Package index • gamlss.longitudinal

Standard workflow

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

screen_margin() select_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>): Summarise a fitted longitudinal GAMLSS-copula 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(): Bootstrap inference for fitted models
predict(<gamlss.longitudinal>): Predict from a longitudinal GAMLSS-copula model
marginal_effects(): Counterfactual marginal effects from fitted distributional parameters
tidy(<gamlss.longitudinal>) glance(<gamlss.longitudinal>) augment(<gamlss.longitudinal>): Broom-style table methods for fitted longitudinal GAMLSS models
reporting_table(): Build an applied reporting table from a fitted model
publication_table(): Build a publication-ready 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
diagnostics pithist qqrplot wormplot rootogram proscore procast: Diagnostic generics for fitted longitudinal GAMLSS-copula models
check_missingness(): Check response missingness against observed predictors
copula_time_summary(): Summarise fitted copula parameters by time
plotDist() plot_dist(): Plot marginal and pairwise distribution diagnostics
plot_margin_fit(): Plot an observed marginal response with a fitted GAMLSS density overlay
plot_copula_overlay() plot_copula_fit(): 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>) plot_copula_diagnostics(): 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.

simulate_longitudinal_covariates(): Generate covariates for longitudinal simulations
simulate_longitudinal_dataset(): Simulate longitudinal GAMLSS-copula data
adoption_benchmark_scenarios(): Adoption benchmark scenario grid
run_adoption_benchmarks(): Run adoption benchmark scenarios
run_coverage_simulations(): Run coverage simulation cases
benchmark_comparator_status(): List available standard longitudinal benchmark comparators
benchmark_standard_models(): Fit standard longitudinal comparator models for benchmarks
summarise_benchmark_results(): Summarise benchmark simulation results into win/tie/loss tables
format_benchmark_report(): Format a benchmark report
write_benchmark_report(): Write a 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