5 liens privรฉs
Actuellement le langage R est incontournable pour qui veut manipuler des donnรฉes en bioinformatique, en particulier pour l'analyse statistique. Mais un successeur est en passe de s'imposer : Julia, combinant puissance du langage avec les fonctionnalitรฉs de R, et comblant les nombreux dรฉfauts de ce dernier - mais plus encore ! Voici une prรฉsentation de ce tout nouveau langage.
We want a language thatโs open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language thatโs homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled.
(Did we mention it should be as fast as C?)
Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. The library, largely written in Julia itself, also integrates mature, best-of-breed C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing. In addition, the Julia developer community is contributing a number of external packages through Juliaโs built-in package manager at a rapid pace. IJulia, a collaboration between the IPython and Julia communities, provides a powerful browser-based graphical notebook interface to Julia.