So many new learning resources have been created this month! There’s information on tools, deploying models, Quarto presentations and blogs, neat tables and so much more. I love how these show so many different types of things you can do, because with code, so much is possible. That combination of learning what you can do, and the ‘how-tos’ of creation, give us the ability to use data to answer the questions we have. I’m so excited to see everyone continue to explore what’s possible.

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pins 1.0.3

The {pins} package publishes data, models, and other R objects, making it easy to share them across projects and with your colleagues. This is a minor release, but has some nice improvements. arrow is now suggested (instead of imported) for easier installation if you are not using arrow and there is better support for {pins} on AWS.

bundle 0.1.0

We’re thrilled to announce the first release of {bundle}. The {bundle} package provides a consistent interface to capture all information needed to serialize a model, situate that information within a portable object, and restore it for use in new settings.

cli 3.4.0

{cli} is a suite of tools to build attractive command line interfaces (CLIs), from semantic elements: headers, lists, alerts, paragraphs, etc. Improved vector collapsing behavior and more in this release.

lifecycle 1.0.2

The {lifecycle} package provides a set of tools and conventions to manage the life cycle of your exported functions. This release adds support for using arbitrary text in a deprecation message by wrapping or in .

rlang 1.0.5

{rlang} is a collection of frameworks and APIs for programming with R. This release fixes backtrace errors you might have seen.

bonsai 0.2.0

A new release of {bonsai}, a package extending tidymodels support for tree-based models, is now on CRAN. v0.2.0 greatly improves support for LightGBM, a gradient boosting framework.

brulee 0.2.0

We’re thrilled to announce the release of {brulee} 0.2.0. {brulee} contains several basic modeling functions that use the torch package infrastructure, such as: neural networks, linear regression, logistic regression, and multinomial regression.

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Source: R-bloggers.com

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