Package index • beezdiscounting

Mixed-effects discounting (TMB)

Fit SLT-beta mixed-effects discounting models to bounded indifference-point data using TMB, and simulate from the same model.

fit_dd_tmb(): Fit an indifference-point mixed-effects discounting model via TMB
simulate_dd_ip(): Simulate IP-family mixed-effects discounting data
VarCorr(<beezdiscounting_tmb>): Random-effect covariance for a TMB discounting model

Choice-based discounting (TMB)

Fit the structural or descriptive (Young 2018) SS-vs-LL choice model — a binomial GLMM — and simulate from either model. The descriptive mode adds a VarCorr method for the random-effect covariance.

fit_dd_choice(): Fit a trial-level SS-vs-LL choice model (binomial GLMM) via TMB
simulate_dd_choice(): Simulate trial-level SS-vs-LL choice data
VarCorr(<beezdiscounting_choice>): Random-effect (co)variances for a beezdiscounting_choice fit

Bayesian discounting (brms)

Bayesian mixed-effects discounting via brms/Stan: the indifference-point and structural-choice fitters with their default priors.

fit_dd_brms(): Fit a Bayesian Mixed-Effects Discounting Model via brms
fit_dd_choice_brms(): Fit a Bayesian Structural Choice Discounting Model via brms
default_dd_priors(): Default priors for Bayesian (brms) delay-discounting models
default_dd_choice_priors(): Default priors for the Bayesian (brms) choice model

Estimated marginal means & comparisons

Post-hoc estimated marginal means and pairwise comparisons for fitted mixed-effects discounting models.

get_dd_param_emms(): Estimated marginal means of the discount rate k
get_dd_comparisons(): Factor-level comparisons of the discount rate k

Model inspection (S3 methods)

S3 methods for extracting information from fitted models.

tidy(<beezdiscounting_brms>): Tidy a beezdiscounting_brms model
tidy(<beezdiscounting_choice>): Tidy a beezdiscounting_choice model into a coefficient tibble
tidy(<beezdiscounting_choice_brms>): Tidy a beezdiscounting_choice_brms model
tidy(<beezdiscounting_comparison>): Tidy a discounting comparison into a flat contrasts frame
tidy(<beezdiscounting_tmb>): Tidy a beezdiscounting_tmb model into a coefficient tibble
glance(<beezdiscounting_brms>): Glance at a beezdiscounting_brms model
glance(<beezdiscounting_choice>): Glance at a beezdiscounting_choice model
glance(<beezdiscounting_choice_brms>): Glance at a beezdiscounting_choice_brms model
glance(<beezdiscounting_tmb>): Glance at a beezdiscounting_tmb model
augment(<beezdiscounting_choice>): Augment a beezdiscounting_choice model
augment(<beezdiscounting_tmb>): Augment a beezdiscounting_tmb model
predict(<beezdiscounting_brms>): Predict from a beezdiscounting_brms model
predict(<beezdiscounting_choice>): Predict from a structural choice discounting model
predict(<beezdiscounting_choice_brms>): Predict P(LL) from a beezdiscounting_choice_brms model
predict(<beezdiscounting_tmb>): Predict from a TMB mixed-effects discounting model
confint(<beezdiscounting_brms>): Credible intervals for a beezdiscounting_brms model
confint(<beezdiscounting_choice>): Confidence intervals for a structural choice discounting model
confint(<beezdiscounting_choice_brms>): Credible intervals for a beezdiscounting_choice_brms model
confint(<beezdiscounting_tmb>): Confidence intervals for a TMB discounting model
summary(<beezdiscounting_brms>): Summarize a beezdiscounting_brms model
summary(<beezdiscounting_choice>): Summarize a structural choice discounting fit
summary(<beezdiscounting_choice_brms>): Summarize a beezdiscounting_choice_brms model
summary(<beezdiscounting_tmb>): Summarize a TMB mixed-effects discounting fit
coef(<beezdiscounting_choice>): Extract coefficients from a structural choice model
coef(<beezdiscounting_tmb>): Extract coefficients from a TMB discounting model
fixef(<beezdiscounting_choice>): Extract fixed effects from a structural choice model
fixef(<beezdiscounting_tmb>): Extract fixed effects from a TMB discounting model
ranef(<beezdiscounting_choice>): Extract subject-level random effects from a choice model
ranef(<beezdiscounting_tmb>): Extract subject-level random effects from a TMB discounting model
fitted(<beezdiscounting_choice>): Fitted values for a beezdiscounting_choice fit
fitted(<beezdiscounting_tmb>): Fitted values for a beezdiscounting_tmb fit
residuals(<beezdiscounting_choice>): Residuals for a beezdiscounting_choice fit
residuals(<beezdiscounting_tmb>): Residuals for a beezdiscounting_tmb fit
print(<beezdiscounting_choice>): Print a structural choice discounting fit
print(<beezdiscounting_tmb>): Print a TMB mixed-effects discounting fit
print(<summary.beezdiscounting_choice>): Print a structural choice discounting model summary
print(<summary.beezdiscounting_tmb>): Print a TMB discounting model summary

