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| cSFM-package | Covariate-adjusted Skewed Functional Model |
| beta2cp | Transformation between Parameters and B-spline Coefficients |
| case2.b.initial | Initial Estimates of Parameter Functions |
| case2.gr | Negative loglikelihood function and the Gradient |
| case2.unmll.optim | Negative loglikelihood function and the Gradient |
| cp2beta | Transformation between Parameters and B-spline Coefficients |
| cSFM | Covariate-adjusted Skewed Functional Model |
| cSFM.est | Model Estimation with Bivariate Regression B-Splines |
| cSFM.est.parallel | Knots Selection by AIC |
| D.gamma | Reparameterize Skewed Normal Parameterized using Shape and Skewness. |
| D.lg | Standard Skewed Normal Parameterized using Skewness. |
| D.SN | Derivatives of Normalized Skewed Normal Parameterized by Shape |
| data.generator.y.F | Generate Data using Skewed Pointwise Distributions and Gaussian copulas |
| data.simulation | Data with Skewed Marginal Distributions and Gaussian Copula (Simulated) |
| DFT.basis | Discrete Fourier Transformation (DFT) Basis System |
| DST | Data with Skewed Marginal Distributions and Gaussian Copula (Simulated) |
| DSV | Data with Skewed Marginal Distributions and Gaussian Copula (Simulated) |
| fitted.cSFM | Generic Method for 'cSFM' Objects |
| g | Standard Skewed Normal Parameterized using Skewness. |
| kpbb | Kronecker Product Bspline Basis |
| legendre.polynomials | Orthogonal Legendre Polynomials Basis System |
| predict.cSFM | Generic Method for 'cSFM' Objects |
| predict.kpbb | Evaluate a predefined Kronecker product B-spline basis at provided values |
| print.cSFM | Generic Method for 'cSFM' Objects |
| shape.dp | Reparameterize Skewed Normal Parameterized using Shape and Skewness. |
| skewness.cp | Reparameterize Skewed Normal Parameterized using Shape and Skewness. |
| uni.fpca | Functional Principle Component Analysis with Corpula |