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pip (also known by Python 3's alias pip3) is a package-management system written in Python and is used to install and manage software packages. [4] The Python Software Foundation recommends using pip for installing Python applications and its dependencies during deployment. [5] Pip connects to an online repository of public packages, called the ...
The Windows Package Manager (also known as winget) is a free and open-source package manager designed by Microsoft for Windows 10 and Windows 11. It consists of a command-line utility and a set of services for installing applications. [5] [6] Independent software vendors can use it as a distribution channel for their software packages.
Mingw-w64 includes a port of the GNU Compiler Collection (GCC), GNU Binutils for Windows (assembler, linker, archive manager), a set of freely distributable Windows specific header files and static import libraries for the Windows API, a Windows-native version of the GNU Project's GNU Debugger, and miscellaneous utilities.
Windows: Statistical software package for exploratory data analysis, dynamic data visualization, statistical modeling, design of experiments, time series forecasting ...
Anaconda, Inc. compiles and builds the packages available in the Anaconda repository itself, and provides binaries for Windows 32/64 bit, Linux 64 bit and MacOS 64-bit (Intel, Apple Silicon). Anything available on PyPI may be installed into a Conda environment using pip, and Conda will keep track of what it has installed and what pip has installed.
In addition, the Python extension allows SPSS to run any of the statistics in the free software package R. From version 14 onwards, SPSS can be driven externally by a Python or a VB.NET program using supplied "plug-ins". (From version 20 onwards, these two scripting facilities, as well as many scripts, are included on the installation media and ...
SciPy – Python library for scientific computing that contains the stats sub-package which is partly based on the venerable |STAT (a.k.a. PipeStat, formerly UNIX|STAT) software scikit-learn – extends SciPy with a host of machine learning models (classification, clustering, regression, etc.)
JMP 8 was released in 2009 with new drag-and-drop features and a 64-bit version to take advantage of advances in the Mac operating system. [19] It also added a new user interface for building graphs, tools for choice experiments and support for Life Distributions. [20]