jblas is a fast linear algebra library for Java. jblas is based on BLAS and LAPACK, the de-facto industry standard for matrix computations, and uses state-of-the-art implementations like ATLAS for all its computational routines, making jBLAS very fast.
jblas can is essentially a light-wight wrapper around the BLAS and LAPACK routines. These packages have originated in the Fortran community which explains their archaic API. On the other hand modern implementations are hard to beat performance wise. jblas aims to make this functionality available to Java programmers such that they do not have to worry about writing JNI interfaces and calling conventions of Fortran code.
This software is released under a BSD-style license.
psldof package for R provides
Degrees of Freedom estimates for Kernel Partial Least Squares
Regression and its kernel extension. The package also provides
model selection based on various information criteria (aic, bic,
gmdl) and based on cross-validation.
This is joint work with Nicole Krämer
For more options, have a look at the CRAN page of plsdof.
This software contains several matlab scripts for computing the RDE (relevant dimensionality estimate). The RDE measures the number of leading PCA components in feature space which contain the relevant information about the labels in a supervised learning problem.
The package also contains methods for plotting the kernel PCA components, performing model selection based on the RDE, and visualizing the RDE for different kernel parameters. Implementations of linear, polynomial, Gaussian, and rational quadratic kernels are also contained.
For more information, have a look at the README file and at the
rdetools.m script, which contains a small demo of how to use the
If you use this software for your research, please cite the following paper:
This software is released under the GNU Public License, v3.