informative default priors for the
parameter class Intercept to improve convergence
of more complex distributional models.
Optionally display the MC standard error in the
summary output. (#280)
Add argument 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)
Refactor many parts of the package to make it more consistent and easier to extend.
Show the link functions of all
distributional parameters in the summary
output. (#277)
Reduce working memory requirements when
extracting posterior samples for use in predict
and related methods thanks to Fanyi Zhang. (#224)
Remove deprecated aliases of functions and arguments from the package. (#278)
No longer support certain prior specifications, which were previously labeled as deprecated.
Remove the depreacted addition term
disp from the package.
Remove old versions of methods fixef,
ranef, coef, and VarCorr.
No longer support models fitted with brms < 1.0,
which used the multivariate 'trait' syntax
orginally deprecated in brms 1.0.
Make posterior sample extraction in the
summary method cleaner and less error prone.
No longer fix the seed for random number generation
in brm to avoid unexpected behavior in simulation studies.
Store stan_funs in brmsfit objects
to allow using update on models with user-defined
Stan functions thanks to Tom Wallis. (#288)
Fix problems in various post-processing methods
when applied to models with the reserved variable
intercept in group-level terms thanks to
the GitHub user ASKurz. (#279)
Fix an unexpected error in predict and related
methods when setting sample_new_levels = "gaussian"
in models with only one group-level effect.
Thanks to Timothy Mastny. (#286)
Allow setting priors on noise-free
variables specified via function me.
Add arguments Ksub, exact_loo
and group to method kfold for
defining omitted subsets according to a
grouping variable or factor.
Allow addition argument se
in skew_normal models.
Ensure correct behavior of horseshoe and lasso priors in multivariate models thanks to Donald Williams.
Allow using identity links on
all parameters of the wiener family
thanks to Henrik Singmann. (#276)
Use reasonable dimnames in the output
of fitted when returning linear predictors
of ordinal models thanks to the GitHub user atrolle. (#274)
Fix problems in marginal_smooths
occuring for multi-membership models thanks to
Hans Tierens.
Rebuild monotonic effects from scratch to allow specifying interactions with other variables. (#239)
Introduce methods posterior_linpred
and posterior_interval for consistency
with other model fitting packages based on
Stan.
Introduce function theme_black
providing a black ggplot2 theme.
Specify special group-level effects within the same terms as ordinary group-level effects.
Add argument prob to
summary, which allows to control the
width of the computed uncertainty intervals. (#259)
Add argument newdata to the
kfold method.
Add several arguments to the plot
method of marginal_effects to improve
control over the appearences of the plots.
Use the same noise-free variables for all model parts in measurement error models. (#257)
Make names of local-level terms used
in the cor_bsts structure more informative.
Store the autocor argument
within brmsformula objects.
Store posterior and prior samples in separate
slots in the output of method hypothesis.
No longer change the default theme of ggplot2 when attaching brms. (#256)
Make sure signs of estimates are not dropped
when rounding to zero in summary.brmsfit. (#263)
Refactor parts of extract_draws
and linear_predictor to be more consistent
with the rest of the package.
Do not silence the Stan parser
when calling brm to get informative
error messages about invalid priors.
Fix problems with spaces in priors
passed to set_prior.
Handle non data.frame objects
correctly in hypothesis.default.
Fix a problem relating to the colour
of points displayed in marginal_effects.
Perform model comparisons based on
marginal likelihoods using the methods
bridge_sampler, bayes_factor,
and post_prob all powered by the
bridgesampling package.
Compute a Bayesian version of R-squared
with the bayes_R2 method.
Specify non-linear models for all distributional parameters.
Combine multiple model formulas using
the + operator and the helper functions
lf, nlf, and set_nl.
Combine multiple priors using the
+ operator.
Split the nlpar argument of
set_prior into the three arguments
resp, dpar, and nlpar
to allow for more flexible prior specifications.
Refactor parts of the package to prepare for the implementation of more flexible multivariate models in future updates.
Keep all constants in the log-posterior
in order for bridge_sampler to be
working correctly.
Reduce the amount of renaming done
within the stanfit object.
Rename argument auxpar of
fitted.brmsfit to dpar.
Use the launch_shinystan generic
provided by the shinystan package.
