An R package implementing variable selection methodology for Gaussian model-based clustering which allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting MCLUST models. By default the algorithm uses a sequential search, but parallelisation is also available.
R (>= 3.2), mclust (>= 5.2), BMA (>= 3.18), foreach, iterators

Package contains most of the popular internal and external cluster validation methods ready to use for the most of the outputs
produced by functions coming from package "cluster". Package contains also functions and examples of usage for cluster
stability approach applied to algorithms implemented in "cluster" package.

Package contains most of the popular internal and external cluster validation methods ready to use for the most of the outputs
produced by functions comming from package "cluster". Package contains also functions and examples of usage for  cluster
stablility for alghoritms implemented in "cluster" package.

Package contains most of the popular internal and external cluster validation methods ready to use for the most of the outputs
produced by functions coming from package "cluster". Package contains also functions and examples of usage for cluster
stability approach that might be applied to algorithms implemented in "cluster" package as well as user defined clustering algorithms.

Package contains most of the popular internal and external
cluster validation methods ready to use for the most of the
outputs produced by functions coming from package "cluster".
Package contains also functions and examples of usage for
cluster stability approach that might be applied to algorithms
implemented in "cluster" package as well as user defined
clustering algorithms.

Analysis of Cardiac Magnetic Resonance Images
c(person("Volker", "Schmid", role = c("aut", "cre"),
email = "volker.schmid@lmu.de"))
Matrix, splines, fields, graphics, parallel
Computes maximum response from Cardiac Magnetic Resonance Images using spatial and voxel wise spline based Bayesian model. This is an implementation of the methods described in Schmid (2011) <doi:10.1109/TMI.2011.2109733> "Voxel-Based Adaptive Spatio-Temporal Modelling of Perfusion Cardiovascular MRI". IEEE TMI 30(7) p. 1305 - 1313.
knitr, rmarkdown, codetools, testthat (>= 3.0.0), R.rsp