ecodive: Parallel and Memory-Efficient R Package for Ecological Diversity Analysis
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2026
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| _version_ | 1866902315778179072 |
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| author | Smith, Daniel P Javornik Cregeen, Sara J Petrosino, Joseph F |
| author_facet | Smith, Daniel P Javornik Cregeen, Sara J Petrosino, Joseph F |
| contents | <p><strong>ecodive</strong> is a high-performance R package designed for calculating a wide array of ecological diversity metrics with a focus on speed, memory efficiency, and scalability. It provides a unified framework for both alpha (within-site) and beta (between-site) diversity, specifically optimized for the high-dimensional datasets common in modern microbiome and macro-ecological research.</p> <h3>Key Features</h3> <ul> <li> <p><strong>High Performance:</strong> Core algorithms are implemented in <strong>C</strong> and parallelized using <strong>pthreads</strong>, enabling execution speeds up to 9,000x faster than traditional R implementations.</p> </li> <li> <p><strong>Memory Efficiency:</strong> Uses a sparse matrix architecture (<code>dgCMatrix</code>) and cache-friendly, column-major data processing to reduce memory footprints by up to 200,000x.</p> </li> <li> <p><strong>Comprehensive Metric Suite:</strong> Implements over 50 symmetric metrics, including:</p> <ul> <li> <p><strong>Alpha Diversity:</strong> Shannon, Simpson, Chao1, ACE, and Faith's Phylogenetic Diversity.</p> </li> <li> <p><strong>Beta Diversity:</strong> Bray-Curtis, Jaccard, Euclidean, and the complete <strong>UniFrac family</strong> (unweighted, weighted, generalized, and variance-adjusted).</p> </li> </ul> </li> <li> <p><strong>Zero Dependencies:</strong> The package has no external R dependencies, ensuring stability, ease of installation, and a secure foundation for other software.</p> </li> <li> <p><strong>Seamless Integration:</strong> Native support for popular bioinformatics objects, including <code>phyloseq</code>, <code>rbiom</code>, and <code>TreeSummarizedExperiment</code>.</p> </li> </ul> <h3>Statement of Need</h3> <p>Modern ecological analysis often involves datasets with thousands of samples and tens of thousands of taxa. Beta diversity calculations typically scale quadratically ($O(n^2)$), creating computational bottlenecks on standard hardware. <strong>ecodive</strong> addresses this by leveraging direct POSIX thread implementations and "zero-copy" analysis for massive datasets, allowing high-throughput analysis on laptops that previously required high-performance computing clusters.</p> <h3>Installation</h3> <p><strong>From CRAN:</strong></p> <div> <div> <div> <pre><code>install.packages('ecodive')</code></pre> </div> </div> </div> <p><strong>From GitHub (Development Version):</strong></p> <div> <div> <div> <pre><code>pak::pak('cmmr/ecodive')</code></pre> </div> </div> </div> <h3>Links</h3> <ul> <li> <p><strong>Documentation:</strong> <a href="https://cmmr.github.io/ecodive/" target="_blank" rel="noopener">https://cmmr.github.io/ecodive/</a></p> </li> <li> <p><strong>Source Code:</strong> <a href="https://github.com/cmmr/ecodive" target="_blank" rel="noopener">https://github.com/cmmr/ecodive</a></p> </li> <li> <p><strong>Issue Tracker:</strong> <a href="https://github.com/cmmr/ecodive/issues" target="_blank" rel="noopener">https://github.com/cmmr/ecodive/issues</a></p> </li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19190016 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | ecodive: Parallel and Memory-Efficient R Package for Ecological Diversity Analysis Smith, Daniel P Javornik Cregeen, Sara J Petrosino, Joseph F R ecology bioinformatics microbiome diversity high-performance computing <p><strong>ecodive</strong> is a high-performance R package designed for calculating a wide array of ecological diversity metrics with a focus on speed, memory efficiency, and scalability. It provides a unified framework for both alpha (within-site) and beta (between-site) diversity, specifically optimized for the high-dimensional datasets common in modern microbiome and macro-ecological research.</p> <h3>Key Features</h3> <ul> <li> <p><strong>High Performance:</strong> Core algorithms are implemented in <strong>C</strong> and parallelized using <strong>pthreads</strong>, enabling execution speeds up to 9,000x faster than traditional R implementations.</p> </li> <li> <p><strong>Memory Efficiency:</strong> Uses a sparse matrix architecture (<code>dgCMatrix</code>) and cache-friendly, column-major data processing to reduce memory footprints by up to 200,000x.</p> </li> <li> <p><strong>Comprehensive Metric Suite:</strong> Implements over 50 symmetric metrics, including:</p> <ul> <li> <p><strong>Alpha Diversity:</strong> Shannon, Simpson, Chao1, ACE, and Faith's Phylogenetic Diversity.</p> </li> <li> <p><strong>Beta Diversity:</strong> Bray-Curtis, Jaccard, Euclidean, and the complete <strong>UniFrac family</strong> (unweighted, weighted, generalized, and variance-adjusted).</p> </li> </ul> </li> <li> <p><strong>Zero Dependencies:</strong> The package has no external R dependencies, ensuring stability, ease of installation, and a secure foundation for other software.</p> </li> <li> <p><strong>Seamless Integration:</strong> Native support for popular bioinformatics objects, including <code>phyloseq</code>, <code>rbiom</code>, and <code>TreeSummarizedExperiment</code>.</p> </li> </ul> <h3>Statement of Need</h3> <p>Modern ecological analysis often involves datasets with thousands of samples and tens of thousands of taxa. Beta diversity calculations typically scale quadratically ($O(n^2)$), creating computational bottlenecks on standard hardware. <strong>ecodive</strong> addresses this by leveraging direct POSIX thread implementations and "zero-copy" analysis for massive datasets, allowing high-throughput analysis on laptops that previously required high-performance computing clusters.</p> <h3>Installation</h3> <p><strong>From CRAN:</strong></p> <div> <div> <div> <pre><code>install.packages('ecodive')</code></pre> </div> </div> </div> <p><strong>From GitHub (Development Version):</strong></p> <div> <div> <div> <pre><code>pak::pak('cmmr/ecodive')</code></pre> </div> </div> </div> <h3>Links</h3> <ul> <li> <p><strong>Documentation:</strong> <a href="https://cmmr.github.io/ecodive/" target="_blank" rel="noopener">https://cmmr.github.io/ecodive/</a></p> </li> <li> <p><strong>Source Code:</strong> <a href="https://github.com/cmmr/ecodive" target="_blank" rel="noopener">https://github.com/cmmr/ecodive</a></p> </li> <li> <p><strong>Issue Tracker:</strong> <a href="https://github.com/cmmr/ecodive/issues" target="_blank" rel="noopener">https://github.com/cmmr/ecodive/issues</a></p> </li> </ul> |
| title | ecodive: Parallel and Memory-Efficient R Package for Ecological Diversity Analysis |
| topic | R ecology bioinformatics microbiome diversity high-performance computing |
| url | https://doi.org/10.5281/zenodo.19190016 |