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RStudio IDE (or RStudio) is an integrated development environment for R, a programming language for statistical computing and graphics. It is available in two formats: RStudio Desktop is a regular desktop application while RStudio Server runs on a remote server and allows accessing RStudio using a web browser .
Shiny is a web framework for developing web applications (apps), originally in R and since 2022 in python. It is free and open source. [2] It was announced by Joe Cheng, CTO of Posit, formerly RStudio, in 2012. [3] One of the uses of Shiny has been in fast prototyping. [4] In 2022, a separate implementation Shiny for Python was announced. [5]
RExcel is an add-on for Microsoft Excel that allows access to the statistics package R from within Excel. It uses the statconnDCOM server and, for certain configurations, the room package. RExcel runs on Microsoft Windows (XP, Vista, or 7), with Excel 2003, 2007, 2010, and 2013. [1]
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R is a programming language for statistical computing and data visualization.It has been adopted in the fields of data mining, bioinformatics and data analysis. [9]The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data.
The findgen function in the above example returns a one-dimensional array of floating point numbers, with values equal to a series of integers starting at 0.. Note that the operation in the second line applies in a vectorized manner to the whole 100-element array created in the first line, analogous to the way general-purpose array programming languages (such as APL, J or K) would do it.
Python Tools for Visual Studio (PTVS) is a free and open-source plug-in for versions of Visual Studio up to VS 2015 providing support for programming in Python. Since VS 2017, it is integrated in VS and called Python Support in Visual Studio. It supports IntelliSense, debugging, profiling, MPI cluster debugging, mixed C++/Python debugging, and ...
Pypeline and ONNX Helper allow users to connect AnyLogic simulations with Python-based models or those stored in the ONNX format. With Pypeline, users can run Python scripts and functions directly within the simulation environment. [71] On the other hand, ONNX is a standard that makes it easier to use and share models across various AI ...