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Calling All Chocolatiers! Whipping Up Windows Automation with Chocolatey Central Management

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We are delighted to announce the release of Chocolatey Central Management v0.12.0, featuring seamless Deployment Plan creation, time-saving duplications, insightful Group Details, an upgraded Dashboard, bug fixes, user interface polishing, and refined documentation. As an added bonus we'll have members of our Solutions Engineering team on-hand to dive into some interesting ways you can leverage the new features available!

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The R Project for Statistical Computing 4.4.0

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All Checks are Passing

3 Passing Tests


Validation Testing Passed


Verification Testing Passed

Details

Scan Testing Successful:

No detections found in any package files

Details
Learn More

Deployment Method: Individual Install, Upgrade, & Uninstall

To install The R Project for Statistical Computing, run the following command from the command line or from PowerShell:

>

To upgrade The R Project for Statistical Computing, run the following command from the command line or from PowerShell:

>

To uninstall The R Project for Statistical Computing, run the following command from the command line or from PowerShell:

>

Deployment Method:

NOTE

This applies to both open source and commercial editions of Chocolatey.

1. Enter Your Internal Repository Url

(this should look similar to https://community.chocolatey.org/api/v2/)


2. Setup Your Environment

1. Ensure you are set for organizational deployment

Please see the organizational deployment guide

2. Get the package into your environment

  • Open Source or Commercial:
    • Proxy Repository - Create a proxy nuget repository on Nexus, Artifactory Pro, or a proxy Chocolatey repository on ProGet. Point your upstream to https://community.chocolatey.org/api/v2/. Packages cache on first access automatically. Make sure your choco clients are using your proxy repository as a source and NOT the default community repository. See source command for more information.
    • You can also just download the package and push it to a repository Download

3. Copy Your Script

choco upgrade r -y --source="'INTERNAL REPO URL'" [other options]

See options you can pass to upgrade.

See best practices for scripting.

Add this to a PowerShell script or use a Batch script with tools and in places where you are calling directly to Chocolatey. If you are integrating, keep in mind enhanced exit codes.

If you do use a PowerShell script, use the following to ensure bad exit codes are shown as failures:


choco upgrade r -y --source="'INTERNAL REPO URL'" 
$exitCode = $LASTEXITCODE

Write-Verbose "Exit code was $exitCode"
$validExitCodes = @(0, 1605, 1614, 1641, 3010)
if ($validExitCodes -contains $exitCode) {
  Exit 0
}

Exit $exitCode

- name: Install r
  win_chocolatey:
    name: r
    version: '4.4.0'
    source: INTERNAL REPO URL
    state: present

See docs at https://docs.ansible.com/ansible/latest/modules/win_chocolatey_module.html.


chocolatey_package 'r' do
  action    :install
  source   'INTERNAL REPO URL'
  version  '4.4.0'
end

See docs at https://docs.chef.io/resource_chocolatey_package.html.


cChocoPackageInstaller r
{
    Name     = "r"
    Version  = "4.4.0"
    Source   = "INTERNAL REPO URL"
}

Requires cChoco DSC Resource. See docs at https://github.com/chocolatey/cChoco.


package { 'r':
  ensure   => '4.4.0',
  provider => 'chocolatey',
  source   => 'INTERNAL REPO URL',
}

Requires Puppet Chocolatey Provider module. See docs at https://forge.puppet.com/puppetlabs/chocolatey.


4. If applicable - Chocolatey configuration/installation

See infrastructure management matrix for Chocolatey configuration elements and examples.

Package Approved

This package was approved as a trusted package on 26 May 2024.

Description

Introduction to R

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

The R environment

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes

  • an effective data handling and storage facility,
  • a suite of operators for calculations on arrays, in particular matrices,
  • a large, coherent, integrated collection of intermediate tools for data analysis,
  • graphical facilities for data analysis and display either on-screen or on hardcopy, and
  • a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
  • The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.

R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.

Many users think of R as a statistics system. We prefer to think of it of an environment within which statistical techniques are implemented.
R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.

R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy.

Package Parameters

  • /UseInf: - Change the inno setup configuration file to use/save when installing

EXAMPLE
choco install r --params "'/UseInf:C:\r.inf'"


Log in or click on link to see number of positives.

