awc - Adaptive weights clustering
AWC is an open source python package containing implementation of the novel non-parametric clustering algorithm Adaptive Weights Clustering. The method is fully automatic and does not require to specify the number of clusters or their structure. The procedure is numerically feasible and applicable for high dimensional datasets.
aws - Adaptive weights smoothing
aws is a contributed package within the R-Project for Statistical Computing that contains a reference implementation of the adaptive weights smoothing algorithms for local constant likelihood and local polynomial regression models. Binaries for several operating systems are available from the Comprehensive R Archive Network.
BOP - A Simulator for Large-Scale Process Engineering Problems
The simulator BOP (Block Orientend Process Simulator) is a software package for steady-state, transient and Monte Carlo simulation of large-scale problems from process engineering. The simulation concept is based on a divide-and-conquer strategy which is efficiently applicable on parallel computers with shared memory.
WIAS R-packages for imaging / neuroscience
At WIAS a number of R-packages for image processing are developed focussing on structural adaptive smoothing methods. Applications range from digital color images via functional magnetic resonance imaging to diffusion weighted imaging.
- Partial Differential Equations
- Laser Dynamics
- Numerical Mathematics and Scientific Computing
- Nonlinear Optimization and Inverse Problems
- Interacting Random Systems
- Stochastic Algorithms and Nonparametric Statistics
- Thermodynamic Modeling and Analysis of Phase Transitions
- Nonsmooth Variational Problems and Operator Equations