Package index • gamlss.longitudinal

Standard workflow

Adoption-facing helpers for the recommended screen, fit, check, predict workflow.

select_margin() screen_margin(): Select candidate marginal distributions
best_fit() best_fit_family(): Extract the best-fitting candidate from a selection result
select_copula(): Select a bivariate copula family from pseudo-observation pairs
select_joint_distribution(): Select margin and copula families by joint longitudinal fit
gamlss_longitudinal(): Fit a longitudinal joint regression model
summary(<gamlss.longitudinal>): Summarize a fitted gamlss.longitudinal model
coef(<gamlss.longitudinal>): Coefficients for a fitted longitudinal GAMLSS-copula model
vcov(<gamlss.longitudinal>): Variance-covariance matrix for a fitted longitudinal GAMLSS-copula model
logLik(<gamlss.longitudinal>): Log-likelihood for a fitted longitudinal GAMLSS-copula model
formula(<gamlss.longitudinal>) terms(<gamlss.longitudinal>) nobs(<gamlss.longitudinal>) model.frame(<gamlss.longitudinal>) fitted(<gamlss.longitudinal>) residuals(<gamlss.longitudinal>): Access components of a fitted longitudinal GAMLSS-copula model

Reportable intervals, hypothesis tests, likelihood comparisons, and effects.

confint(<gamlss.longitudinal>): Confidence intervals for fixed coefficients
wald_test(): Wald tests for fixed coefficients
likelihood_compare(): Compare fitted models with likelihood-ratio summaries
bootstrap_inference(): Parametric bootstrap inference for fitted models
predict(<gamlss.longitudinal>): Predict from a longitudinal GAMLSS-copula model
marginal_effects(): Counterfactual marginal effects from fitted distributional parameters
reporting_table(): Build an applied reporting table from a fitted model
model_spec(): Audit a fitted longitudinal GAMLSS-copula model specification
simulate(<gamlss.longitudinal>): Simulate responses from a longitudinal GAMLSS-copula model

Decision-oriented checks and visual diagnostics for fitted models.

check_model(): Check a fitted longitudinal GAMLSS-copula model
check_missingness(): Check response missingness against observed predictors
copula_time_summary(): Summarise fitted copula parameters by time
topmodels_diagnostics pithist qqrplot wormplot rootogram proscore procast: Diagnostic generics for fitted longitudinal GAMLSS-copula models
plot_dist() plotDist(): Plot marginal and pairwise distribution diagnostics
plot_margin_fit(): Plot an observed marginal response with a fitted GAMLSS density overlay
plot_copula_fit() plot_copula_overlay(): Plot empirical pseudo-observation pairs with a fitted copula overlay
plot(<gamlss.longitudinal>): Plot diagnostics dashboard for fitted gamlss.longitudinal objects
plot(<copula_time_summary>): Plot fitted copula trends by time
plot(<copula_contour_compare>): Compare fitted and empirical copula contour surfaces
plot_copula_diagnostics() plot(<copula>): Plot copula diagnostics for a fitted gamlss.longitudinal object
plot_terms(): Plot term effects for a fitted longitudinal model
plot_fixed_terms(): Plot all fixed terms with confidence bands
plot_smooth_terms(): Plot all smooth terms with confidence bands

Data generators and opt-in comparator benchmarks for adoption evidence.

simulate_longitudinal_covariates(): Generate covariates for longitudinal simulations
simulate_longitudinal_dataset(): Simulate longitudinal GAMLSS-copula data
run_coverage_simulations(): Run opt-in distribution/copula/method coverage simulations
benchmark_comparator_status(): List available standard longitudinal benchmark comparators
benchmark_standard_models(): Fit standard longitudinal comparator models for adoption benchmarks
adoption_benchmark_scenarios(): Return named adoption benchmark scenarios
run_adoption_benchmarks(): Run repeated adoption benchmark scenarios
summarise_benchmark_results(): Summarise benchmark simulation results into win/tie/loss tables
format_benchmark_report(): Format an adoption benchmark report
write_benchmark_report(): Write an adoption benchmark report
write_coverage_summary_report(): Write a LaTeX summary report for coverage simulations

Small helper functions used in simulation examples.

sim_factor_effect(): Look up named effects for a factor-like variable
sim_rescale01(): Rescale a numeric vector to the unit interval
sim_smooth_linear() sim_smooth_sin() sim_smooth_bump() sim_smooth_sigmoid() sim_smooth_u() sim_smooth_wiggle(): Simple deterministic smooth shapes for simulation truth