In cases where actual malware is found, the packages are subject to removal. Software sometimes has false positives. Moderators do not necessarily validate the safety of the underlying software, only that a package retrieves software from the official distribution point and/or validate embedded software against official distribution point (where distribution rights allow redistribution).

Chocolatey Pro provides runtime protection from possible malware.

Add to Builder Version Downloads Last Updated Status
The R Project for Statistical Computing 4.3.3 3142 Friday, March 1, 2024 Approved
The R Project for Statistical Computing 4.3.2 5057 Wednesday, November 1, 2023 Approved
The R Project for Statistical Computing 4.3.1 6031 Friday, June 16, 2023 Approved
The R Project for Statistical Computing 4.3.0 3511 Saturday, April 22, 2023 Approved
The R Project for Statistical Computing 4.2.3 2655 Thursday, March 16, 2023 Approved
The R Project for Statistical Computing 4.2.2 6556 Tuesday, November 1, 2022 Approved
The R Project for Statistical Computing 4.2.1 6842 Thursday, June 23, 2022 Approved
The R Project for Statistical Computing 4.2.0 3894 Friday, April 22, 2022 Approved
The R Project for Statistical Computing 4.1.3 3226 Thursday, March 10, 2022 Approved
The R Project for Statistical Computing 4.1.2 6106 Tuesday, November 2, 2021 Approved
The R Project for Statistical Computing 4.1.1 4714 Tuesday, August 10, 2021 Approved
The R Project for Statistical Computing 4.1.0 4102 Tuesday, May 18, 2021 Approved
The R Project for Statistical Computing 4.0.5 1828 Wednesday, March 31, 2021 Approved
The R Project for Statistical Computing 4.0.4 2182 Monday, February 15, 2021 Approved
The R Project for Statistical Computing 4.0.3 11690 Saturday, October 10, 2020 Approved
The R Project for Statistical Computing 4.0.2 2646 Monday, June 22, 2020 Approved
The R Project for Statistical Computing 4.0.1 671 Saturday, June 6, 2020 Approved
The R Project for Statistical Computing 4.0.0 1876 Friday, April 24, 2020 Approved
The R Project for Statistical Computing 3.6.3 2464 Saturday, February 29, 2020 Approved
The R Project for Statistical Computing 3.6.2 3106 Thursday, December 12, 2019 Approved
The R Project for Statistical Computing 3.6.1 2732 Saturday, July 6, 2019 Approved
The R Project for Statistical Computing 3.6.0 1582 Friday, April 26, 2019 Approved
The R Project for Statistical Computing 3.5.3 630 Monday, March 11, 2019 Approved
The R Project for Statistical Computing 3.5.2 1636 Thursday, December 20, 2018 Approved
The R Project for Statistical Computing 3.5.1 4015 Monday, July 2, 2018 Approved
The R Project for Statistical Computing 3.5.0.20180424 515 Tuesday, April 24, 2018 Approved
The R Project for Statistical Computing 3.5.0 371 Monday, April 23, 2018 Approved
The R Project for Statistical Computing 3.4.4 487 Wednesday, April 4, 2018 Approved
Discussion for the The R Project for Statistical Computing Package

Ground Rules:

  • This discussion is only about The R Project for Statistical Computing and the The R Project for Statistical Computing package. If you have feedback for Chocolatey, please contact the Google Group.
  • This discussion will carry over multiple versions. If you have a comment about a particular version, please note that in your comments.
  • The maintainers of this Chocolatey Package will be notified about new comments that are posted to this Disqus thread, however, it is NOT a guarantee that you will get a response. If you do not hear back from the maintainers after posting a message below, please follow up by using the link on the left side of this page or follow this link to contact maintainers. If you still hear nothing back, please follow the package triage process.
  • Tell us what you love about the package or The R Project for Statistical Computing, or tell us what needs improvement.
  • Share your experiences with the package, or extra configuration or gotchas that you've found.
  • If you use a url, the comment will be flagged for moderation until you've been whitelisted. Disqus moderated comments are approved on a weekly schedule if not sooner. It could take between 1-5 days for your comment to show up.
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