cSFM: Covariate-adjusted Skewed Functional Model (cSFM)

cSFM is a method to model skewed functional data when considering covariates via a copula-based approach.

Author
Meng Li, Ana-Maria Staicu, and Howard D. Bondell
Date of publication
2014-01-23 16:49:05
Maintainer
Meng Li <mli9@ncsu.edu>
License
GPL-2
Version
1.1

View on CRAN

Man pages

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

Files in this package

cSFM
cSFM/NAMESPACE
cSFM/data
cSFM/data/data.simulation.RData
cSFM/R
cSFM/R/Package_BasicSetting.r
cSFM/R/Package_Sim.GenerateData.r
cSFM/R/Package_HAC_RAC_SHAC.r
cSFM/MD5
cSFM/DESCRIPTION
cSFM/man
cSFM/man/DFT.basis.Rd
cSFM/man/predict.kpbb.Rd
cSFM/man/Reparameterization.Rd
cSFM/man/HAC.est.Rd
cSFM/man/data.simulation.Rd
cSFM/man/uni.fpca.Rd
cSFM/man/D.SN.Rd
cSFM/man/cp2beta.Rd
cSFM/man/HAC.est.parallel.Rd
cSFM/man/generic.HAC.Rd
cSFM/man/kpbb.Rd
cSFM/man/SSN.Rd
cSFM/man/case2.unmll.optim.Rd
cSFM/man/data.generator.y.F.Rd
cSFM/man/case2.b.initial.Rd
cSFM/man/legendre.polynomials.Rd
cSFM/man/HAC-package.Rd