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nbsmoke
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public |
Basic notebook checks. Do they run? Do they contain lint?
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2025-04-22 |
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nc-time-axis
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public |
Provides support for a cftime axis in matplotlib
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2025-04-22 |
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ligotimegps
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public |
A pure-python version of lal.LIGOTimeGPS
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2025-04-22 |
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platypus-opt
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public |
Multiobjective optimization in Python
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2025-04-22 |
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finitediff
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public |
Finite difference weights for any derivative order on arbitrarily spaced grids
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2025-04-22 |
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r-picante
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public |
Functions for phylocom integration, community analyses, null-models, traits and evolution. Implements numerous ecophylogenetic approaches including measures of community phylogenetic and trait diversity, phylogenetic signal, estimation of trait values for unobserved taxa, null models for community and phylogeny randomizations, and utility functions for data input/output and phylogeny plotting. A full description of package functionality and methods are provided by Kembel et al. (2010) <doi:10.1093/bioinformatics/btq166>.
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2025-04-22 |
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pyct
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public |
Python package common tasks for users (e.g. copy examples, fetch data, ...)
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2025-04-22 |
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torchfile
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public |
Deserialize torch-serialized objects from Python.
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2025-04-22 |
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log4cpp
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public |
Log4cpp is library of C++ classes for flexible logging to files
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2025-04-22 |
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pytest-django-haystack
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public |
django-haystack plugin for py.test
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2025-04-22 |
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django-haystack
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public |
Modular search for Django
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2025-04-22 |
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metsim
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public |
Meteorology Simulator for Python
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2025-04-22 |
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calamari_ocr
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public |
OCR Engine based on OCRopy and Kraken based on python3.
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2025-04-22 |
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md-toc
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public |
An utility that is able to generate a table of contents for a markdown file
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2025-04-22 |
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mdx_gh_links
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public |
An extension to Python-Markdown which adds support for shorthand links to GitHub users, repositories, issues and commits.
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2025-04-22 |
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pyodeint
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public |
Python wrapper around odeint (from the boost C++ library)
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2025-04-22 |
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fpyutils
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public |
A collection of useful non-standard Python functions which aim to be simple to use, highly readable but not efficient
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2025-04-22 |
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profimp
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public |
Python imports tracer
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2025-04-22 |
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edit_distance
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public |
Computing edit distance on arbitrary Python sequences.
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2025-04-22 |
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adios
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public |
The Adaptable IO System (ADIOS) for flexible and fast scientific data processing.
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2025-04-22 |
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r-base64url
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public |
In contrast to RFC3548, the 62nd character ("+") is replaced with "-", the 63rd character ("/") is replaced with "_". Furthermore, the encoder does not fill the string with trailing "=". The resulting encoded strings comply to the regular expression pattern "[A-Za-z0-9_-]" and thus are safe to use in URLs or for file names. The package also comes with a simple base32 encoder/decoder suited for case insensitive file systems.
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2025-04-22 |
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ligo-common
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public |
Base package for `ligo` python namespace
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2025-04-22 |
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r-htree
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public |
Historical regression trees are an extension of standard trees, producing a non-parametric estimate of how the response depends on all of its prior realizations as well as that of any time-varying predictor variables. The method applies equally to regularly as well as irregularly sampled data. The package implements random forest and boosting ensembles based on historical regression trees, suitable for longitudinal data. Standard error estimation and Z-score variable importance is also implemented.
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2025-04-22 |
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r-rfutilities
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public |
Utilities for Random Forest model selection, class balance correction, significance test, cross validation and partial dependency plots.
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2025-04-22 |
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r-huge
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public |
Provides a general framework for high-dimensional undirected graph estimation. It integrates data preprocessing, neighborhood screening, graph estimation, and model selection techniques into a pipeline. In preprocessing stage, the nonparanormal(npn) transformation is applied to help relax the normality assumption. In the graph estimation stage, the graph structure is estimated by Meinshausen-Buhlmann graph estimation or the graphical lasso, and both methods can be further accelerated by the lossy screening rule preselecting the neighborhood of each variable by correlation thresholding. We target on high-dimensional data analysis usually d >> n, and the computation is memory-optimized using the sparse matrix output. We also provide a computationally efficient approach, correlation thresholding graph estimation. Three regularization/thresholding parameter selection methods are included in this package: (1)stability approach for regularization selection (2) rotation information criterion (3) extended Bayesian information criterion which is only available for the graphical lasso.
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2025-04-22 |
