pca-decomposition
OfficialDimensionality reduction with PCA and varimax.
AuthorGeneralReasoning
Version1.0.0
Installs0
System Documentation
What problem does it solve?
PCA reduces many correlated variables into fewer uncorrelated components, and varimax rotation clarifies factor structure for easier interpretation.
Core Features & Use Cases
- Performs standardized PCA with optional varimax rotation to improve component interpretability.
- Outputs factor loadings and component scores for downstream analysis, enabling attribution, clustering, and visualization.
- Suitable for exploratory data analysis and dimensionality reduction in engineering, science, and finance contexts.
Quick Start
Standardize your data, fit a Varimax-rotated PCA with the desired number of factors, and extract component loadings and scores.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
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Please help me install this Skill: Name: pca-decomposition Download link: https://github.com/GeneralReasoning/env-skillsbench/archive/main.zip#pca-decomposition Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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