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repoze.sendmail
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None |
Repoze Sendmail
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2025-03-25 |
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wicken
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None |
Maps metadata concepts to concrete specifations and file formats
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2025-03-25 |
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petulant-bear
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None |
Presents etree interface to netcdf4-python objects using NCML data model
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2025-03-25 |
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qa-dkrz
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None |
Quality Assurance checker of meta-data in climate data sets (netCDF files) for CF conformance and CMIP5 and CORDEX projects conventions.
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2025-03-25 |
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szip
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None |
Szip compression software, providing lossless compression of scientific data: http://www.hdfgroup.org/doc_resource/SZIP/
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2025-03-25 |
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libaec
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None |
Adaptive Entropy Coding library
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2025-03-25 |
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java-jdk
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None |
OpenJDK
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2025-03-25 |
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bird-feeder
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None |
Bird Feeder publishes Thredds metadata catalogs to a Solr index service with birdhouse schema.
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2025-03-25 |
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solr
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None |
Solr is the popular, blazing-fast, open source enterprise search platform built on Apache Lucene
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2025-03-25 |
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pysolr
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None |
Lightweight python wrapper for Apache Solr.
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2025-03-25 |
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myproxyclient
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None |
This a pure Python implementation of a client to the MyProxy Credential Management Server.
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2025-03-25 |
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threddsclient
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None |
Thredds catalog client.
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2025-03-25 |
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celery
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None |
Distributed Task Queue
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2025-03-25 |
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amqp
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None |
Low-level AMQP client for Python (fork of amqplib)
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2025-03-25 |
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kombu
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None |
Messaging library for Python
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2025-03-25 |
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billiard
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None |
Python multiprocessing fork with improvements and bugfixes
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2025-03-25 |
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thredds
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None |
The THREDDS Data Server (TDS) is a web server that provides metadata and data access for scientific datasets, using a variety of remote data access protocols.
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2025-03-25 |
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apache-tomcat
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None |
Apache Tomcat is an open source software implementation of the Java Servlet, JavaServer Pages, Java Expression Language and Java WebSocket technologies.
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2025-03-25 |
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pyproj
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None |
No Summary
|
2025-03-25 |
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pycsw
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None |
pycsw is an OGC CSW server implementation written in Python
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2025-03-25 |
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r-evd
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None |
Functions for extreme value distributions. Extends simulation, distribution, quantile and density functions to univariate and multivariate parametric extreme value distributions, and provides fitting functions which calculate maximum likelihood estimates for univariate and bivariate maxima models, and for univariate and bivariate threshold models.
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2025-03-25 |
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r-ncdf4
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None |
This package provides a high-level R interface to data files written using Unidata's netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets. Using this package, netCDF files (either version 4 or "classic" version 3) can be opened and data sets read in easily. It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files. This package replaces the former ncdf package, which only worked with netcdf version 3 files. For various reasons the names of the functions have had to be changed from the names in the ncdf package. The old ncdf package is still available at the URL given below, if you need to have backward compatibility. It should be possible to have both the ncdf and ncdf4 packages installed simultaneously without a problem. However, the ncdf package does not provide an interface for netcdf version 4 files.
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2025-03-25 |
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r-ncdf
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None |
This package provides a high-level R interface to Unidata's netCDF data files, which are portable across platforms and include metadata information in addition to the data sets. Using this package netCDF files can be opened and data sets read in easily. It is also easy to create new netCDF dimensions, variables, and files, or manipulate existing netCDF files. This interface provides considerably more functionality than the old "netCDF" package for R, and is not compatible with the old "netCDF" package for R.
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2025-03-25 |
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r-presenceabsence
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None |
This package provides a set of functions useful when evaluating the results of presence-absence models. Package includes functions for calculating threshold dependent measures such as confusion matrices, pcc, sensitivity, specificity, and Kappa, and produces plots of each measure as the threshold is varied. It will calculate optimal threshold choice according to a choice of optimization criteria. It also includes functions to plot the threshold independent ROC curves along with the associated AUC (area under the curve).
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2025-03-25 |
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r-raster
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None |
No Summary
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2025-03-25 |
