Earlier this year, I put a native function package, catted "mtx," on
GitHub. I've no idea if anyone is using it, but I just put up a new
release. The biggest change is that the new version uses the GNU gsl
library rather than an ad-hoc eigensystems package I used in the first
release, so the new version is easier to install.
A new feature is some massaging of the covariance and eigensystems code
to allow principal component analysis of statistical data*--see the
README for an example. That's in addition to the existing capabilities:
* Matrix determinants
* Matrix eigenvalues and eigenvectors
* Identity matrices
* Vector cross products
* Vector interior angles
* Vector or scalar rotation matrices
* Gaussian complex random values
* Vector/matrix normalisation
* Homogeneous matrices
* Covariance
Anyway, it's at github.com/ChrisMoller/mtx
* One of my sons is a medical physicist and one the newer things in
that is using PCA to use sampled radiomic data to model the shapes of
cancerous regions. He asked me for the capability, but I have no idea
how useful it will be for anyone else.
