bmm1.2.0 package

Easy and Accessible Bayesian Measurement Models Using 'brms'

apply_links

Apply link functions for parameters in a formula or bmmformula

bmf2bf

Convert bmmformula objects to brmsformula objects

bmm_options

View or change global bmm options

bmm-package

Easy and Accesible Bayesian Measurement Models Using 'brms'

bmm

Fit Bayesian Measurement Models

bmmformula

Create formula for predicting parameters of a bmmodel

c_parametrizations

Convert between parametrizations of the c parameter of the SDM distrib...

calc_error_relative_to_nontargets

Calculate response error relative to non-target values

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 specifi...

check_model

Generic S3 method for checking if the model is supported and model pre...

circle_transform

Convert degrees to radians or radians to degrees.

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

construct_m3_act_funs

Get Activation Functions for different M3 versions

default_prior.bmmformula

Get Default priors for Measurement Models specified in BMM

fit_info

Extract information from a brmsfit object

imm

Interference measurement model by Oberauer and Lin (2017).

IMMdist

Distribution functions for the Interference Measurement Model (IMM)

k2sd

Transform kappa of the von Mises distribution to the circular standard...

m3

The Multinomial / Memory Measurement Model

m3dist

Distribution functions for the Memory Measurement Model (M3)

mixture2p_dist

Distribution functions for the two-parameter mixture model (mixture2p)

mixture2p

Two-parameter mixture model by Zhang and Luck (2008).

mixture3p_dist

Distribution functions for the three-parameter mixture model (mixture3...

mixture3p

Three-parameter mixture model by Bays et al (2009).

postprocess_brm

Generic S3 method for postprocessing the fitted brm model

print_pretty_models_md

Generate a markdown list of the measurement models available in bmm

rejection_sampling

Rejection Sampling

restructure.bmmfit

Restructure Old bmmfit Objects

revert_postprocess_brm

Generic S3 method for reverting any postprocessing of the fitted brm m...

sdm

Signal Discrimination Model (SDM) by Oberauer (2023)

SDMdist

Distribution functions for the Signal Discrimination Model (SDM)

softmax

Softmax function and its inverse

stancode.bmmformula

Generate Stan code for bmm models

standata.bmmformula

Stan data for bmm models

summary.bmmfit

Create a summary of a fitted model represented by a bmmfit object

supported_models

Measurement models available in bmm

update.bmmfit

Update a bmm model

use_model_template

Create a file with a template for adding a new model (for developers)

wrap

Wrap angles that extend beyond (-pi;pi)

Fit computational and measurement models using full Bayesian inference. The package provides a simple and accessible interface by translating complex domain-specific models into 'brms' syntax, a powerful and flexible framework for fitting Bayesian regression models using 'Stan'. The package is designed so that users can easily apply state-of-the-art models in various research fields, and so that researchers can use it as a new model development framework. References: Frischkorn and Popov (2023) <doi:10.31234/osf.io/umt57>.

  • Maintainer: Vencislav Popov
  • License: GPL-2
  • Last published: 2025-07-24