Set bayesplot::theme_default()
as the default ggplot2 theme when
attaching brms.
Include citations of the brms overview paper as published in the Journal of Statistical Software.
Fix problems when calling fitted
with hurdle_lognormal models thanks to
Meghna Krishnadas.
Fix problems when predicting sigma
in asym_laplace models thanks to
Anna Josefine Sorensen.
Fit conditional autoregressive (CAR) models
via function cor_car thanks to the case
study of Max Joseph.
Fit spatial autoregressive (SAR) models
via function cor_sar. Currently works
for families gaussian and student.
Implement skew normal models via family
skew_normal. Thanks to Stephen Martin
for suggestions on the parameterization.
Add method reloo to perform exact
cross-validation for problematic observations
and kfold to perform k-fold cross-validation
thanks to the Stan Team.
Regularize non-zero coefficients in the
horseshoe prior thanks to Juho Piironen
and Aki Vehtari.
Add argument new_objects to various
post-processing methods to allow for passing of
data objects, which cannot be passed via
newdata.
Improve parallel execution flexibility
via the future package.
Improve efficiency and stability of ARMA models.
Throw an error when the intercept is removed in an ordinal model instead of silently adding it back again.
Deprecate argument threshold in brm
and instead recommend passing threshold directly
to the ordinal family functions.
Throw an error instead of a message when invalid priors are passed.
Change the default value of the autocor
slot in brmsfit objects to an empty
cor_brms object.
Shorten Stan code by combining declarations and definitions where possible.
Fix problems in pp_check
when the variable specified in argument
x has attributes thanks to
Paul Galpern.
Fix problems when computing fitted values for truncated discrete models based on new data thanks to Nathan Doogan.
Fix unexpected errors when passing models, which did not properly initiliaze, to various post-processing methods.
Do not accidently drop the second
dimension of matrices in summary.brmsfit
for models with only a single observation.
Fit latent Gaussian processes of one
or more covariates via function gp
specified in the model formula (#221).
Rework methods fixef, ranef,
coef, and VarCorr to be more flexible
and consistent with other post-processing methods (#200).
Generalize method hypothesis to be
applicable on all objects coercible to a
data.frame (#198).
Visualize predictions via spaghetti
plots using argument spaghetti in
marginal_effects and marginal_smooths.
Introduce method add_ic to
store and reuse information criteria in
fitted model objects (#220).
Allow for negative weights in multi-membership grouping structures.
Introduce an as.array method
for brmsfit objects.
Show output of R code in HTML vignettes thanks to Ben Goodrich (#158).
Resolve citations in PDF vignettes thanks to Thomas Kluth (#223).
Improve sampling efficiency for
exgaussian models thanks to
Alex Forrence (#222).
Also transform data points when using argument
transform in marginal_effects
thanks to Markus Gesmann.
Fix an unexpected error in marginal_effects
occuring for some models with autocorrelation terms
thanks to Markus Gesmann.
Fix multiple problems occuring for models with
the cor_bsts structure thanks to Andrew Ellis.
Implement zero-one-inflated beta models
via family zero_one_inflated_beta.
Allow for more link functions in zero-inflated and hurdle models.
Ensure full compatibility with bayesplot version 1.2.0.
Deprecate addition argument disp.
Fix problems when setting priors on coefficients of auxiliary parameters when also setting priors on the corresponding coefficients of the mean parameter. Thanks to Matti Vuorre for reporting this bug.
Allow ordered factors to be used as grouping variables thanks to the GitHub user itissid.
Fit finite mixture models using family
function mixture.
Introduce method pp_mixture to compute
posterior probabilities of mixture component
memberships thanks to a discussion with Stephen Martin.
Implement different ways to sample new levels
of grouping factors in predict and related
methods through argument sample_new_levels.
Thanks to Tom Wallis and Jonah Gabry for a detailed
discussion about this feature.
Add methods loo_predict, loo_linpred,
and loo_predictive_interval for computing
LOO predictions thanks to Aki Vehtari and Jonah Gabry.
Allow using offset in formulas
of non-linear and auxiliary parameters.
Allow sparse matrix multiplication in non-linear and distributional models.
Allow using the identity link for
all auxiliary parameters.
