svcm: 2d and 3d Space-Varying Coefficient Models
Version 0.1.2

2d and 3d space-varying coefficient models are fitted to regular grid data using either a full B-spline tensor product approach or a sequential approximation. The latter one is computationally more efficient. Resolution increment is enabled.

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AuthorSusanne Heim, with support from Paul Eilers, Thomas Kneib, and Michael Kobl
Date of publication2009-06-14 19:13:12
MaintainerSusanne Heim <susanneheim@gmx.net>
LicenseGPL (>= 2)
Version0.1.2
URL http://www.statistik.lmu.de/~heim
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("svcm")

Man pages

brain2d: Two-dimensional Diffusion Weighted Dataset
brain3d: Three-dimensional Diffusion Weighted Dataset
cleversearch: Optimization over a parameter grid
resolution: Re-scaling resolution of SVCM predictors and effects
svcm: Fitting space-varying coefficient models
svcm-package: 2d and 3d Space-Varying Coefficient Models

Functions

EDofTP Source code
SEQpsi Source code
brain2d Man page
brain3d Man page
calknots Source code
cleversearch Man page Source code
dsC2env Source code
predictsvcm Source code
resolution Man page Source code
svcm Man page Source code
svcm-package Man page
targetsvcm Source code

Files

MD5
TODO
tests
tests/svcm3d.R
src
src/dsC2env.c
src/combi.cpp
R
R/targetsvcm.R
R/svcm.R
R/SEQpsi.R
R/resolution.R
R/predictsvcm.R
R/EDofTP.R
R/dsC2env.R
R/cleversearch.R
R/calknots.R
NAMESPACE
man
man/svcm.Rd
man/svcm-package.Rd
man/resolution.Rd
man/cleversearch.Rd
man/brain3d.Rd
man/brain2d.Rd
inst
inst/doc
inst/doc/svcm_resol_2dexample.pdf
inst/doc/svcm_3dexample.pdf
inst/doc/svcm_2dexample.pdf
inst/doc/svcm.pdf
inst/doc/resolution_scheme.pdf
inst/doc/data_3dexample.pdf
inst/CITATION
DESCRIPTION
data
data/brain3d.rda
data/brain2d.rda
COPYING
svcm documentation built on May 20, 2017, 2:17 a.m.