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synfd

Synthesize dense/sparse functional data/snippets

Install

devtools::install_github("linulysses/synfd")

Examples

generate irregularly observed Gaussian functional data with Matern covariance function

Y <- irreg.fd(mu=1, X=gaussian.process(), n=10, m=5)

generate samples froma a process defined via K-L representation

Y <- irreg.fd(mu=cos, X=kl.process(eigen.values=1/(2^(1:25)),eigen.functions='FOURIER',distribution='LAPLACE'),n=10, m=5)

generate regularly observed trajectories

Y <- reg.fd(mu=1, X=gaussian.process(cov=matern), n=10, m=101)

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Version

Install

install.packages('synfd')

Monthly Downloads

9

Version

0.1.3

License

GPL-3

Maintainer

Zhenhua Lin

Last Published

June 4th, 2020

Functions in synfd (0.1.3)

rep.row

replicate a row vector into a matrix
regular.grid

Generate a Regular Grid
rfd

Sample Functional Data
gen.mul.data

Generate multivariate data with the given mean vector and covariance matrix
white.noise

Create a White Noise Process
wiener.process

Create a Wiener Process
rep.col

replicate a column vector into a matrix
matern

The Matern covariance function
plot.dense.fd

Plot Regular Functional Data
gaussian.process

Create a Centered Gaussian Process
centered.process

Create a Centered Random Process
evaluate.basis

Evaluate Orthonormal Basis Functions
plot.sparse.fd

Plot Irregular Functional Data
kl.process

Create a Process via Karhunen-Loeve Representation
irreg.fd

Sample Irregular Functional Data
reg.fd

Sample Regular Functional Data