R (>= 2.14.0), methods, awsMethods (>= 1.0-1), adimpro,
oro.nifti (>= 0.3.9), oro.dicom, rgl, gsl, parallel

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")))
methods, parallel, oro.nifti (>= 0.3.9), oro.dicom, adimpro,
gsl, rgl
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.
Karsten Tabelow [aut, cre],
Joerg Polzehl [aut],
Felix Anker [ctb]

methods, parallel, oro.nifti (>= 0.3.9), oro.dicom, gsl,
quadprog

methods, parallel, oro.nifti (>= 0.3.9), oro.dicom, gsl

Analysis of Diffusion Weighted Imaging (DWI) Data
R (>= 3.1.0), awsMethods (>= 1.0-1), adimpro, rgl

methods, parallel, oro.nifti (>= 0.3.9), oro.dicom, gsl,
quadprog, Rsolnp

methods, parallel, adimpro (>= 0.9), rgl, oro.nifti (>=
0.3.9), oro.dicom, gsl, quadprog

methods, parallel, adimpro (>= 0.9), aws (>= 2.4.1), rgl,
oro.nifti (>= 0.3.9), oro.dicom, gsl, quadprog

Compute the dynamic threshold panel model suggested by (Stephanie Kremer, Alexander Bick and Dieter Nautz (2013) <doi:10.1007/s00181-012-0553-9>) in which they extended the (Hansen (1999) <doi: 10.1016/S0304-4076(99)00025-1>) original static panel threshold estimation and the Caner and (Hansen (2004) <doi:10.1017/S0266466604205011>) cross-sectional instrumental variable threshold model, where generalized methods of moments type estimators are used.