A lot of that is up and coming. https://github.com/DlangScience https://github.com/DlangScience [x] Plotting: http://code.dlang.org/packages/plot2kill http://

A lot of that is up and coming. https://github.com/DlangScience https://github.com/DlangScience [x] Plotting: http://code.dlang.org/packages/plot2kill http://code.dlang.org/packages/plot2kill [+] Optimization: Nothing directly for the purpose, but the intermediate steps are done. And a little bit of work from http://code.dlang.org/packages/atmosphere http://code.dlang.org/packages/atmosphere [x] Probability Distributions: http://dlangscience.github.io/dstats/api/dstats/random.html http://dlangscience.github.io/dstats/api/dstats/random.html and http://dlang.org/phobos/std_mathspecial.html http://dlang.org/phobos/std_mathspecial.html [-] Machine Learning: Not that I'm aware of, but I vaguely recall some work being done on this [-] Financial calculations: I couldn't find anything, but I'd be surprised if this wasn't implemented already. [x] Masked Arrays: Accomplished via language features in combination with ndslice [x] Structured Arrays: Absolutely. Standard library support as well as a number of 3rd-party libs can do this. [-] SVD [?] QR: Afraid I'm not sure what you're referring to. [+] Cholesky Decomposition: Trivial to implement [x] Eigenvalues/Eigenvectors: Yes. SciD provides Eigens as well as two other third party packages. https://dlangscience.github.io/scid/api/scid/linalg/eigenvalues.html https://dlangscience.github.io/scid/api/scid/linalg/eigenval... [x] Least Squares (linear): Trivial to implement, but also implemented by a few of the 2-d & 3-d graphics libraries. [x] Least Squares (nonlinear): Nothing, but we both know that this is easily done by applying a function to your independent variable to linearize it in many cases. [-] Levenberg Marquardt [x] Matrix Inverse: Provided by SciD [?] pseudoinverses: I'm afraid my math background doesn't cover that, and I couldn't find anything with a similar naming in the package repository [x] Integration: SciD provides integration [-] Range Kutta [-] Interpolation: Sort of. A few of the graphics libraries have this, but it's asking a bit much to include a graphics library to do interpolation. [-] Bsplines [-] fft convoles [?] Multidimensional images [?] KDTrees: Probably in one of the 3d graphics libs [+] Symbolic equation solvers: Some of this was done for Tango back in the day, and it has been ported, but the project looks fairly dead. https://github.com/opticron/libdmathexpr/blob/master/source/libdmathexpr/mathexpr.d https://github.com/opticron/libdmathexpr/blob/master/source/... [x] Merge/join of data sets: Can be done efficiently and easily with core language features Additionally, anything written for C can be trivially wrapped and used in D. An example is http://scimath.com/ http://scimath.com/, which completes much of what you mentioned. Are they in standard libraries? SciD needs more time Fully Documented? D tends to encourage good API documentation, and the algorithms are largely ports of "69 year old, fully debugged code". I think you're point about "reliably email to anyone across the world" is an overstatement. Python is popular, not ubiquitous. D is obviously much less popular. Can anyone run the code? Yes. For them to modify it, they need to learn the language. You might not care about how long computations take, but I know back at my old university there were dozens of researchers complaining about the resources that had to be spent on supercomputer time, and the annoying amount of time that many calculations involving things like molecular configurations can take. Speeding this up saves money and makes the research less painful. "No one is ever going to use D for serious number crunching without the infrastructure in place" - Yeah, that's totally true. The infrastructure is a WIP. There's nothing wrong with that. Your post comes across to me as being rather cynical - but why not be supportive of the good work that's being done?