Numerical algorithms inspired by Numerical Recipes. These use the classes {@link nr.Vec} for vectors and {@link nr.Mat} for matrices that are simply interfaces that provide get/set methods for individual elements. Use the {@link nr.Vec_array} and {@link nr.Mat_array} for simple implementations that use double[] and double[][].

Many of the algorithms (mostly the ODE solvers and extrapolation) are from Numerical Recipes, but implemented by me, and I believe that none of these violate their copyright. These routines are released under the Sun Public License, but please acknowledge me.

The matrix decompositions are from the jama package from the National Institute of Standards and Technology. Those are written in Java and are in the public domain, so no concern about violating NR's overly restrictive licensing.

The statistical and random functions are from the CERN COLT package. This is probably the best collection of JAVA numerical algorithms, and I should use them more.

On a personal note, I think NR is great, and have been using since the first C version, but agree with the comments on Amazon. It's still written in FORTRAN, despite nominally being translated to C++. This is obvious from the 6-letter routine names and the use of i-n initial letters for integers, but even more so in the algorithms. They are all written as procedural code, with no use of object-oriented language features even where they would be ideal. They use global variables and hard-coded function names to pass things "behind the back" of their routines, where they should be using instance variables and methods. Actually using their routines (especially in a different language like Java) requires a fair amount of translation. The algorithms themselves, and the text, are very good.

I did not use the jama.Matrix class directly, because I wanted the vectors and matrices to be interfaces rather than classes. This gives me more flexibility in using other data structures especially sub-vectors and sub-matrices. It does, however, sacrifice performance, since the CERN and NIST data structures are finely tuned for performance.