Measurement models available in bmm
Value
A character vector of model names with class bmm_models, which
prints as the grouped list. Use it as a character vector in base R or
dplyr, e.g. "imm" %in% bmm_models(); as.character() gives a plain one
where a function refuses the class.
Details
Printed, the result lists the models grouped by the task they are
meant for, one line per model with its constructor and full name. The
groups are: continuous reproduction; categorical recall and n-AFC
decisions; detection, recognition and confidence judgments; choices and
response times; processing-tree models. Type ?modelname (for example
?imm) for the arguments of a model.
Examples
bmm_models()
#> The following models are supported:
#>
#> Continuous reproduction
#>
#> - imm(): Interference measurement model by Oberauer and Lin (2017)
#> - mixture2p(): Two-parameter mixture model by Zhang and Luck (2008)
#> - mixture3p(): Three-parameter mixture model by Bays et al (2009)
#> - sdm(): Signal Discrimination Model (SDM) by Oberauer (2023)
#>
#> Categorical recall and n-AFC decisions
#>
#> - m3(): The Multinomial / Memory Measurement Model
#>
#> Processing-tree models
#>
#> - mpt(): Multinomial Processing Tree (MPT) models
#>
#> Detection, recognition and confidence judgments
#>
#> - sdt_cdp(): Continuous Dual-Process Signal Detection Theory (CDP)
#> - sdt_mafc(): Signal Detection Theory (m-AFC)
#> - sdt_ranking(): Signal Detection Theory (Ranking)
#> - sdt_rating(): Signal Detection Theory (Confidence Rating)
#> - sdt_yn(): Signal Detection Theory (Yes/No)
#>
#> Choices and response times
#>
#> - cswald(): Censored-Shifted Wald Model
#> - ddm(): Diffusion Decision Model
#> - ezdm(): EZ-Diffusion Model
#>
#> Type ?modelname to get information about a specific model, e.g. ?imm
"imm" %in% bmm_models()
#> [1] TRUE