# brms 2.8.0

### New Features

* Fit multinomial models via family `multinomial`. (#463)
* Fit Dirichlet models via family `dirichlet`. (#463)
* Fit conditional logistic models using the `categorical` and
`multinomial` families together with non-linear formula syntax. (#560)
* Choose the reference category of `categorical` and related 
families via argument `refcat` of the corresponding family functions.
* Use different subsets of the data in different univariate parts
of a multivariate model via addition argument `subset`. (#360)
* Control the centering of population-level design matrices
via argument `center` of `brmsformula` and related functions.
* Add an `update` method for `brmsfit_multiple` objects. (#615)
* Split folds after `group` in the `kfold` method. (#619)

### Other changes

* Deprecate `compare_ic` and instead recommend `loo_compare` for the
comparison of `loo` objects to ensure consistency between packages. (#414)
* Use the **glue** package in the Stan code generation. (#549)
* Introduce `mvbind` to eventually replace `cbind` 
in the formula syntax of multivariate models.
* Validate several sampling-related arguments in 
`brm` before compiling the Stan model. (#576)
* Show evaluated vignettes on CRAN again. (#591)
* Export function `get_y` which is used to extract response
values from `brmsfit` objects.

### Bug fixes

* Fix an error when trying to change argument `re_formula` 
in `bayes_R2` thanks to the GitHub user emieldl. (#592)
* Fix occasional problems when running chains in parallel
via the **future** package thanks to Jared Knowles. (#579)
* Ensure correct ordering of response categories in ordinal
models thanks to Jonas Kristoffer Lindelov. (#580)
* Ignore argument `resp` of `marginal_effects` in 
univariate models thanks to Vassilis Kehayas. (#589)
* Correctly disable cell-mean coding in varying effects.
* Allow to fix parameter `ndt` in drift diffusion models.
* Fix Stan code for t-distributed varying effects 
thanks to Ozgur Asar.
* Fix an error in the post-processing of monotonic effects
occuring for multivariate models thanks to James Rae. (#598)
* Fix lower bounds in truncated discrete models.
* Fix checks of the original data in `kfold` thanks to
the GitHub user gcolitti. (#602)
* Fix an error when applying the `VarCorr` method to
meta-analytic models thanks to Michael Scharkow. (#616)


# brms 2.7.0

### New features

* Fit approximate and non-isotropic Gaussian processes via `gp`. (#540)
* Enable parallelization of model fitting in `brm_multiple` 
via the future package. (#364)
* Perform posterior predictions based on k-fold cross-validation 
via `kfold_predict`. (#468)
* Indicate observations for out-of-sample predictions in 
ARMA models via argument `oos` of `extract_draws`. (#539)

### Other changes

* Allow factor-like variables in smooth terms. (#562)
* Make plotting of `marginal_effects` more robust to
the usage of non-standard variable names.
* Deactivate certain data validity checks when using custom families.
* Improve efficiency of adjacent category models.
* No longer print informational messages from the Stan parser.

### Bug fixes

* Fix an issue that could result in a substantial efficiency 
drop of various post-processing methods for larger models.
* Fix an issue when that resulted in an error when
using `fitted(..., scale = "linear")` with ordinal models
thanks to Andrew Milne. (#557)
* Allow setting priors on the overall intercept in sparse models.
* Allow sampling from models with only a single observation 
that also contain an offset thanks to Antonio Vargas. (#545)
* Fix an error when sampling from priors in mixture models
thanks to Jacki Buros Novik. (#542)
* Fix a problem when trying to sample from priors of
parameter transformations.
* Allow using `marginal_smooths` with ordinal models
thanks to Andrew Milne. (#570)
* Fix an error in the post-processing of `me` 
terms thanks to the GitHub user hlluik. (#571)
* Correctly update `warmup` samples when using 
`update.brmsfit`.


# brms 2.6.0

### New features

* Fit factor smooth interactions thanks to Simon Wood.
* Specify separate priors for thresholds in ordinal models. (#524)
* Pass additional arguments to `rstan::stan_model` via argument
`stan_model_args` in `brm`. (#525)
* Save model objects via argument `file` in `add_ic` 
after adding model fit criteria. (#478) 
* Compute density ratios based on MCMC samples via `density_ratio`.
* Ignore offsets in various post-processing methods via
argument `offset`.
* Update addition terms in formulas via `update_adterms`.

### Other changes

* Improve internal modularization of smooth terms.
* Reduce size of internal example models.

### Bug fixes

* Correctly plot splines with factorial covariates via `marginal_smooths`.
* Allow sampling from priors in intercept only models
thanks to Emmanuel Charpentier. (#529)
* Allow logical operators in non-linear formulas.