Introduce argument negative_rt in
predict and posterior_predict to
distinquish responses on the upper and lower
boundary in wiener diffusion models
thanks to Guido Biele.
Introduce method control_params to
conveniently extract control parameters of the
NUTS sampler.
Introduce argument int_conditions in
marginal_effects for enhanced plotting of
two-way interactions thanks to a discussion with
Thomas Kluth.
Improve flexibility of the conditions
argument of marginal_effects.
Extend method stanplot to correctly
handle some new mcmc_ plots of the
bayesplot package.
Improve the update method to
only recompile models when the Stan code
changes.
Warn about divergent transitions when calling
summary or print on brmsfit objects.
Warn about unused variables in argument
conditions when calling marginal_effects.
Export and document several distribution functions that were previously kept internal.
Fix problems with the inclusion of offsets occuring for more complicated formulas thanks to Christian Stock.
Fix a bug that led to invalid Stan code when sampling from priors in intercept only models thanks to Tom Wallis.
Correctly check for category specific group-level effects in non-ordinal models thanks to Wayne Folta.
Fix problems in pp_check when specifying
argument newdata together with arguments
x or group.
Rename the last column in the output of
hypothesis to "star" in order to avoid
problems with zero length column names thanks to
the GitHub user puterleat.
Add a missing new line statement at the end
of the summary output thanks to Thomas Kluth.
Allow horseshoe and lasso
priors to be applied on population-level effects
of non-linear and auxiliary parameters.
Force recompiling Stan models
in update.brmsfit via argument
recompile.
Avoid indexing of matrices in non-linear models to slightly improve sampling speed.
Fix a severe problem (introduced in version 1.5.0),
when predicting Beta models thanks to Vivian Lam.
Fix problems when summarizing some models fitted with older version of brms thanks to Vivian Lam.
Fix checks of argument group in
method pp_check thanks to Thomas K.
Get arguments subset and nsamples
working correctly in marginal_smooths.
Implement the generalized extreme value
distribution via family gen_extreme_value.
Improve flexibility of the horseshoe
prior thanks to Juho Piironen.
Introduce auxiliary parameter mu
as an alternative to specifying effects within
the formula argument in function
brmsformula.
Return fitted values of auxiliary parameters
via argument auxpar of method fitted.
Add vignette "brms_multilevel", in which
the advanced formula syntax of brms is explained
in detail using several examples.
Refactor various parts of the package to ease implementation of mixture and multivariate models in future updates. This should not have any user visible effects.
Save the version number of rstan in
element version of brmsfit objects.
Fix a rare error when predicting von_mises
models thanks to John Kirwan.
Fit quantile regression models via family
asym_laplace (asymmetric Laplace distribution).
Specify non-linear models in a (hopefully) more
intuitive way using brmsformula.
Fix auxiliary parameters to certain values
through brmsformula.
Allow family to be specified in
brmsformula.
Introduce family frechet for modelling
strictly positive responses.
Allow truncation and censoring at the same time.
Introduce function prior_ allowing
to specify priors using one-sided formulas or quote.
Pass priors to Stan directly without
performing any checks by setting check = FALSE
in set_prior.
Introduce method nsamples to extract
the number of posterior samples.
Export the main formula parsing function
parse_bf.
Add more options to customize two-dimensional surface
plots created by marginal_effects or marginal_smooths.
Change structure of brmsformula
objects to be more reliable and easier to extend.
Make sure that parameter nu never
falls below 1 to reduce convergence problems
when using family student.
Deprecate argument nonlinear.
Deprecate family geometric.
Rename cov_fixed to cor_fixed.
Make handling of addition terms more transparent by exporting and documenting related functions.
Refactor helper functions of the fitted
method to be easier to extend in the future.
Remove many units tests of internal functions and add tests of user-facing functions instead.
Import some generics from nlme instead of lme4 to remove dependency on the latter one.
Do not apply structure to NULL
anymore to get rid of warnings in R-devel.
Fix problems when fitting smoothing terms
with factors as by variables thanks to
Milani Chaloupka.
Fix a bug that could cause some monotonic effects to be ignored in the Stan code thanks to the GitHub user bschneider.
Make sure that the data of models with only a single observation are compatible with the generated Stan code.
Handle argument algorithm
correctly in update.brmsfit.
