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C, Fortran, R [7] R language, Python (by RPy), Perl (by Statistics::R module) R++: Zebrys 1.6.15 (8 December 2023 ()) [8] No Proprietary: CLI, GUI: C++, Qt R language: RKWard: RKWard community 0.7.3 (21 April 2022 ()) [9] Yes GNU GPL: CLI, GUI: C++, ECMAScript R language, Python (by RPy), Perl (by Statistics::R module) Revolution Analytics
dplyr is an R package whose set of functions are designed to enable dataframe (a spreadsheet-like data structure) manipulation in an intuitive, user-friendly way. It is one of the core packages of the popular tidyverse set of packages in the R programming language. [1]
In Microsoft Excel, these functions are defined using Visual Basic for Applications in the supplied Visual Basic editor, and such functions are automatically accessible on the worksheet. Also, programs can be written that pull information from the worksheet, perform some calculations, and report the results back to the worksheet.
R is both a language and software used for statistical computing and graphing. R was originally developed by Bell Laboratories (Currently known as Lucent Technologies) by John Chambers. Since R is largely written in C language, users can use C or C++ commands to manipulate R-objects directly. Also, R runs on most UNIX platforms.
JMP can be used in conjunction with the R and Python open source programming languages to access features not available in JMP itself. [42] JMP software is partly focused on exploratory data analysis and visualization. It is designed for users to investigate data to learn something unexpected, as opposed to confirming a hypothesis.
Support for multi-dimensional arrays may also be provided by external libraries, which may even support arbitrary orderings, where each dimension has a stride value, and row-major or column-major are just two possible resulting interpretations. Row-major order is the default in NumPy [19] (for Python).
Excel graph of the difference between two evaluations of the smallest root of a quadratic: direct evaluation using the quadratic formula (accurate at smaller b) and an approximation for widely spaced roots (accurate for larger b). The difference reaches a minimum at the large dots, and round-off causes squiggles in the curves beyond this minimum.
A simple example is fitting a line in two dimensions to a set of observations. Assuming that this set contains both inliers, i.e., points which approximately can be fitted to a line, and outliers, points which cannot be fitted to this line, a simple least squares method for line fitting will generally produce a line with a bad fit to the data including inliers and outliers.