Fix swald_lccdf() returning incorrect log-survival
probability when response time equals non-decision time in the
cswald model. Previously returned -Inf
instead of 0 (log of survival = 1) (#348).
Other changes
The ddm model supports both cmdstanr
and rstan backends. Previously, cmdstanr was
required.
bmm 1.3.0
New models
Add the Diffusion Decision Model (ddm)
for speeded decision-making tasks with trial-level RT and response data.
The model estimates drift rate, boundary separation, non-decision time,
and (optionally) relative starting point. Includes distribution
functions dddm() and rddm() (#280).
Add the EZ-Diffusion Model (ezdm) for
speeded decision-making tasks. The model estimates drift rate, boundary
separation, and non-decision time from aggregated summary statistics
(mean RT, variance of RT, accuracy) using the closed-form equations
derived by Wagenmakers et al. (2007). Supports both 3-parameter
(symmetric starting point) and 4-parameter (asymmetric starting point)
versions based on Srivastava et al. (2016). Implements Bayesian
hierarchical estimation following Chavez & Vandekerckhove (2025).
Includes distribution functions dezdm() and
rezdm() (#281).
Add the Censored Shifted Wald Model
(cswald) for choice reaction time tasks with two response
boundaries. The model estimates drift rate, boundary separation, and
non-decision time from trial-level RT and response data. Implements two
versions: simple (treats errors as censored correct
responses, appropriate for high-accuracy tasks) and
crisk (competing risks version with separate
accumulators for each response, suitable for balanced accuracy).
Includes distribution functions dcswald(),
pcswald(), qcswald(), and
rcswald(). Thanks to @GidonFrischkorn
New features
New S3 method conditional_effects() for
bmmfit objects. Provides an intuitive interface for
visualizing predictor effects on model parameters, with automatic
routing between distributional and non-linear parameters, inverse link
transformations to show parameters on their natural scale
(scale = "native"), softmax handling for mixture3p weight
parameters, and filtering of internal model variables (#203).
New S3 methods for emmeans support on
bmmfit objects. Users can now call
emmeans(fit, ~ condition, dpar = "kappa") for any bmmodel
(#323).
New pp_check() method for multinomial models (e.g.,
m3). Since brms::pp_check() does not support
the multinomial family, bmm now provides a custom method
that compares observed and predicted response proportions in the
ppc_bars style from bayesplot. The method
supports faceting by experimental conditions via group,
configurable credible intervals via probs, and
population-level predictions via re_formula = NA. For
non-multinomial models, pp_check() delegates to
brms::pp_check() and auto-selects the grouped plot variant
when group is specified (#324).
New function parameters() lists all parameters of a
bmmodel or bmmfit object with descriptions,
link functions, and fixed values (#329).
New function extract_stan_blocks() extracts
individual program blocks (functions, data, parameters, etc.) from
compiled Stan code (#286).
New function extract_parameter_dimensions()
extracts parameter names, dimensions, and types from a Stan parameters
block.
New functions ezdm_summary_stats() and
adjust_ezdm_accuracy() to compute and pre-process
summary statistics from trial-level RT data for the EZ-Diffusion Model
(#291).
New functions flag_contaminant_rts() and
validate_fast_guesses() for trial-level contamination
detection in RT data. Identifies fast guesses and attention lapses using
mixture modeling and provides Bayesian validation of fast guess
assumptions (#307).
New function create_initfun() creates
initialization functions for models that benefit from or require initial
values for MCMC sampling (#285).
New online article
on pre-processing and contamination detection for reaction time
data
Other changes
Improved rm3() random generation function for the
M3 model (#279).
Removed magrittr dependency; replaced %>% with the
native pipe |> (#341).
Minimum R version is now 4.1.0.
bmm 1.2.0
New models
Add the Memory Measurement Model (Oberauer & Lewandowsky, 2019)
and its generalization as the Multinomial Measurement Model for
categorical decision tasks as new model class m3 with
three versions: simple span (ss), complex span
(cs), and custom. For details, see the
article on
the bmm website (#237). Thanks to @GidonFrischkorn and @chenyu-psy
New features
Updates to the bmf2bf S3 methods for more flexible
translation of bmmformulas into brmsformulas
(#227).
New function apply_links adds link functions to all
non-linear formulas in a bmmformula object.
New example data set oberauer_lewandowsky_2019_e1
for exploring the m3 model.
The file_refit argument of the bmm
function now accepts character strings like brms. A warning
is given when “on_change” is specified, as this is not currently
implemented for bmmodels (#228).
New function rejection_sampling
Bug fixes
Fix conflict in setting default priors when model parameters were
transformed in a non-linear formula (#232).
