Thanks for posting this project but it will be helpful if the link to directed to the original JOSS paper because it seems that nobody is referring it in the comments [1].

In Hennessey's and Patterson's Turing Award Lecture, "A New Golden Age for Computer Architecture", they lamented about the difficulty of mapping the the general purpose programming language (Python, Java, C, Fortran) onto the Domain-specific Architecture (DSA) and suggested that the best way is to utilize Domain-specific Language (DSL). They've pointed to the significant rise of DSL and specifically mentioned Matlab, TensorFlow, P4 and Halide as popular examples [2]. The fact that Matlab is by far the most popular DSL compared to the other three is somewhat very interesting fact to note.

From the Castor's JOSS paper when referring to Matlab and Julia, "Despite the many advantages that these languages have and their high popularity, many codes are still developed natively in Fortran, C, and C++, for practical or historical reasons. Even if there are tools to automatically generate C/C++ code from a high-level language (as Matlab Coder), this work is often done manually by specialists." This precisely my sentiment of the current data science languages and my thought is that why not a language that has best of both world, native compiled language and a seamlessly integrated data science DSL that's much more than a library?

Enter D language, the only language that for several years now has BLAS library faster than the one implemented in Fortran [3], support C language in its latest compiler [4] and has been touted as the better C++ language by its authors [5]. What it really needs now is a seamlessly integrated DSL with Matlab syntax familiarity to win over the type A data scientist [6]. The DSL is better be implemented as CTFE that would enable higher level of integration with the D language [7].

As for another popular DSL mentioned in the Turing Lecture, TensorFlow, D has already has one minimalist TensorFlow (TF) alternative library and it's already faster than TF [8]. For the other two DSL, namely P4 and Halide, it will be probably happen sooner rather than later that the D alternatives will emerge and personally interested to explore P4 written in D language.

[1] Castor: A C++ library to code “à la Matlab”:

https://www.theoj.org/joss-papers/joss.03965/10.21105.joss.0...

[2] A New Golden Age for Computer Architecture:

https://cacm.acm.org/magazines/2019/2/234352-a-new-golden-ag...

[3] Numeric age for D: Mir GLAS is faster than OpenBLAS and Eigen:

http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/...

[4] Adding ANSI C11 C compiler to D so it can import and compile C files directly:

https://news.ycombinator.com/item?id=27102584

[5] Origins of the D Programming Language:

https://dl.acm.org/doi/pdf/10.1145/3386323

[6] There are two types of data scientists — and two types of problems to solve:

https://medium.com/@jamesdensmore/there-are-two-types-of-dat...

[7] Compile-Time Sort in D:

https://news.ycombinator.com/item?id=30317408

[8] Vectorflow: Minimalist neural network library faster than TensorFlow in D:

https://news.ycombinator.com/item?id=31046675