genomic-prediction

Execution blueprints and code templates for fitting genomic prediction models, including GBLUP, GFBLUP, MultiBLUP, Bayesian methods, and Random Forest using peer-reviewed software. Strictly incorporates robust self-auditing, high-clarity code commenting, big data performance safe

# Genomic Prediction Blueprints This skill provides execution blueprints and templates for implementing established genomic prediction models using official software packages. Always use these validated implementations rather than writing custom approximation scripts. ## 1. Core Operating Principles - **No Workarounds**: Never attempt to build custom approximations, pseudo-code fallbacks, or shortcuts to bypass execution errors. - **No Silent Fallbacks**: If required input variables/metadata are missing, explicitly flag the missing data, abort, and report. Do not use pseudo-valid inputs. - *

When to use

Community skill by scicrow. Source: https://github.com/scicrow/genomic-prediction-skill

Examples

1. Core Operating Principles

- No Workarounds: Never attempt to build custom approximations, pseudo-code fallbacks, or shortcuts to bypass execution errors.

2. Environment and Data Prerequisites

- R Packages: `qgg`, `rrBLUP`, `BGLR`, `randomForest` or `ranger`.

3. High-Clarity Structured Code Commenting

- Structured Section Banners: ALWAYS organize code scripts into distinct, numbered sections (e.g., `# SECTION 1: ...`) separated by visual block heade