Fix a bug sometimes causing an error in
marginal_effects when using family
wiener thanks to Andrew Ellis.
Fix problems in fitted when applied
to zero_inflated_beta models thanks to
Milani Chaloupka.
Fix minor problems related to the prediction of autocorrelated models.
Fix a few minor bugs related to the backwards compatibility of multivariate and related models fitted with brms < 1.0.0.
Introduce the auxiliary parameter disc
('discrimination') to be used in ordinal models.
By default it is not estimated but fixed to one.
Create marginal_effects plots of
two-way interactions of variables that were
not explicitely modeled as interacting.
Move rstan to 'Imports' and Rcpp to 'Depends' in order to avoid loading rstan into the global environment automatically.
Fix a bug leading to unexpected errors in some S3 methods when applied to ordinal models.
Fit error-in-variables models
using function me in the model formulae.
Fit multi-membership models using function
mm in grouping terms.
Add families exgaussian
(exponentially modified Gaussian distribution)
and wiener (Wiener diffusion model distribution)
specifically suited to handle for response times.
Add the lasso prior as an alternative
to the horseshoe prior for sparse models.
Add the methods log_posterior,
nuts_params, rhat, and neff_ratio
for brmsfit objects to conveniently access
quantities used to diagnose sampling behavior.
Combine chains in method as.mcmc using
argument combine_chains.
Estimate the auxiliary parameter
sigma in models with known standard errors of
the response by setting argument sigma to
TRUE in addition function se.
Allow visualizing two-dimensional smooths
with the marginal_smooths method.
Require argument data to be explicitely
specified in all user facing functions.
Refactor the stanplot method
to use bayesplot on the backend.
Use the bayesplot theme as the default in all plotting functions.
Add the abbreviations mo and cs
to specify monotonic and category specific effects
respectively.
Rename generated variables in the data.frames
returned by marginal_effects to avoid potential
naming conflicts.
Deprecate argument cluster and use
the native cores argument of rstan instead.
Remove argument cluster_type as it is
no longer required to apply forking.
Remove the deprecated partial argument.
Add the new family hurdle_lognormal
specifically suited for zero-inflated continuous responses.
Introduce the pp_check method to perform
various posterior predictive checks
using the bayesplot package.
Introduce the marginal_smooths method to
better visualize smooth terms.
Allow varying the scale of global shrinkage
parameter of the horseshoe prior.
Add functions prior and prior_string
as aliases of set_prior, the former
allowing to pass arguments without quotes ""
using non-standard evaluation.
Introduce four new vignettes explaining how to fit non-linear models, distributional models, phylogenetic models, and monotonic effects respectively.
Extend the coef method to better
handle category specific group-level effects.
Introduce the prior_summary method
for brmsfit objects to obtain a summary
of prior distributions applied.
Sample from the prior of the original population-level
intercept when sample_prior = TRUE even in models
with an internal temporary intercept used to improve
sampling efficiency.
Introduce methods posterior_predict,
predictive_error and log_lik as
(partial) aliases of predict, residuals,
and logLik respectively.
Improve computation of Bayes factors
in the hypothesis method to be less
influenced by MCMC error.
Improve documentation of default priors.
Refactor internal structure of some formula and prior evaluating functions. This should not have any user visible effects.
Use the bayesplot package as the
new backend of plot.brmsfit.
Better mimic mgcv when parsing smooth terms to make sure all arguments are correctly handled.
Avoid an error occuring during the prediction of new data when grouping factors with only a single factor level were supplied thanks to Tom Wallis.
Fix marginal_effects to consistently
produce plots for all covariates in non-linear models
thanks to David Auty.
Improve the update method to better recognize
situations where recompliation of the Stan code
is necessary thanks to Raphael P.H.
Allow to correctly update the sample_prior
argument to value "only".
Fix an unexpected error occuring in many S3 methods when the thinning rate is not a divisor of the total number of posterior samples thanks to Paul Zerr.
Estimate monotonic group-level effects.
Estimate category specific group-level effects.
Allow t2 smooth terms based on
multiple covariates.
Estimate interval censored data via the
addition argument cens in the model formula.
Allow to compute residuals also based
on predicted values instead of fitted values.
Use the prefix 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 identifaction.
