Non-Crossing Additive Regression Quantiles and Non-Parametric Growth Charts
Easy computing growth charts
Growth charts regression quantiles with automatic smoothness estimatio...
Log Likelihood, AIC and BIC for gcrq objects
Estimation of noncrossing regression quantiles with monotonicity restr...
Plot method for gcrq objects
Prediction for "gcrq" objects
Print method for the gcrq class
Specifying a smooth term in the gcrq formula.
Non-Crossing Additive Regression Quantiles and Non-Parametric Growth C...
Summarizing model fits for growth charts regression quantiles
Variance-Covariance Matrix for a Fitted 'gcrq' Model
Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties. Random intercepts and variable selection are allowed via the lasso penalties. The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) <doi:10.1177/1471082X20929802>. Monotonicity and concavity constraints on the fitted curves are allowed, see Muggeo and others (2013) <doi:10.1007/s10651-012-0232-1>, and also <doi:10.13140/RG.2.2.12924.85122> or <doi:10.13140/RG.2.2.29306.21445> some code examples.