Allow a NULL formula (formula(NULL)) to be added to a
bmmformula for consistentcy with brms (#264)
Improve error messages when attempting to construct bmmformulas
without a left-hand-side variable
Documentation
Add documentation to the continuous
reproduction task article for pre-processing half-circular stimulus
spaces when using bmmodels of the circular
model class (#229, #233).
vectorize k2sd() function for improved performance
various internal refactorings (#246, #242)
dplyr, magrittr and tidyr dependencies are now optional (#240)
new contributor - Chenyun Li (chenyu-psy) for his work on the m3
model
bmm 1.0.0
First version of the package on published on CRAN!
New features
you can now specify to save the bmmfit object
generated by bmm() to a file with the
file argument, similarly to
brms::brm() (#190)
the parameterization of the imm was adapted to
accurately reflect the model as implemented by Oberauer et
al. (2017)
prepare package for CRAN submission
Bug fixes
fix incorrect specification of default priors when only an
interaction is specified (#201)
the random generation function for the mixture3p
and imm returned incorrect samples for some rare
parameter combinations, this has now been fixed, so that the functions
now return correct samples for all parameter combinations.
Deprecated functions and
arguments
BREAKING CHANGE: the arguments for the distribution functions of the
mixture2p and mixture3p model have
been change to match the snake_case coding scheme. Instead of
pMem and pNT these are now
p_mem and p_nt. The old names are
deprecated and are no longer supported
bmm 0.5.1
Bug fixes
fix the display of the model call in the summary method for bmm
models
bmm 0.5.0
New features
add a summary() method for bmmfit
objects (#144)
add a global option bmm.summary_backend to control
the backend used for the summary() method (choices are
“bmm” and “brms”)
function restructure() now allows to apply methods
introduced in newer bmm versions to
bmmfit objects created by older bmm
versions
you can now specify any model parameter to be a constant by using an
equal sign in the bmmformula (#142)
you can now choose to estimate parameters that are fixed to a
constant by default for all models (#145)
default priors for all models are now specified via the
configure_prior() S3 method (#145)
cmdstanr will be used as the default backend for
brms if the user has it installed (#145)
various updates to the documentation and data sets
fix a bug preventing the sort_data check from being
executed (#72)
fix bugs with the summary() function not displaying
implicit parameters (#152) and not working properly with some
hierarchical designs (#173)
fix a bug in which the sort_data check occurred in
cases where it shouldn’t (#158)
Deprecated functions and
arguments
BREAKING CHANGE: remove get_model_prior(), get_stancode()
and get_standata(). Due to recent
changes in brms version 2.21.0, you can now use the
brms functions default_prior,
stancode and standata directly with
bmm models.
the function fit_model() is deprecated in favor of
bmm() and will be removed in a future version
(#163)
the argument setsize for the
mixture3p and IMM models is now called
set_size for consistency. The old argument name is
deprecated and will be removed in a future version (#163)
the distributions functions for the imm model are renamed from
dIMM, pIMM, rIMM and
qIMM to dimm, pimm,
rimm and qimm (#163)
the argument parallel for the bmm() function is
deprecated and will be removed in a future version. Use
cores instead, as for brms::brm()
(#163)
the models IMMfull(), IMMabc() and
IMMbsc() are now called via imm(),
imm(version = “abc”) or imm(version =
“bsc”). The old names are deprecated and will be removed in a
future version (#163)
the sdmSimple() model is now called
sdm(). The old name is deprecated and will be removed
in a future version (#163)
Other changes
bmm now requires the at least version 2.21.0 of
brms.
bmm 0.4.0
New features
add a check for the sdmSimple model if the data is
sorted by predictors. This leads to much faster sampling. The user can
control the default behavior with the sort_data
argument (#72)
the mixture3p and IMM models now
require that the intercept must be suppressed when set size is used as a
predictor (#96).
add postprocessing methods for sdmSimple to allow
the use of pp_check(),
conditional_effects and bridgesampling
with the model (#30)
add informed default priors for all models. You can always use the
get_model_prior() function to see the default priors
for a model
add a new function set_default_prior for
developers, which allows them to more easily set default priors on new
models regardless of the user-specified formula
you can now specify variables for models via regular expressions
rather than character vectors (#102)
you can now view and set all bmm global options via
bmm_options(). See ?bmm_options for
more information
add a start-up message upon loading the package
Bug fixes
fix a bug in the mixture3p and IMM
models which caused an error when intercept was not suppressed and set
size was used as predictor
update() now works properly with
bmmfit objects (#95)
fix a bug in the sort_data check which caused an
error when using grouped covariance structure in random effects across
different parameters
Other changes
brms is now loaded automatically when loading
bmm with library(bmm)
bmm 0.3.0
New features
BREAKING CHANGE: The fit_model function now
requires a bmmformula to be passed. The syntax of the
bmmformula or its short form bmf is
equal to specifying a brmsformula. However, as of this
version the bmmformula only specifies how parameters of
a bmmodel change across experimental conditions or
continuous predictors. The response variables that the model is fit to
now have to be specified when the model is defined using model =
bmmodel(). (#79)
BREAKING CHANGE: The non_target and
spaPos variables for the mixture3p and
IMM models were relabeled to
nt_features and nt_distances for
consistency. This is also to communicate that distance is not limited to
spatial distance but distances on any feature dimensions of the
retrieval cues. Currently, still only a single generalization gradient
for the cue features is possible.