# brms 2.5.0

### New features

* Improve `marginal_effects` to better display ordinal and 
categorical models via argument `categorical`. (#491, #497)
* Improve method `kfold` to offer more options for specifying
omitted subsets. (#510)
* Compute estimated values of non-linear parameters via
argument `nlpar` in method `fitted`.
* Disable automatic cell-mean coding in model formulas without 
an intercept via argument `cmc` of `brmsformula` and related 
functions thanks to Marie Beisemann.
* Allow using the `bridge_sampler` method even if 
prior samples are drawn within the model. (#485)
* Specify post-processing functions of custom families 
directly in `custom_family`.
* Select a subset of coefficients in `fixef`, `ranef`, 
and `coef` via argument `pars`. (#520)
* Allow to `overwrite` already stored fit indices
when using `add_ic`.

### Other changes

* Ignore argument `resp` when post-processing
univariate models thanks to Ruben Arslan. (#488)
* Deprecate argument `ordinal` of `marginal_effects`. (#491)
* Deprecate argument `exact_loo` of `kfold`. (#510)
* Deprecate usage of `binomial` families without specifying `trials`.
* No longer sample from priors of population-level intercepts 
when using the default intercept parameterization.

### Bug fixes

* Correctly sample from LKJ correlation priors 
thanks to Donald Williams.
* Remove stored fit indices when calling `update` on 
brmsfit objects thanks to Emmanuel Charpentier. (#490)
* Fix problems when predicting a single data point using 
spline models thanks to Emmanuel Charpentier. (#494)
* Set `Post.Prob = 1` if `Evid.Ratio = Inf` in 
method `hypothesis` thanks to Andrew Milne. (#509) 
* Ensure correct handling of argument `file` in `brm_multiple`.


# brms 2.4.0

### New features

* Define custom variables in all of Stan's program blocks
via function `stanvar`. (#459)
* Change the scope of non-linear parameters to be global 
within univariate models. (#390)
* Allow to automatically group predictor values in Gaussian 
processes specified via `gp`. This may lead to a
considerable increase in sampling efficiency. (#300)
* Compute LOO-adjusted R-squared using method `loo_R2`.
* Compute non-linear predictors outside of a loop over 
observations by means of argument `loop` in `brmsformula`.
* Fit non-linear mixture models. (#456)
* Fit censored or truncated mixture models. (#469)
* Allow `horseshoe` and `lasso` priors to be set on special 
population-level effects.
* Allow vectors of length greater one to be passed to `set_prior`.
* Conveniently save and load fitted model objects in `brm`
via argument `file`. (#472)
* Display posterior probabilities in the output of `hypothesis`.

### Other changes

* Deprecate argument `stan_funs` in `brm` in favor of using the
`stanvars` argument for the specification of custom Stan functions.
* Deprecate arguments `flist` and `...` in `nlf`.
* Deprecate argument `dpar` in `lf` and `nlf`.

### Bug fixes

* Allow custom families in mixture models thanks to Noam Ross. (#453)
* Ensure compatibility with **mice** version 3.0. (#455)
* Fix naming of correlation parameters of group-level terms 
with multiple subgroups thanks to Kristoffer Magnusson. (#457) 
* Improve scaling of default priors in `lognormal` models (#460).
* Fix multiple problems in the post-processing of categorical models.
* Fix validation of nested grouping factors in post-processing 
methods when passing new data thanks to Liam Kendall.


# brms 2.3.1

### New features

* Allow censoring and truncation in zero-inflated and hurdle models. (#430)
* Export zero-inflated and hurdle distribution functions. 

### Other changes

* Improve sampling efficiency of the ordinal families 
`cumulative`, `sratio`, and `cratio`. (#433)
* Allow to specify a single k-fold subset in method `kfold`. (#441)

### Bug fixes

* Fix a problem in `launch_shinystan` due to which the
maximum treedepth was not correctly displayed thanks to
Paul Galpern. (#431)


# brms 2.3.0

### Features

* Extend `cor_car` to support intrinsic CAR models in pairwise
difference formulation thanks to the case study of Mitzi Morris.
* Compute `loo` and related methods for non-factorizable normal models.

### Other changes 

* Rename quantile columns in `posterior_summary`. This affects the
output of `predict` and related methods if `summary = TRUE`. (#425)
* Use hashes to check if models have the same response values 
when performing model comparisons. (#414)
* No longer set `pointwise` dynamically in `loo` and related methods. (#416)
* No longer show information criteria in the summary output.
* Simplify internal workflow to implement native response distributions. (#421)

### Bug fixes

* Allow `cor_car` in multivariate models with residual correlations 
thanks to Quentin Read. (#427)
* Fix a problem in the Stan code generation of distributional `beta` models
thanks to Hans van Calster. (#404)
* Fix `launch_shinystan.brmsfit` so that all parameters
are now shown correctly in the diagnose tab. (#340)


# brms 2.2.0

### Features

* 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`.

### Other changes

* 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.

