Search Results for "inotify-tools"
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Showing 5 open source projects for "inotify-tools"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Haystack is a modern, engaging, and intuitive intranet platform that employees actually use.With customizable iOS and Android mobile apps, Slack and Microsoft Teams integrations, and an intuitive design employees love, Haystack brings an outstanding digital employee experience to your entire workforce, no matter where their work takes them.
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mzMatch is a Java collection of small commandline tools specific for metabolomics MS data analysis. The tools are built on top of the PeakML core library, providing mass spectrometry specific functionality and access to the PeakML file format.
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TXM
Unicode XML TEI text analysis platform
...lang=en. Read a full description at the TEI Tools wiki http://wiki.tei-c.org/index.php/TXM. -
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GNUexp is a GNUstep/Cocoa framework and a collection of tools helping you to plan, develop, and administer applications associated with visual psychophysics, experimental psychology and statistical data analysis.
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MGARCH is a software library for R-Project to simulate and estimate various MGARCH processes. This project also provides convenience tools for preparing data, summarizing and visualizing results, prediction and documentation.
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Get to know our award-winning HR software.BambooHR is all-in-one HR software made for small and medium businesses and the people who work in them—like you. Our software makes it easy to collect, maintain, and analyze your people data, improve the way you hire talent, onboard new employees, manage compensation, and develop your company culture. It’s designed to set you free to focus on what matters most—your people.
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Tools to build molecular-docking activity prediction models by PLS regression with iterative training and pose-selection. Descriptors include (i) docking score(s), (ii) pharmacophore features, (iii) multi-feature descriptors learned by decision trees.
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