Ensure full compatibility with ggplot2 version 2.2.
Fix a bug that could result in incorrect
threshold estimates for cumulative and
sratio models thanks to Peter Congdon.
Fix a bug that sometimes kept distributional
gamma models from being compiled
thanks to Tim Beechey.
Fix a bug causing an error in predict
and related methods when two-level factors or
logical variables were used as covariates in
non-linear models thanks to Martin Schmettow.
Fix a bug causing an error when passing lists to additional arguments of smoothing functions thanks to Wayne Folta.
Fix a bug causing an error in the
prior_samples method for models with
multiple group-level terms that refer to the same
grouping factor thanks to Marco Tullio Liuzza.
Fix a bug sometimes causing an error when
calling marginal_effects for weighted models.
Center design matrices inside the Stan code
instead of inside make_standata.
Get rid of several warning messages occuring on CRAN.
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.
Allow estimating correlations between group-level effects defined across multiple formulae (e.g., in non-linear models) by specifying IDs in each grouping term via an extended lme4 syntax.
Implement distributional regression models allowing to fully predict auxiliary parameters of the response distribution. Among many other possibilities, this can be used to model heterogeneity of variances.
Zero-inflated and hurdle models do not use
multivariate syntax anymore but instead have
special auxiliary parameters named zi and
hu defining zero-inflation / hurdle probabilities.
Implement the von_mises family to model
circular responses.
Introduce the brmsfamily function for
convenient specification of family objects.
Allow predictions of t2 smoothing
terms for new data.
Feature vectors as arguments for the addition
argument trunc in order to model varying
truncation points.
Remove the cauchy family
after several months of deprecation.
Make sure that group-level parameter names are unambiguous by adding double underscores thanks to the idea of the GitHub user schmettow.
The predict method now returns predicted
probabilities instead of absolute frequencies of
samples for ordinal and categorical models.
Compute the linear predictor in the model block of the Stan program instead of in the transformed parameters block. This avoids saving samples of unnecessary parameters to disk. Thanks goes to Rick Arrano for pointing me to this issue.
Colour points in marginal_effects plots if
sensible.
Set the default of the robust argument
to TRUE in marginal_effects.brmsfit.
Fix a bug that could occur when predicting factorial response variables for new data. Only affects categorical and ordinal models.
Fix a bug that could lead to duplicated variable names in the Stan code when sampling from priors in non-linear models thanks to Tom Wallis.
Fix problems when trying to pointwise
evaluate non-linear formulae in
logLik.brmsfit thanks to Tom Wallis.
Ensure full compatibility of the ranef
and coef methods with non-linear models.
Fix problems that occasionally occured when handling dplyr datasets thanks to the GitHub user Atan1988.
Add support for generalized additive mixed models
(GAMMs). Smoothing terms can be specified using
the s and t2 functions in the model formula.
Introduce as.data.frame and as.matrix
methods for brmsfit objects.
The 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.
Update syntax of Stan models to match the recommended syntax of Stan 2.10.
The ngrps method should now always
return the correct result for non-linear models.
Fix problems in marginal_effects for
models using the reserved variable intercept
thanks to Frederik Aust.
Fix a bug in the print method of
brmshypothesis objects that could lead to
duplicated and thus invalid row names.
Residual standard deviation parameters of
multivariate models are again correctly displayed
in the output of the summary method.
Fix problems when using variational Bayes algorithms with brms while having rstan >= 2.10.0 installed thanks to the GitHub user cwerner87.
Allow the '/' symbol in group-level terms in
the formula argument to indicate nested
grouping structures.
Allow to compute WAIC and LOO
based on the pointwise log-likelihood using argument
pointwise to substantially reduce memory requirements.
Add horizontal lines to the errorbars in
marginal_effects plots for factors.
Fix a bug that could lead to a cryptic error
message when changing some parts of the model
formula using the update method.
Fix a bug that could lead to an error when
calling marginal_effects for predictors
that were generated with the base::scale
function thanks to Tom Wallis.
Allow interactions of numeric and categorical
predictors in marginal_effects to be passed
to the effects argument in any order.
Fix a bug that could lead to incorrect results
of predict and related methods when called with
newdata in models using the poly function
thanks to Brock Ferguson.
