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 name. The
groups are: continuous reproduction; categorical recall and n-AFC
decisions; detection, recognition and confidence judgments; choices and
response times. A model that fits several tasks, such as mpt(), is
listed under each. 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
#> - mixture2p(): Two-Parameter Mixture Model
#> - mixture3p(): Three-Parameter Mixture Model
#> - sdm(): Signal Discrimination Model (SDM)
#>
#> Categorical recall and n-AFC decisions
#>
#> - m3(): Memory Measurement Model
#> - mpt(): Multinomial Processing Tree (MPT) Model
#>
#> Detection, recognition and confidence judgments
#>
#> - mpt(): Multinomial Processing Tree (MPT) Model
#> - psychometric_function(): Psychometric Function
#> - 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
#>
#> 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