### Bug fixes

* 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)


# brms 2.1.0

### Features
    
* 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.


### Bug fixes
    
* 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)




# brms 2.0.1

### Features
    
* Export the helper functions `posterior_summary` 
      and `posterior_table` both being used to
      summarize posterior samples and predictions.


### Bug fixes
    
* 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.




# brms 2.0.0

  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.
  
### Features
    
* 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)


### Other changes
    
* 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.


### Bug fixes
    
* 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)




# brms 1.10.2
### Features
    
* 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.


### Bug fixes
    
* 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.




# brms 1.10.0
### Features
    
* 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.


### Other changes
    
* 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.


### Bug fixes
    
* 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`.




# brms 1.9.0
### Features
    
* 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.


### Other changes
    
* 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.


### Bug fixes
    
* 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.	




# brms 1.8.0
### Features
    
* 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.


### Other changes
    
* 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.


### Bug fixes
    
* 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. 




# brms 1.7.0
### Features
    
* 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.


### Other changes
    
* 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.


### Bug fixes
    
* 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.




# brms 1.6.1
### Features
    
* Implement zero-one-inflated beta models
      via family `zero_one_inflated_beta`.
* Allow for more link functions in 
      zero-inflated and hurdle models.


### Other changes
    
* Ensure full compatibility with 
      `bayesplot` version 1.2.0.
* Deprecate addition argument `disp`.


### Bug fixes
    
* 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.




# brms 1.6.0
### Features
    
* 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.


### Other changes
    
* 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.


### Bug fixes
    
* 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.




# brms 1.5.1
### Features
    
* 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`.


### Other changes
    
* Avoid indexing of matrices in non-linear
      models to slightly improve sampling speed.


### Bug fixes
    
* 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`.




# brms 1.5.0
### Features
    
* 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.


### Other changes
    
* 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.


### Bug fixes
    
* Fix a rare error when predicting `von_mises`
      models thanks to John Kirwan.




# brms 1.4.0
### Features
    
* 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`.


### Other changes
    
* 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.


### Bug fixes
    
* 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.




# brms 1.3.1
### Features
    
* 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.


### Other changes
    
* Move `rstan` to 'Imports' and `Rcpp` 
      to 'Depends' in order to avoid loading `rstan` 
      into the global environment automatically.


### Bug fixes
    
* Fix a bug leading to unexpected errors
      in some S3 methods when applied to ordinal models.




# brms 1.3.0
### Features
    
* 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.


### Other changes
    
* 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.




# brms 1.2.0
### Features
    
* 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.


### Other changes
    
* 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`.


### Bug fixes
     
* 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.




# brms 1.1.0
### Features
    
* 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.


### Other changes
    
* 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.


### Bug fixes
     
* 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.




# brms 1.0.1
  \subsection{MINOR CHANGES
    
* Center design matrices inside the Stan code
      instead of inside `make_standata`.
* Get rid of several warning messages
      occuring on CRAN.




# brms 1.0.0
  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`.
### Features
     
* 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.


### Other changes
    
* 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`.


### Bug fixes
     
* 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.


# brms 0.10.0
### Features
     
* 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.

### Other changes

* 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.


### Bug fixes
     
* 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.


# brms 0.9.1
### Features
     
* 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.


### Other changes
     
* Add horizontal lines to the errorbars in `marginal_effects` plots for factors.


### Bug fixes
    
* 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.


# brms 0.9.0
### Features
     
* 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.


### Other changes
    
* 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.


### Bug fixes
    
* 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.


# brms 0.8.0
### Features
     
* 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.


### Other changes
    
* 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.


### Bug fixes
    
* 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.



# brms 0.7.0

### Features
    
* 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.


### Other changes
    
* 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.


### Bug fixes
    
* 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.


# brms 0.6.0

### Features
    
* 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.


### Other changes
    
* 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.

### Bug fixes

* 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.




# brms 0.5.0
### Features
    
* 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.


### Other changes
    
* 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.


### Bug fixes
    
* 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.


# brms 0.4.1

### Features
    
* Allow for sampling from all specified proper priors in the model.
* Compute Bayes-factors for point hypotheses in S3 method `hypothesis`.

### Bug fixes
    
* 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.


# brms 0.4.0

### Features
    
* 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.


### Other changes
    
* 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.


### Bug fixes
    
* 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.



# brms 0.3.0

### Features
    
* 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.


### Other changes
    
* Amend parametrization of random effects to increase efficiency of the sampling
algorithms. 
* Improve vectorization of sampling statements.


### Bug fixes
    
* 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`.


# brms 0.2.0

### Features
    
* 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.


### Other changes
    
* Amend warning and error messages to make them more informative.
* Correct examples in the documentation.
* Extend the README file.


### Bug fixes
    
* 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.


# brms 0.1.0
  
* Initial release version