Make sure that user-specified factor contrasts are always applied in multivariate models.
Add support for monotonic effects
allowing to use ordinal predictors without
assuming their categories to be equidistant.
Apply multivariate formula syntax in categorical models to considerably increase modeling flexibility.
Add the addition argument 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.
Treat the fixed effects design matrix as sparse
by using the sparse argument of brm.
This can considerably reduce working memory
requirements if the predictors contain many zeros.
Add the cor_fixed correlation structure to
allow for fixed user-defined covariance matrices of the
response variable.
Allow to pass self-defined Stan functions
via argument stan_funs of brm.
Add the expose_functions method allowing to
expose self-defined Stan functions in R.
Extend the functionality of the update
method to allow all model parts to be updated.
Center the fixed effects design matrix also in multivariate models. This may lead to increased sampling speed in models with many predictors.
Refactor Stan code and data generating functions to be more consistent and easier to extent.
Improve checks of user-define prior specifications.
Warn about models that have not converged.
Make sure that regression curves computed by
the marginal_effects method are always smooth.
Allow to define category specific effects in
ordinal models directly within the formula
argument.
Fix problems in the generated Stan code when using very long non-linear model formulas thanks to Emmanuel Charpentier.
Fix a bug that prohibited to change priors on single standard deviation parameters in non-linear models thanks to Emmanuel Charpentier.
Fix a bug that prohibited to use nested grouping factors in non-linear models thanks to Tom Wallis.
Fix a bug in the linear predictor computation
within R, occuring for ordinal models
with multiple category specific effects. This
could lead to incorrect outputs of predict,
fitted, and logLik for these models.
Make sure that the global "contrasts" option
is not used when post-processing a model.
Implement generalized non-linear models, which
can be specified with the help of the nonlinear
argument in brm.
Compute and plot marginal effects using the
marginal_effects method thanks to the help
of Ruben Arslan.
Implement zero-inflated beta models through
family zero_inflated_beta thanks to the
idea of Ali Roshan Ghias.
Allow to restrict domain of fixed effects and
autocorrelation parameters using new arguments
lb and ub in function set_prior
thanks to the idea of Joel Gombin.
Add an as.mcmc method for compatibility
with the coda package.
Allow to call the WAIC, LOO,
and logLik methods with new data.
Make sure that brms is fully compatible with loo version 0.1.5.
Optionally define the intercept as an ordinary fixed effect to avoid the reparametrization via centering of the fixed effects design matrix.
Do not compute the WAIC in summary
by default anymore to reduce computation time
of the method for larger models.
The cauchy family is now deprecated
and will be removed soon as it often has convergence
issues and not much practical application anyway.
Change the default settings of the number of
chains and warmup samples to the defaults of rstan
(i.e., chains = 4 and warmup = iter / 2).
Do not remove bad behaving chains anymore as they may point to general convergence problems that are dangerous to ignore.
Improve flexibility of the theme argument
in all plotting functions.
Only show the legend once per page, when computing
trace and density plots with the plot method.
Move code of self-defined Stan functions
to inst/chunks and incorporate them into the
models using rstan::stanc_builder.
Also, add unit tests for these functions.
Fix problems when predicting with newdata
for zero-inflated and hurdle models thanks to Ruben Arslan.
Fix problems when predicting with newdata
if it is a subset of the data stored in a
brmsfit object thanks to Ruben Arslan.
Fix data preparation for multivariate models
if some responses are NA thanks to Raphael Royaute.
Fix a bug in the predict method occurring
for some multivariate models so that it now always
returns the predictions of all response variables,
not just the first one.
Fix a bug in the log-likelihood computation of
hurdle_poisson and hurdle_negbinomial models.
This may lead to minor changes in the values obtained by
WAIC and LOO for these models.
Fix some backwards compatibility issues of models fitted with version <= 0.5.0 thanks to Ulf Koether.
Use variational inference algorithms
as alternative to the NUTS sampler by specifying
argument algorithm in the brm function.
Implement beta regression models through family
Beta.
Implement zero-inflated binomial models through family
zero_inflated_binomial.
Implement multiplicative effects for family
bernoulli to fit (among others) 2PL IRT models.
Generalize the 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.
Combine fixed and random effects estimates using
the new coef method.
