| %cross% | Matrix cross-multiplication between two matrices |
| %mult% | Matrix multiplication between two matrices |
| %vec% | Matrix multiplication between a matrix and a vector |
| colSums2 | colSums of a matrix |
| constraint | Sum-to-zero constraint |
| cor.var | Implementation of the corrected variance Vc |
| crs | Bases for cubic regression splines (equivalent to "cr" in 'mgcv') |
| crs.FP | Penalty matrix constructor for cubic regression splines |
| datCancer | Patients diagnosed with cervical cancer |
| deriv_R | Derivative of a Choleski factor |
| design.matrix | Design matrix for the model needed in Gauss-Legendre quadrature |
| grad_rho | Gradient vector of LCV and LAML wrt rho (log smoothing parameters) |
| Hess_rho | Hessian matrix of LCV and LAML wrt rho (log smoothing parameters) |
| instr | Position of the nth occurrence of a string in another one |
| inv.repam | Reverses the initial reparameterization for stable evaluation of the log determinant of the penalty matrix |
| model.cons | Design and penalty matrices for the model |
| NR.beta | Inner Newton-Raphson algorithm for regression parameters estimation |
| NR.rho | Outer Newton-Raphson algorithm for smoothing parameters estimation via LCV or LAML optimization |
| predict.survPen | Hazard and Survival prediction from fitted 'survPen' model |
| print.summary.survPen | print summary for a 'survPen' fit |
| pwcst | Defining piecewise constant (excess) hazard in survPen formulae |
| rd | Defining random effects in survPen formulae |
| repam | Applies initial reparameterization for stable evaluation of the log determinant of the penalty matrix |
| smf | Defining smooths in survPen formulae |
| smooth.cons | Design and penalty matrices of penalized splines in a smooth.spec object |
| smooth.cons.integral | Design matrix of penalized splines in a smooth.spec object for Gauss-Legendre quadrature |
| smooth.spec | Covariates specified as penalized splines |
| summary.survPen | Summary for a 'survPen' fit |
| survPen | (Excess) hazard model with (multidimensional) penalized splines and integrated smoothness estimation |
| survPen.fit | (Excess) hazard model with multidimensional penalized splines for given smoothing parameters |
| survPenObject | Fitted survPen object |
| tensor | Defining smooths in survPen formulae |
| tensor.in | tensor model matrix for two marginal bases |
| tensor.prod.S | Tensor product for penalty matrices |
| tensor.prod.X | tensor model matrix |
| tint | Defining smooths in survPen formulae |