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cSFM (version 1.1)
Covariate-adjusted Skewed Functional Model (cSFM)
Description
cSFM is a method to model skewed functional data when considering covariates via a copula-based approach.
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Version
Version
1.1
1.0
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Install
install.packages('cSFM')
Monthly Downloads
17
Version
1.1
License
GPL-2
Maintainer
Meng Li
Last Published
January 23rd, 2014
Functions in cSFM (1.1)
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cSFM.est.parallel
Knots Selection by AIC
DFT.basis
Discrete Fourier Transformation (DFT) Basis System
D.SN
Derivatives of Normalized Skewed Normal Parameterized by Shape
cSFM-package
Covariate-adjusted Skewed Functional Model
case2.b.initial
Initial Estimates of Parameter Functions
Simulation
Data with Skewed Marginal Distributions and Gaussian Copula (Simulated)
uni.fpca
Functional Principle Component Analysis with Corpula
cSFM object
Generic Method for 'cSFM' Objects
unmll
Negative loglikelihood function and the Gradient
Data Simulation
Generate Data using Skewed Pointwise Distributions and Gaussian copulas
cSFM.est
Model Estimation with Bivariate Regression B-Splines
SSN
Standard Skewed Normal Parameterized using Skewness.
legendre.polynomials
Orthogonal Legendre Polynomials Basis System
cp.beta
Transformation between Parameters and B-spline Coefficients
predict.kpbb
Evaluate a predefined Kronecker product B-spline basis at provided values
Reparameterization
Reparameterize Skewed Normal Parameterized using Shape and Skewness.
kpbb
Kronecker Product Bspline Basis