Call the residuals method with newdata thanks
to the idea of Friederike Holz-Ebeling.
Allow new levels of random effects grouping
factors in the predict, fitted, and residuals
methods using argument allow_new_levels.
Selectively exclude random effects in the predict,
fitted, and residuals methods using
argument re_formula.
Add a plot method for objects returned by
method hypothesis to visualize prior and posterior
distributions of the hypotheses being tested.
Improve evaluation of the response
part of the formula argument to
reliably allow terms with more than one variable
(e.g., y/x ~ 1).
Improve sampling efficiency of models containing many fixed effects through centering the fixed effects design matrix thanks to Wayne Folta.
Improve sampling efficiency of models containing
uncorrelated random effects specified by means
of (random || group) terms in formula
thanks to Ali Roshan Ghias.
Utilize user-defined functions in the Stan code of ordinal models to improve readability as well as sampling efficiency.
Make sure that model comparisons using LOO
or WAIC are only performed when models are
based on the same responses.
Use some generic functions of the lme4
package to avoid unnecessary function masking. This
leads to a change in the argument order of
method VarCorr.
Change the ggplot theme in the
plot method through argument theme.
Remove the 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.
Amend names of random effects parameters to simplify matching with their respective grouping factor levels.
Fix a bug in the hypothesis method
that could cause valid model parameters to be falsely
reported as invalid.
Fix a bug in the prior_samples method
that could cause prior samples of parameters
of the same class to be artifically correlated.
Fix 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.
Fix a bug in the evaluation of formula that
could cause complicated random effects terms to be
falsely treated as fixed effects.
Fix several bugs when calling the fitted
and predict methods with newdata thanks
to Ali Roshan Ghias.
Add support for zero-inflated and hurdle models thanks to the idea of Scott Baldwin.
Implement inverse gaussian models through family
inverse.gaussian.
Allow to specify truncation boundaries of the response variable thanks to the idea of Maciej Beresewicz.
Add support for autoregressive (AR) effects of residuals, which can
be modeled using the cor_ar and cor_arma functions.
Stationary autoregressive-moving-average (ARMA) effects of order one can now also be fitted using special covariance matrices.
Implement multivariate student-t models.
Binomial and ordinal families now support
the cauchit link function.
Allow family functions to be used in the
family argument.
Easy access to various rstan plotting functions
using the stanplot method.
Implement horseshoe priors to model sparsity in fixed effects coefficients thanks to the idea of Josh Chang.
Automatically scale default standard deviation priors so that they remain only weakly informative independent on the response scale.
Report model weights computed by the loo package when comparing multiple fitted models.
Separate the fixed effects Intercept from other fixed effects in the Stan code to slightly improve sampling efficiency.
Move autoregressive (AR) effects of the response from
the cor_ar to the cor_arr function as the result
of implementing AR effects of residuals.
Improve checks on argument newdata used in the
fitted and predict method.
Method standata is now the only way to extract data
that was passed to Stan from a brmsfit object.
Slightly improve Stan code for models containing no random effects.
Change the default prior of the degrees of freedom of the
student family to gamma(2,0.1).
Improve readability of the output of method VarCorr.
Export the make_stancode function to give users
direct access to Stan code generated by brms.
Rename the brmdata function to make_standata.
The former remains usable as a deprecated alias.
Improve documenation to better explain differences in autoregressive effects across R packages.
Fix a bug that could cause an unexpected error
when the predict method was called with newdata.
Avoid side effects of the rstan compilation
routines that could occasionally cause R to crash.
Make brms work correctly with loo version 0.1.3 thanks to Mauricio Garnier Villarreal and Jonah Gabry.
Fix a bug that could cause WAIC and LOO estimates to be slightly
incorrect for gaussian models with log link.
Compute the Watanabe-Akaike information criterion (WAIC) and leave-one-out cross-validation (LOO) using the loo package.
Provide an interface to shinystan with
S3 method launch_shiny.
New functions get_prior and set_prior to make
prior specifications easier.
Log-likelihood values and posterior predictive samples can now be calculated within R after the model has been fitted.
Make predictions based on new data using S3 method predict.
Allow for customized covariance structures of grouping factors with multiple random effects.
