Title: Quadratic Programming using Cyclic Projections
Description: Solves quadratic programming problems using Richard L. Dykstra's cyclic projection algorithm. Routine allows for a combination of equality and inequality constraints. See Dykstra (1983) <doi:10.1080/01621459.1983.10477029> for details.

Title: non-parametric denoising and clustering method of noisy images
Author: Yves Rozenholc, Christophe Pouzat, Tiffany Lieury
Description: The aim of the method is first to denoise each temporal
sequence using both the spatial and temporal information
contained in the data, and then to cluster them according to
their dynamic features. The method is based on the assumption
that a finite sequence of noisy images both indexed by time and
space is composed of noisy versions of only a limited amount of

Package: DynClust
Type: Package
Title: non-parametric denoising and clustering method of noisy images
both indexed by time and space
Version: 1.1
Date: 2012-07-31
Author: Yves Rozenholc, Christophe Pouzat, Tiffany Lieury
Maintainer: Tiffany Lieury <lieuryt-dynclust@yahoo.fr>
Description: Two-stage method for the denoising and clustering of stack
of noisy images acquired over time, based on the simple
assumption that a finite sequence of noisy images both indexed
by time and space is composed of noisy versions of only a
limited amount of dynamic features. The aim of the method is
first to denoise signals using both the spatial and temporal
information contained in the data, and then cluster the
denoised signals depending on their dynamic features. Two
signals are considered to have similar features if their
difference does not significantly deviate from zero. By
comparing difference signals, no assumption is therefore made
on the shape of the theoretical signals. In order for the
method to be applicable to experimental data, the data should
be normally distributed (or at least follow a symmetric
distribution) with a constant variance. Also the number of
observations n must be of the form n=d^2. Moreover, the method
is based on the implicit assumption that, for a given data set,
almost each dynamic feature is present in two or more pixels.
The use of the method can be time consumming depending on the
size of the data-array (for example about 2 hours for
130x175x60 data set on a PC dual core processor 2.80 GHz, RAM 4
Go, Ubuntu Maverick 64 bits, R 2.12.1).
Depends: R (>= 2.10)
License: GPL (>= 2)
Packaged: 2012-08-31 11:07:46 UTC; tiffany
Repository: CRAN
Date/Publication: 2012-08-31 13:14:50