Introduction | Input Data | Weighted vs Unweighted Metrics | Formulas | Abundance-Based (Weighted) | Presence / Absence (Unweighted) | Phylogenetic | Partial Calculation | References

Introduction | Alpha Diversity | Beta Diversity | Example | Rarefaction | Classic Metrics | Phylogenetic Metrics | Distance Matrices | Performance with Large Datasets

The Hierarchical Model | Creating Groups | Implicit Creation (Recommended) | Explicit Creation | Using Lists as Groups | Inspecting Structure | Moving and Renaming | Deleting Objects

The compress Argument and h5_compression() | Valid Compression Strings Reference | Choosing a Codec: Modern vs. Legacy | 1. Blosc2 (Highly Recommended) | 2. Standalone Modern Codecs | 3. Gzip (The Universal Standard) | 4. Legacy Codecs (Obsolete or Niche) | Lossy Compression: ZFP and Scale-Offset | ZFP (Floating-Point & Integer) | Scale-Offset (Integer Packing & Float Rounding) | Filter Interactions & Invalid Combinations | Tuning Chunk Size | Evaluating Results with h5_inspect()

HDF5 for R Users | Basic Usage | Writing Numeric Data | Reading Data | Data Type Mapping | Complex Data Structures | Lists as Groups | Data Frames as Compound Datasets | Attributes | File Inspection

Why Partial Reading Matters | The HDF5 Storage Model: Chunking and Compression | Designing for Partial Reading | Memory Efficiency of start and count | The "Smart" start Parameter | Single-Value Examples | Dimension Simplification (Exact vs. Range Indexing) | Drilling Down: Multi-Value start and N-Dimensional Arrays

Basic Usage | Scalars vs. 1D Arrays | Numeric and Logical Data | Automatic Type Selection | Handling Missing Values (NA) | Forcing Specific Types | Character Vectors (Strings) | Variable-Length | Fixed-Length | Compression | 64-bit Integers

Supported Data Types | Dimensions: Scalars, Vectors, and Arrays | Numeric Data | 64-bit Integers (integer64) | Double (Numeric) Data | Logical Data | Character Data | Variable-Length: | Fixed-Length: | Dates and Times (POSIXt) | Complex Data | Raw Data | Factors | Lists | Data Frames | NULL | Compression | The Shuffle Filter

Writing Matrices | Writing N-Dimensional Arrays | Dimension Names (dimnames) | Dimension Ordering (Row-Major vs. Column-Major) | How h5lite handles it | Interoperability with Python | Compression and Chunking | Partial I/O

Optimizing ecodive for Large Datasets | The Optimal Input: Compressed Sparse Matrices | Why Sparse Matrices? | rbiom Users | Handling Data Transformations | The Exception: Centered Log-Ratio (CLR) | Summary

Input Matrix | Richness Metrics | Diversity Metrics | Dominance Metrics | Phylogenetic Metrics | Formulas | Abundance-based Coverage Estimator (ACE) | Faith's Phylogenetic Diversity (Faith's PD)

Writing Attributes | 1. Explicit Writing | 2. Implicit Writing (R Attributes) | Reading Attributes | 1. Accessing Specific Attributes | 2. Reading with the Dataset | Managing Attributes | Listing Attributes | Deleting Attributes | Important Limitations | 1. No Dimension Scales (Loss of Names) | 2. No Attributes on Attributes (Nesting) | Controlling Attribute Types | Customizing Storage Type | Customizing Read Type | Special Note: Dimensions

Basic Usage | Customizing Column Types | Row Names

Basic Usage | Navigation and Working Directories | Reference Semantics | Closing the Handle | Method Reference

The Safety Rule: Always Lock | Recommended Strategy: File Locking with flock

Reacting to Results | Output vs Result | Reformatting Output | Transformation | job Details | Non-blocking | Signaling Errors | Promise Handling

Setup | Introduction | A Callback Function | Triggers | Special Triggers | jobqueue States | worker States | job States | Attaching | Removing | Default job Hooks | Use Case: Priority Setting | Use Case: Rate Limiting

Introduction | Expressions | Variables | Functions | Example

Create a jobqueue | Create a job | Access the Result | Add a Callback Hook | Interrupt Evaluation | Passing Data

Introduction | Setup | Immediate Stop | Interrupting with $stop() | Assigning to $output | Replacing and Combining | Stop ID | Copy ID | In jobqueue() | Timeouts