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'

supported_models(): Measurement models available in bmm

bmm_options(): View or change global bmm options

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

Extract model information

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

default_prior(<bmmformula>): Get Default priors for Measurement Models specified in BMM

fit_info(): Extract information from a brmsfit object

stancode(<bmmformula>): Generate Stan code for bmm models

standata(<bmmformula>): Stan data for bmm models

Specifying models

Functions for specifying which model to fit

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).

sdm() sdmSimple(): Signal Discrimination Model (SDM) by Oberauer (2023)

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)

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)

rejection_sampling(): Rejection Sampling

Data, model and parameter transformations

Utility functions for transforming data and parameters

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

k2sd(): Transform kappa of the von Mises distribution to the circular standard deviation

restructure(<bmmfit>): Restructure Old bmmfit Objects

softmax() softmaxinv(): Softmax function and its inverse

wrap(): Wrap angles that extend beyond (-pi;pi)

Datasets

Available datasets for fitting the models

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

postprocess_brm(): Generic S3 method for postprocessing the fitted brm 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)