Machine Learning Utility Functions
The `util.is*` functions introduced in Node v0.12.
Utilities to help with endpoint resolution
[](https://www.npmjs.com/package/@aws-sdk/util-locate-window) [](https://www.npmjs.com/packag
unist utility to visit nodes
Node.js's util module for all engines
A parser to Amazon Resource Names
unist utility to serialize a node, position, or point as a human readable location
unist utility to check if a node passes a test
unist utility to recursively walk over nodes, with ancestral information
mdast utility to serialize markdown
hast utility to check if a node is inter-element whitespace
unist utility to get the position of a node
mdast utility to get the plain text content of a node
[](https://www.npmjs.com/package/@aws-sdk/util-user-agent-node) [](https://www.npmjs.com/
Utility functions
mdast utility to transform to hast
Various helper utilities
mdast utility to check if a node is phrasing content
mdast extension to parse and serialize GFM strikethrough
mdast extension to parse and serialize MDX or MDX.js JSX
hast utility to create an element from a simple CSS selector
mdast utility to parse markdown
mdast extension to parse and serialize GFM task list items
Basic YAML reader operations. Transform form YAML to data
Helper utilities for working with the Daml Ledgers
Common utilities for machine learning
Various small utilities used at MLZ
Various utilities including `FixedU128` and `LinkedList`.
Utils for HTML
Core tensor and network utilities for the tml machine learning library
some utility traits and functions for unhtml
Self-ML is, as the name implies, a self-ml parser. It also has some utilities to aid in building self-ml files.
Ruby Scientist and Graphics is a practical data science toolkit for Ruby. It includes a lightweight built-in DataFrame for loading, cleaning, and transforming data; quick descriptive statistics and correlations; charting via Gruff (bar and line); and simple ML utilities (linear regression and k-means)—all behind a small, unified, pandas-inspired API. Key features: - Load data from CSV and JSON. - Clean and transform (remove/add columns, handle missing values, limit rows). - Describe datasets and compute correlations quickly. - Create bar and line charts with customization options. - Train/predict with linear regression; cluster with k-means. - Save/load project state (data + trained model) and run simple pipelines. - Optional backend adapters (e.g., Rover) while keeping the same API. Ideal for analysts and developers who want to explore data in Ruby without relying on Python or R. Note: plotting via Gruff uses rmagick, which requires ImageMagick installed on the system.
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