This release includes reference fits for all implemented models to
ensure that future changes to the package do not compromise the included
models and change the results that their implementations produce.
The check_formula methods have been adapted to
match the new bmmformula syntax. It now evaluates if
formulas have been specified using the bmmformula
function, if formulas for all parameters of a bmmodel
have been specified and warns the user that only a fixed intercept will
be estimated if no formula for one of the parameters was provided.
Additionally, check_formula throws an error should
formulas be provided that do not match a parameter of the called
bmmodel unless they are part of a non-linear
transformation.
You can now specify formulas for internally fixed parameters such as
mu in all visual working memory models. This allows you
to predict if there is response bias in the data. If a formula is not
provided for mu, the model will assume that the mean of
the response distribution is fixed to zero.
there is now an option bmm.silent that allows to
suppress messages
the baseline activation b was removed from the
IMM models, as this is internally fixed to zero for
scaling and as of now cannot be predicted by independent variables
because the model would be unidentifiable.
the arguments used to fit the bmmodel are now
accessible in the bmmfit object via the
fit$bmm$fit_args list.
add class(‘bmmfit’) to the object returned from fit_model() allowing
for more flexible postprocessing of the underlying
brmsfit object. The object is now of class(‘bmmfit’,
‘brmsfit’)
changes to column names of datasets zhang_luck_2008
and oberauer_lin_2017 to make them more consistent
Bug Fixes
an error with the treatment of distances in the
IMMfull and the IMMbsc has been
corrected. This versions ensures that only positive distances can be
passed to any of the two models.
removed a warning regarding the scaling of the distances in the
IMMfull and the IMMbsc that was
specific only for circular distances.
Documentation
All articles have been update to the new bmmformula
syntax.
bmm 0.2.2
Bug Fixes
fixed a bug where passing a character vector or negative values to
set_size argument of visual working memory models caused an error or
incorrect behavior (#97)
bmm 0.2.1
Bug Fixes
Minor change to sdmSimple Stan helper functions to avoid a harmless
warning message in the Stan output
bmm 0.2.0
New features
New model available - The Signal Discrimination Model by Oberauer
(2023) for visual working memory continuous reproduction tasks. See
?sdmSimple. The current version does not take into account non-target
activation
Add ability to extract information about the default priors in
bmm models with get_model_prior()
(#53)
Add ability to generate stan code and stan data for each model with
get_model_stancode() and
get_model_standata() (#81)
BREAKING CHANGE: Add distribution functions for likelihood
(e.g. dIMM()) and random variate generation
rIMM()) for all models in the package. Remove
deprecated gen_3p_data() and
gen_imm_data() functions (#69)
Two new data sets are available: zhang_luck_2008
and oberauer_lin_2017 (#22)
Documentation
Website for the development version of the package is now available
at https://venpopov.com/bmm/dev/ (#18)
Add articles for each model to the website at
https://venpopov.com/bmm/dev/articles/
Add a detailed developer’s guide to the website at
https://venpopov.com/bmm/dev/dev-notes (#21)
Improve README with more detailed information about the package’s
goals and its models (#21)
Other changes
Save bmm package version in the
brmsfit object for reproducibility -
e.g. fit$version$bmm (#88)
bmm 0.1.1
New features
BREAKING CHANGE: Improve user interface to fit_model() ensures
package stability and future development. Model specific arguments are
now passed to the model functions as named arguments
(e.g. mixture3p(non_targets, setsize)). This allows for
a more flexible and intuitive way to specify model arguments. Passing
model specific arguments directly to the fit_model()
function is now deprecated (#43).
Add information about each model such as domain, task, name,
version, citation, requirements and parameters (#42)
Add ability to generate a template file for adding new models to the
package with use_model_template() (for developers)
(#39)
Other changes
Improve documentation of model functions. You can now get help on
each model by typing ?model_name into your console. For
example, calling the information on the full version of the Interference
Measurement Model would look like this: ?IMMfull
bmm 0.1.0
A major restructuring of the package to support stable and
generalizable development of future models (#41).
New Features
Refactor the fit_model() function to be generic and
independent of the model being fit (#20)
Transform models to be S3 objects. (#41).
View currently supported models with new function
supported_models(). Currently supported models are:
mixture2p(), mixture3p(),
IMMabc(), IMMbsc(),
IMMfull()
Add S3 methods for checking the data, formula, model and priors
(#41)
Add distribution functions for the Signal Discrimination Model. See
?SDM for usage (#27)
Add softmax and softmaxinv
functions
Bug Fixes
Change default prior on log(kappa) to Normal(2,1) for the
mixture3p() model (#15)
Other changes
BREAKING CHANGE: deprecate model_type argument in
fit_model(). Models must now be specified with S3
functions passed to argument model rather than model
names as strings passed to argument model_type
(#41)