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... (for reading image data). The code is extremely compact. Minimizing code length is a major goal. As a result, the core of the algorithms can be easily spotted. Many tricks for speeding up Matlab code are applied (e.g. vectorization, matrix factorization, etc.). Usually, functions in this package are orders faster than Matlab builtin ones (e.g. kmeans). Many tricks for numerical stability are applied, such as computing probability in logrithm domain, square root matrix update to enforce matrix symmetry.

..., and thus the process may be repeated to obtain nth order derivative files. The package is particularly appealing for applications where the same derivative must be found at multiple different points, i.e. non-linear root finding/optimization, stiff ode integration, etc.