The realm of programming is closely intertwined with C, which is a language that almost every programmer will encounter in his career. It's a pioneering language in this regard and its principles have been implemented in lots of subsequent programming languages. However, there's another language that, while not as universally known as C, has had a significant impact in its field: the code of S programming language.
Bell laboratory came up with S in the 1970s and thus anything that is conceived can be transcribed into a programmed language and videlicet productivity and error-free software. The language was initially built by Chambers John assistants of Rick Becker, Trevor Hastie, William Cleveland, and Allan Wilks. The endeavor bridged the gap that existed with the use of the Fortran routines, which were used to do statistical modeling games with only non-interactive and no flexible model.
S being software for processing statistical functions and figure threats was the main purpose. Per se, the machine's philosophy was to make it possible for statistical methods to become the standard computing routines. Contrary to C, a general-purpose language, S was the language developed for the narrow area of statistical work. Thus, while being the tool for the small audience, S had the power to process the data.
As time passes, the sigh has transformed. The language has been cycled several times, enriched with more capabilities and features, each time. One of the S biggest adoptions is S-PLUS, a commercialized product which with a visually appealing user interface extends and enhances S capabilities.
Meanwhile, C is a procedural language that is employed to create system programming software and embedded systems. On the other hand, S is a multi-paradigm programming language that includes OO features. As compared to C's, the syntax as well as semantics of S are quite different, and they aim at specifying a convenient as well as powerful platform for working with the data and analysis. Rather than low-level memory management and system calls, it is done by providing an environment abstraction that is suitable for machine learning.
The realm of programming is closely intertwined with C, which is a language that almost every programmer will encounter in his career. It's a pioneering language in this regard and its principles have been implemented in lots of subsequent programming languages. However, there's another language that, while not as universally known as C, has had a significant impact in its field: the code of S programming language.
Bell laboratory came up with S in the 1970s and thus anything that is conceived can be transcribed into a programmed language and videlicet productivity and error-free software. The language was initially built by Chambers John assistants of Rick Becker, Trevor Hastie, William Cleveland, and Allan Wilks. The endeavor bridged the gap that existed with the use of the Fortran routines, which were used to do statistical modeling in games with only non-interactive and no flexible model.
S being software for processing statistical functions and figure threats was the main purpose. Per se, the machine's philosophy was to make it possible for statistical methods to become the standard computing routines. Contrary to C, a general-purpose language, S was the language developed for the narrow area of statistical work. Thus, while being the tool for the small audience, S had the power to process the data.
As time passes, the sigh has transformed. The language has been cycled several times, enriched with more capabilities and features, each time. One of the S biggest adoptions is S-PLUS, a commercialized product which with a visually appealing user interface extends and enhances S capabilities.
Meanwhile, C is a procedural language that is employed to create system programming software and embedded systems. On the other hand, S is a multi-paradigm programming language that includes OO features. As compared to C's, the syntax as well as semantics of S are quite different, and they aim at specifying a convenient as well as powerful platform for working with the data and analysis. Rather than low-level memory management and system calls, it is done by providing an environment abstraction that is suitable for machine learning.
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