Function reference
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albrecht2019exp1
- Test data for fitting the Exemplar-based judgment model
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anova(<cm>)
- Analysis of Deviance for Cogscimodel Fits
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baseline_const_c()
baseline_const_d()
baseline_mean_c()
baseline_mean_d()
- Baseline models
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bayes_beta_c()
bayes_beta_d()
bayes_dirichlet_d()
bayes_dirichlet_c()
bayes()
- Bayesian Inference Cognitive Model
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softmax()
epsilon_greedy()
epsilon()
luce()
argmax()
- Choicerule Models (action-selection rules)
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chr_as_rhs()
- Convert a character to formula
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Cm
cm-class
- The R6 class underlying all "cm" (cognitive model) objects 'Cm$new(formula, data, parspace)'
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cm_choicerules()
- Show the Choicerules for Discrete Cognitive Models
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cm_options()
- Advanced Options for Cognitive Models
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cm_solvers()
- Show the Optimization Solvers for Cognitive Models
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cognitivemodel()
- Add components to a model
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constraints()
- Show the constraints of a cognitive model
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cpt_d()
cpt_c()
cpt_mem_d()
cpt_mem_c()
- Cumulative Prospect Theory Models
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cpttest
- Test data for fitting the Cumulative Prospect Theory, cpt, model
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.grid()
- Make a regular or random grid
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.solve_grid_constraint()
- Adds the parameter that are constrained to the free parameters in the construction of a grid
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ebm_cpp()
- Computes Predictions for the Exemplar-based Models (GCM, EBM)
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end()
- Ends building a cognitivemodel via
+
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fit()
- Estimates free parameters of a cognitvemodel generated via
+
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fun()
- Adds a function to a cognitivemodel via
+
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getCall(<cm>)
- Get the Call to a cognitive model object
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get_ev()
- Gets the expected value of gambles
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get_p()
- Gets the probabilities of gambles
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get_var()
- Gets the variance of gambles
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get_x()
- Gets the outcomes of gambles
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logLik(<cm>)
MSE()
RMSE.cm()
SSE()
SSE()
- Computes Various Model Fit Measures
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hm1988()
- Dynamic optimization model for risk-sensitive foraging problems in discrete time
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npar()
nobs()
nstim()
natt()
coef(<cm>)
- Information about a cognitive model and the data in it
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mahalanobis()
- Weighted Mahalanobis Distance
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make_parspace()
- Define parameter for cognitive models
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minkowski()
- Weighted Minkowski Distance
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nosofsky1989
- Test data for fitting the Exemplar-based categorization model
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nosofsky1989long
- Test data for fitting the Exemplar-based categorization model
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npar()
- Number of Parameters, Attributes, and Stimuli
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parspace()
- Show the paramter space of a cognitive model
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`+`(<cm>)
- Adds a component to a cognitivemodel via
+
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predict(<cm>)
- Predictions from Cognitive Models (class cm)
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print(<csm_constraint>)
- Prints the constraints of a cogscimodel object nicely
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rsenvironment()
- Class for risk-sensitive foraging environments
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shift_d()
- Shifting Cognitive Model
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shortfall_d()
shortfall_c()
- Shortfall Risky Choice Model
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shortfalltest
- Test data for fitting the Shortfall model
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shortfall_cpp()
- Computes Predictions in the Shortfall Model
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summary(<cm>)
- Summarizes cognitive models
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threshold()
threshold_c()
threshold_d()
- Threshold Model
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tversky1992ex
- Example data with two risky gambles
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utility_pow_d()
utility_pow_c()
- Utility Function Models
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varG()
- Variance of probabilistically-described gambles (without N-1 correction)