Package: dti
Version: 1.2-4.1
Date: 2016-02-08
Title: Analysis of Diffusion Weighted Imaging (DWI) Data
Authors@R: c(person("Karsten", "Tabelow", role = c("aut", "cre"),
email = "karsten.tabelow@wias-berlin.de"),
person("Joerg", "Polzehl", role = c("aut"),
email = "joerg.polzehl@wias-berlin.de"),
person("Felix", "Anker", role = c("ctb")))
Depends: R (>= 3.1.0), awsMethods (>= 1.0-1), adimpro, rgl
Imports: methods, parallel, oro.nifti (>= 0.3.9), oro.dicom, gsl, quadprog
LazyData: TRUE
Description: Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging
modality, that measures diffusion of water in tissues like the human
brain. The package contains R-functions to process diffusion-weighted
data. The functionality includes diffusion tensor imaging (DTI),
diffusion kurtosis imaging (DKI), modeling for high angular resolution
diffusion weighted imaging (HARDI) using Q-ball-reconstruction and
tensor mixture models, several methods for structural adaptive
smoothing including POAS and msPOAS, and a streamline fiber tracking
for tensor and tensor mixture models.
The package provides functionality to manipulate and visualize results
in 2D and 3D.
License: GPL (>= 2)
Copyright: This package is Copyright (C) 2005-2016 Weierstrass
Institute for Applied Analysis and Stochastics.
URL: http://www.wias-berlin.de/research/ats/imaging/
NeedsCompilation: yes
Packaged: 2016-02-09 07:51:33 UTC; tabelow
Author: Karsten Tabelow [aut, cre],
Joerg Polzehl [aut],
Felix Anker [ctb]
Maintainer: Karsten Tabelow <karsten.tabelow@wias-berlin.de>
Repository: CRAN
Date/Publication: 2016-02-09 09:59:00
Package: aws
Version: 1.9-4
Date: 2014-03-04
Title: Adaptive Weights Smoothing
Authors@R: c(person("Joerg","Polzehl",role=c("aut","cre"),email="joerg.polzehl@wias-berlin.de"),person("Felix","Anker",role=c("ctb")))
Depends: R (>= 2.14.0), methods, awsMethods (>= 1.0-1), gsl
Description: The package contains R-functions implementing the
Propagation-Separation Approach to adaptive smoothing as
described in J. Polzehl and V. Spokoiny (2006),
Propagation-Separation Approach for Local Likelihood Estimation,
Prob. Theory and Rel. Fields, 135(3):335--362.
and J. Polzehl and V. Spokoiny (2004) Spatially adaptive
regression estimation: Propagation-separation approach,
WIAS-Preprint 998.
License: GPL (>= 2)
Copyright: This package is Copyright (C) 2005-2014 Weierstrass
Institute for Applied Analysis and Stochastics.
URL: http://www.wias-berlin.de/projects/matheon_a3/
Packaged: 2014-03-05 13:43:22 UTC; polzehl
Author: Joerg Polzehl [aut, cre],
Felix Anker [ctb]
Maintainer: Joerg Polzehl <joerg.polzehl@wias-berlin.de>
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-03-05 18:21:44
providing 
.