Add co-occurrence analysis tool

tools/cooccurrence/.shed.yml

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+name: nlp_cooccurrence_analysis
+owner: iuc
+description: Co-occurrence analysis from NLP-annotated JSON
+long_description: |
+  Computes term co-occurrence from NLP-annotated JSON output (spaCy, Stanza, or CoreNLP).
+  Supports sentence-level, sliding window, and dependency-based co-occurrence methods.
+  Produces pair lists and co-occurrence matrices for downstream analysis.
+homepage_url: https://github.com/galaxyproject/tools-iuc/tree/main/tools/cooccurrence
+remote_repository_url: https://github.com/galaxyproject/tools-iuc/tree/main/tools/cooccurrence
+type: unrestricted
+categories:
+  - Text Manipulation
+  - Natural Language Processing

tools/cooccurrence/README.md

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+# Galaxy Wrapper for Co-occurrence Analysis
+
+This Galaxy tool analyzes word co-occurrence relationships from NLP JSON output, enabling researchers to discover semantic and syntactic patterns in text corpora.
+
+## Features
+
+- **Multiple analysis methods**: Sentence-level, sliding window, and dependency-based co-occurrence detection
+- **Flexible input**: Works with JSON output from spaCy, Stanza, or CoreNLP tools
+- **Term representation options**: Lemma (recommended), surface form, or lowercased text
+- **POS filtering**: Optional part-of-speech filtering (NOUN, PROPN, VERB, ADJ, ADV, NUM)
+- **Stop word handling**: Built-in stop word removal plus support for custom stop word lists
+- **Named entity analysis**: Option to restrict analysis to named entity co-occurrences only
+- **Multiple outputs**: Co-occurrence pair list (TSV) and optional symmetric matrix
+- **Configurable parameters**: Adjustable window sizes, frequency thresholds, and filtering options
+
+## Requirements
+
+- **Input**: JSON output from Galaxy NLP tools (spaCy, Stanza, or CoreNLP)
+- **No dependencies**: Pure Python implementation with no external model downloads required
+
+## Analysis Methods
+
+| Method | Description | Use Case |
+|---|---|---|
+| **Sentence-level** | Terms co-occur if they appear in the same sentence | Document-level topic analysis (recommended starting point) |
+| **Sliding window** | Terms co-occur within a fixed token window | Local semantic relationships and collocations |
+| **Dependency-based** | Terms co-occur if connected by syntactic dependencies | Grammatical relationships (requires dependency parse) |
+
+## Input Format
+
+The tool expects JSON input with this structure from spaCy or Stanza:
+```json
+{
+  "sentences": [
+    {
+      "tokens": [
+        {
+          "text": "word",
+          "lemma": "lemmatized_form", 
+          "pos": "POS_TAG",
+          "is_alpha": true,
+          "is_stop": false
+        }
+      ],
+      "dependencies": [...] // for dependency-based analysis
+    }
+  ]
+}
+```
+
+## Output Formats
+
+### Co-occurrence Pairs (TSV)
+Tab-separated file with columns:
+- `term1`: First term in the pair
+- `term2`: Second term in the pair  
+- `count`: Number of co-occurrences
+
+Results are sorted by count in descending order.
+
+### Co-occurrence Matrix (TSV) 
+Optional full term-by-term matrix where:
+- Rows and columns represent vocabulary terms
+- Cell values represent co-occurrence counts
+- Can be large for extensive vocabularies
+
+## Key Parameters
+
+### Term Representation
+- **Lemma** (recommended): Reduces inflected forms to base form ("supports" → "support")
+- **Surface form**: Uses original text as-is
+- **Lowercased**: Simple case normalization
+
+### Filtering Options
+- **POS tag restriction**: Focus on specific parts of speech (nouns, verbs, etc.)
+- **Remove stop words**: Uses `is_stop` field from spaCy (for Stanza, use POS filtering)
+- **Alphabetic only**: Exclude punctuation and numbers
+- **Named entities only**: Restrict to named entity spans (PERSON, ORG, GPE, etc.)
+- **Custom stop words**: Upload text file with one word per line
+- **Minimum count**: Exclude pairs below frequency threshold
+
+### Method-Specific Options
+- **Window size**: For sliding window analysis (2-50 tokens, default: 5)
+
+## Example Use Cases
+
+- **Literary analysis**: Discover character relationships and thematic connections
+- **Historical research**: Track concept associations across time periods  
+- **Entity networks**: Build networks of people, organizations, and places
+- **Corpus linguistics**: Identify collocation patterns and semantic fields
+- **Digital humanities**: Analyze term associations in historical documents
+
+## Example Workflow
+
+1. Upload text → spaCy NLP (POS annotation, JSON output)
+2. spaCy JSON → Co-occurrence Analysis (sentence-level, NOUN + PROPN, remove stops)
+3. Pair list → downstream visualization or network analysis
+
+## Installation
+
+Install this tool from the Galaxy Toolshed: `cooccurrence_analysis`
+
+No additional setup required - the tool is ready to use after installation.
+
+## Citation
+
+This tool implements foundational co-occurrence analysis methods. Please cite:
+
+```
+Manning, Christopher D. and Hinrich Schütze. 
+Foundations of Statistical Natural Language Processing. 
+MIT Press, 1999.
+```
+
+## Version History
+
+- **1.0.0+galaxy0**: Initial release with sentence, window, and dependency-based analysis methods