Package index • bmm

About bmm

These pages provide a summary of the functionality available in bmm

bmm-package: Easy and Accesible Bayesian Measurement Models Using 'brms'
bmm_models(): Measurement models available in bmm
bmm_options(): View or change global bmm options
bmm_setup(): Check whether this machine can fit bmm models

Fitting models

Main functions for model fitting

bmm() fit_model(): Fit Bayesian Measurement Models
bmmformula() bmf(): Create formula for predicting parameters of a bmmodel
update(<bmmfit>): Update a bmm model
summary(<bmmfit>): Create a summary of a fitted model represented by a bmmfit object
conditional_effects(<bmmfit>): Conditional Effects for BMM Models
recover_data(<bmmfit>) emm_basis(<bmmfit>): emmeans support for bmmfit objects
pp_check(<bmmfit>): Posterior predictive check for bmmfit objects
ppp(): Posterior predictive p-values for count models
posterior_epred(<bmmfit>) fitted(<bmmfit>): Expected response of a fitted bmm model

Model evaluation & post-processing

ROC/AUC, quantile-transformed ROC, and latent-distribution analysis for signal detection models

roc_sdt(): Model-implied ROC curve from a fitted SDT model
roc_observed(): Observed ROC points from a fitted SDT model
auc_sdt(): Area under the ROC curve from a fitted SDT model
latent_sdt(): Model-implied latent decision-variable distributions
sdt_thresholds(): Latent decision thresholds from a fitted rating SDT model
mratio(): Metacognitive efficiency (M-ratio) from a fitted meta-d' SDT model
sdt_sensitivity(): Sensitivity on the noise, signal, or root-mean-square scale
plot(<bmm_sdt_roc>): Plot a model-implied SDT ROC curve
plot(<bmm_sdt_auc>): Plot the posterior AUC distribution from a fitted SDT model
plot(<bmm_sdt_latent>): Plot the latent decision-variable distributions of an SDT model

Extract model information

Utility functions for extracting default priors, generate stan code, stan data, etc.

bmm_data_check(): Human-readable pre-fit data report for bmm models
default_prior(<bmmformula>): Get Default priors for Measurement Models specified in BMM
extract_parameter_dimensions(): Extract dimension from parameters in STAN parameter block
extract_stan_blocks(): Extract code from different STAN program blocks
fit_info(): Extract information from a brmsfit object
model_citation(): References for a measurement model
native_parameters(): Posterior draws of model parameters on the native scale
parameter_info(): Get parameter information for a bmm model
pp_check_vars(): List the posterior predictive checks available for a model fit
prior_info(): Priors of a fitted bmm model and where they came from
stancode(<bmmformula>): Generate Stan code for bmm models
standata(<bmmformula>): Stan data for bmm models

Specifying models

Functions for specifying which model to fit

cswald(): Censored-Shifted Wald Model
ddm(): Diffusion Decision Model
ezdm(): EZ-Diffusion Model
imm() IMMfull() IMMbsc() IMMabc(): Interference measurement model by Oberauer and Lin (2017).
m3(): The Multinomial / Memory Measurement Model
mixture2p(): Two-parameter mixture model by Zhang and Luck (2008).
mixture3p(): Three-parameter mixture model by Bays et al (2009).
mpt(): Multinomial Processing Tree (MPT) models
mpt_from_eqn(): Create an MPT model from an EQN model file
mpt_from_string(): Create an MPT model from an MPTinR-style model string
mpt_tree(): Create a tree for a Multinomial Processing Tree (MPT) model
plot(<mpt_tree>) plot(<mpt>): Plot the structure of MPT trees
sdm() sdmSimple(): Signal Discrimination Model (SDM) by Oberauer (2023)
sdt_cdp(): Continuous Dual-Process Signal Detection Theory Model
sdt_mafc(): m-Alternative Forced Choice Signal Detection Theory Model
sdt_ranking(): Ranking Signal Detection Theory Model
sdt_rating(): Confidence Rating Signal Detection Theory Model
sdt_yn(): Yes/No Signal Detection Theory Model