New S3 methods fitted and residuals
to compute fitted values and residuals, respectively.
Arguments WAIC and predict are removed
from the brm function, as they are no longer necessary.
New argument cluster_type in function brm
allowing to choose the cluster type created by the parallel package.
Remove chains that fail to initialize while sampling in parallel leaving the other chains untouched.
Redesign trace and density plots to be faster and more stable.
S3 method VarCorr now always returns covariance
matrices regardless of whether correlations were estimated.
Fix a bug in S3 method hypothesis related
to the calculation of Bayes-factors for point hypotheses.
User-defined covariance matrices that are not strictly positive definite for numerical reasons should now be handled correctly.
Fix problems when a factor is used as fixed effect and as random effects grouping variable at the same time thanks to Ulf Koether.
Fix minor issues with internal parameter naming.
Perform additional checking on user defined priors.
Allow for sampling from all specified proper priors in the model.
Compute Bayes-factors for point hypotheses
in S3 method hypothesis.
Fix a bug that could cause an error for models with multiple grouping factors thanks to Jonathan Williams.
Fix a bug that could cause an error for weighted poisson and exponential models.
Implement the Watanabe-Akaike Information Criterion (WAIC).
Implement the ||-syntax for random effects allowing
for the estimation of random effects standard deviations
without the estimation of correlations.
Allow to combine multiple grouping factors within one random
effects argument using the interaction symbol :.
Generalize S3 method hypothesis to be used with
all parameter classes not just fixed effects.
In addition, one-sided hypothesis testing is now possible.
Introduce new family multigaussian allowing
for multivariate normal regression.
Introduce new family bernoulli for dichotomous
response variables as a more efficient alternative to families
binomial or categorical in this special case.
Slightly change the internal structure of brms to reflect that rstan is finally on CRAN.
Thoroughly check validity of the response variable before the data is passed to Stan.
Prohibit variable names containing double underscores
__ to avoid naming conflicts.
Allow function calls with several arguments
(e.g. poly(x,3)) in the formula argument of
function brm.
Always center random effects estimates returned
by S3 method ranef around zero.
Prevent the use of customized covariance matrices for grouping factors with multiple random effects for now.
Remove any experimental JAGS code from the package.
Fix a bug in S3 method hypothesis
leading to an error when numbers with decimal places
were used in the formulation of the hypotheses.
Fix a bug in S3 method ranef that caused
an error for grouping factors with only one random effect.
Fix a bug that could cause the fixed intercept to be wrongly estimated in the presence of multiple random intercepts thanks to Jarrod Hadfield.
Introduce new methods parnames and
posterior_samples for class 'brmsfit' to extract
parameter names and posterior samples for given parameters,
respectively.
Introduce new method hypothesis for class brmsfit
allowing to test non-linear hypotheses concerning fixed effects.
Introduce new argument 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.
Introduce weighted and censored regressions through argument
addition of function brm.
Introduce new argument cov.ranef in the brm
function allowing for customized covariance structures of
random effects thanks to the idea of Boby Mathew.
Introduce new argument autocor in function brm
allowing for autocorrelation of the response variable.
Introduce new functions cor.ar, cor.ma,
and cor.arma, to be used with argument autocor
for modeling autoregressive, moving-average,
and autoregressive-moving-average models.
Amend parametrization of random effects to increase efficiency of the sampling algorithms.
Improve vectorization of sampling statements.
Fix a bug that could cause an error when fitting
poisson models while predict = TRUE.
Fix a bug that caused an error when sampling only one chain
while silent = TRUE.
New S3 class brmsfit to be returned by
the brm function.
New methods for class brmsfit:
summary, print, plot, predict,
fixef, ranef, VarCorr,
nobs, ngrps, and formula.
Introduce new argument silent in the
brm function, allowing to suppress most of
Stan's intermediate output.
Introduce new families negbinomial (negative binomial)
and geometric to allow for more flexibility
in modeling count data.
Amend warning and error messages to make them more informative.
Correct examples in the documentation.
Extend the README file.
Fix a bug that caused problems when formulas contained more complicated function calls.
Fix a bug that caused an error when posterior predictives were
sampled for family cumulative
Fix a bug that prohibited to use of improper flat priors for parameters that have proper priors by default.
Initial release version