Function reference • cognitivemodels

All functions

albrecht2019exp1: Test data for fitting the Exemplar-based judgment model

anova(<cm>): Analysis of Deviance for Cogscimodel Fits

baseline_const_c() baseline_const_d() baseline_mean_c() baseline_mean_d(): Baseline models

bayes_beta_c() bayes_beta_d() bayes_dirichlet_d() bayes_dirichlet_c() bayes(): Bayesian Inference Cognitive Model

softmax() epsilon_greedy() epsilon() luce() argmax(): Choicerule Models (action-selection rules)

chr_as_rhs(): Convert a character to formula

Cm cm-class: The R6 class underlying all "cm" (cognitive model) objects 'Cm$new(formula, data, parspace)'

cm_choicerules(): Show the Choicerules for Discrete Cognitive Models

cm_options(): Advanced Options for Cognitive Models

cm_solvers(): Show the Optimization Solvers for Cognitive Models

cognitivemodel(): Add components to a model

constraints(): Show the constraints of a cognitive model

cpt_d() cpt_c() cpt_mem_d() cpt_mem_c(): Cumulative Prospect Theory Models

cpttest: Test data for fitting the Cumulative Prospect Theory, cpt, model

.grid(): Make a regular or random grid

.solve_grid_constraint(): Adds the parameter that are constrained to the free parameters in the construction of a grid

gcm() ebm_j() mem() ebm(): Exemplar-based Cognitive Models

ebm_cpp(): Computes Predictions for the Exemplar-based Models (GCM, EBM)

end(): Ends building a cognitivemodel via +

fit(): Estimates free parameters of a cognitvemodel generated via +

fun(): Adds a function to a cognitivemodel via +

getCall(<cm>): Get the Call to a cognitive model object

get_ev(): Gets the expected value of gambles

get_p(): Gets the probabilities of gambles

get_var(): Gets the variance of gambles

get_x(): Gets the outcomes of gambles

logLik(<cm>) MSE() RMSE.cm() SSE() SSE(): Computes Various Model Fit Measures

hm1988(): Dynamic optimization model for risk-sensitive foraging problems in discrete time

npar() nobs() nstim() natt() coef(<cm>): Information about a cognitive model and the data in it

mahalanobis(): Weighted Mahalanobis Distance

make_parspace(): Define parameter for cognitive models

minkowski(): Weighted Minkowski Distance

nosofsky1989: Test data for fitting the Exemplar-based categorization model

nosofsky1989long: Test data for fitting the Exemplar-based categorization model

npar(): Number of Parameters, Attributes, and Stimuli

parspace(): Show the paramter space of a cognitive model

`+`(<cm>): Adds a component to a cognitivemodel via +

predict(<cm>): Predictions from Cognitive Models (class cm)

print(<csm_constraint>): Prints the constraints of a cogscimodel object nicely

rsenvironment(): Class for risk-sensitive foraging environments

shift_d(): Shifting Cognitive Model

shortfall_d() shortfall_c(): Shortfall Risky Choice Model

shortfalltest: Test data for fitting the Shortfall model

shortfall_cpp(): Computes Predictions in the Shortfall Model

summary(<cm>): Summarizes cognitive models

threshold() threshold_c() threshold_d(): Threshold Model

tversky1992ex: Example data with two risky gambles

utility_pow_d() utility_pow_c(): Utility Function Models

varG(): Variance of probabilistically-described gambles (without N-1 correction)