Titlebook: Domain-Specific Languages in R; Advanced Statistical Thomas Mailund Book 2018 Thomas Mailund 2018 DSLs.Domain Specific Languages.embedded.R

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Parsing and Manipulating Expressions,esult of evaluating the expression (computation time notwithstanding). A literate expression as this one is not something we can get a hold on to in a program—we get either an error or the value the expression evaluates to. If we want to get hold of the actual expression, we need to “quote” it.

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Lambda Expressions,, lambda expressions. It is perhaps not something we would use in real-world code, as it simply gives an alternative syntax to anonymous functions, which are already supported in R. However, it is an excellent example of code that is potentially useful and gives us a chance to experiment with syntax

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Continuous-Time Markov Chains,ce of which states the chain is in at different time points. As with the list comprehension example in the previous chapter, we are not going to use operators to create a new syntax for the language but will create a DSL by providing functions that can be strung together to construct “sentences.”

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Dynamic Programming,ed for speeding up recursive computations. When we need to compute a quantity from a recursion that splits into other recursions and these recursions overlap—so the computation involves evaluating the same recursive calls multiple times—we can speed up the computation by memorizing the results of th

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Book 2018how DSLs are programming languages specialized for a particular purpose, as opposed to general purpose programming languages. Along the way, you’ll learn to specify tasks you want to do in a precise way and achieve programming goals within a domain-specific context. .Domain-Specific Languages in R.

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https://doi.org/10.1007/978-1-4842-3588-1DSLs; Domain Specific Languages; embedded; R programming; R language; big data; AI; deep learning; statistic