Distributions

Functions for special distributions

dimm() pimm() qimm() rimm(): Distribution functions for the Interference Measurement Model (IMM)
dsdm() psdm() qsdm() rsdm(): Distribution functions for the Signal Discrimination Model (SDM)
sdt_d() sdt_criterion() sdt_bias(): Utility functions for Signal Detection Theory
dcswald() rcswald() pcswald() qcswald(): Distribution functions for the censored shifted Wald model (cswald)
dddm() rddm(): Distribution function for the Diffusion Decision Model (ddm)
dezdm() rezdm(): Distribution functions for the EZ-Diffusion Model (ezdm)
dm3() rm3(): Distribution functions for the Memory Measurement Model (M3)
dmixture2p() pmixture2p() qmixture2p() rmixture2p(): Distribution functions for the two-parameter mixture model (mixture2p)
dmixture3p() pmixture3p() qmixture3p() rmixture3p(): Distribution functions for the three-parameter mixture model (mixture3p)
dmpt() rmpt(): Distribution functions for Multinomial Processing Tree (MPT) models
rejection_sampling(): Rejection Sampling
dsdt_cdp() rsdt_cdp(): Distribution functions for Continuous Dual-Process SDT (CDP)
dsdt_dpsdt() rsdt_dpsdt(): Distribution functions for dual-process SDT (DPSDT)
dsdt_mafc() rsdt_mafc(): Distribution functions for m-AFC SDT
dsdt_metad() rsdt_metad(): Distribution functions for meta-d' SDT
dsdt_ranking() rsdt_ranking(): Distribution functions for Ranking SDT
dsdt_rating() rsdt_rating(): Distribution functions for Confidence Rating SDT
dsdt_yn() rsdt_yn(): Distribution functions for Yes/No SDT

Data, model and parameter transformations

Utility functions for transforming data and parameters

adjust_ezdm_accuracy(): Adjust Accuracy Counts for Contamination (deprecated)
aggregate_sdt_cdp_data(): Aggregate long-format Remember/Know data for sdt_cdp()
c_sqrtexp2bessel() c_bessel2sqrtexp(): Convert between parametrizations of the c parameter of the SDM distribution
calc_error_relative_to_nontargets(): Calculate response error relative to non-target values
deg2rad() rad2deg(): Convert degrees to radians or radians to degrees.
construct_m3_act_funs(): Get Activation Functions for different M3 versions
ezdm_summary_stats(): Compute Robust Summary Statistics for EZ-Diffusion Model
flag_contaminant_rts(): Flag contaminant reaction times using mixture modeling
k2sd(): Transform kappa of the von Mises distribution to the circular standard deviation
mpt_data_reshape(): Reshape wide MPT frequency tables to the long format of bmm()
restructure(<bmmfit>): Restructure Old bmmfit Objects
softmax() softmaxinv(): Softmax function and its inverse
validate_fast_guesses(): Test if fast contaminants show random guessing behavior
wrap(): Wrap angles that extend beyond (-pi;pi)

Datasets

Available datasets for fitting the models

broeder_schuetz_2009_e3: Recognition ROC data from Broeder & Schuetz (2009, Experiment 3)
data_color_judgement_task: Example Data from a Color Judgement Task
meyer_grant_jakob_2025: Ranking recognition data from Meyer-Grant & Jakob (2025)
oberauer_lewandowsky_2019_e1: Data from Experiment 1 reported by Oberauer & Lewandowsky (2019)
oberauer_lin_2017: Data from Experiment 1 reported by Oberauer & Lin (2017)
zhang_luck_2008: Data from Experiment 2 reported by Zhang & Luck (2008)

Developers’ corner

Functions to assist in developing new models

apply_links(): Apply link functions for parameters in a formula or bmmformula
bmf2bf(): Convert bmmformula objects to brmsformula objects
check_data(): Generic S3 method for checking data based on model type
check_formula(): Generic S3 method for checking if the formula is valid for the specified model
check_model(): Generic S3 method for checking if the model is supported and model preprocessing
configure_model(): Generic S3 method for configuring the model to be fit by brms
configure_prior(): Generic S3 method for configuring the default prior for a bmmodel
create_initfun(): Generic S3 method for creating an initial values function
data_check_finding(): Construct a single finding for a pre-fit data report
data_check_findings(): Generic S3 method for model-specific pre-fit data findings
native_transform(): Transform parameter draws from the sampling scale to the native scale
postprocess_brm(): Generic S3 method for postprocessing the fitted brm model
pp_observables() pp_simulate(): Declare the observables of a bmm model for posterior predictive checks
print_model_details(): Generic S3 method for model-specific lines in print() of a model
revert_postprocess_brm(): Generic S3 method for reverting any postprocessing of the fitted brm model
use_model_template(): Create a file with a template for adding a new model (for developers)