Delay discounting (NLS / scoring)

Fit hyperbolic/exponential discount functions via nonlinear regression and score delay-discounting tasks.

fit_dd(): Fit Delay-Discounting Model
results_dd(): Extract Results from Delay-Discounting Model
plot_dd(): Plot Delay-Discounting Model
calc_dd(): Calculate scores, answers, and timing for 5.5 trial delay discounting from Qualtrics template
score_dd(): Score 5.5 trial delay discounting from Qualtrics template
ans_dd(): Converts answers from 5.5 trial delay discounting from Qualtrics template
timing_dd(): Extract timing metrics from 5.5 trial delay discounting from Qualtrics template

Probability discounting

Scoring functions for probability-discounting tasks.

calc_pd(): Calculate scores, answers, and timing for 5.5 trial probability discounting from Qualtrics template
score_pd(): Score 5.5 trial probability discounting from Qualtrics template
ans_pd(): Converts answers from 5.5 trial probability discounting from Qualtrics template
timing_pd(): Extract timing metrics from 5.5 trial probability discounting from Qualtrics template

Monetary Choice Questionnaire (MCQ, 27- and 21-item)

Scoring, lookup, simulation, and reshaping for the 27-item (Kirby, Petry, & Bickel, 1999) and 21-item (Kirby & Marakovic, 1996) MCQ.

score_mcq(): Score an MCQ (27- or 21-item)
score_mcq27(): Score 27-item MCQ
summarize_mcq(): Provide a summary of the results from the MCQ output table.
prop_ss(): Calculate proportion of SIR/SS responses at each k value
get_lookup_table(): Get internal lookup table for the 27- or 21-item MCQ
mcq_to_choice(): Convert 27- or 21-item MCQ responses to a trial-level choice frame
mcq27_to_choice(): Convert 27-item MCQ responses to a trial-level choice frame
generate_data_mcq(): Generate fake MCQ data
wide_to_long_mcq(): Reshape MCQ data wide to long
long_to_wide_mcq(): Reshape MCQ data long to wide
wide_to_long_mcq_excel(): Reshape MCQ data from wide (as used in the 21- and 27-Item Monetary Choice Questionnaire Automated Scorer) to long
long_to_wide_mcq_excel(): Reshape MCQ data from long to wide (as used in the 21- and 27-Item Monetary Choice Questionnaire Automated Scorer)

Utilities & metrics

Area under the curve, fit metrics, and systematicity checks.

calc_aucs(): Calculate Area-Under-the-Curve (AUC) Metrics for Delay Discounting Data
calc_r2(): Calculate R-Squared for a Model
calc_conf_int(): Calculate Confidence Intervals for a Parameter
check_unsystematic(): Check for Unsystematic Data Violations

Visualization

Plot methods for fitted discounting models – discount curves, Bayesian posterior bands, group-comparison forest plots, and random-effect QQ diagnostics – plus plotting for scored task outputs.

plot(<beezdiscounting_brms>): Plot a Bayesian (brms) indifference-point discounting model
plot(<beezdiscounting_choice>): Plot a choice-based discounting model
plot(<beezdiscounting_choice_brms>): Plot a Bayesian (brms) choice-based discounting model
plot(<beezdiscounting_comparison>): Plot group differences in discount rate
plot(<beezdiscounting_tmb>): Plot a mixed-effects discounting model
plot(<prop_ss_output>): Plot Proportion of SIR/SS Choices by k Value
plot(<score_mcq27_output>): Plot MCQ-27 Scores
plot(<score_mcq_output>): Plot MCQ Scores
plot_dd(): Plot Delay-Discounting Model
plot_qq(<beezdiscounting_tmb>) plot_qq(<beezdiscounting_choice>): Random-effect normal QQ plots for discounting models

Datasets

Built-in datasets for examples and testing.

dd_ip: Delay Discounting Data
mcq27: Example 27-item MCQ data
mcq21: Example 21-item MCQ data
five.fivetrial_dd: Example Qualtrics output from the 5.5 trial delay discounting template.
five.fivetrial_pd: Example Qualtrics output from the 5.5 trial probability discounting template.

Reexports

Generics re-exported from other packages.

reexports augment tidy glance fixef ranef plot_qq: Objects exported from other packages
`%>%`: Pipe operator