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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 |
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r-catboost
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public |
CatBoost is a machine learning algorithm that uses gradient boosting on decision trees. It is available as an open source library.
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2025-04-22 |
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libframe
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public |
LIGO/Virgo Frame Library
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2025-04-22 |
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r-logistf
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public |
Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.
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2025-04-22 |
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r-hier.part
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public |
Partitioning of the independent and joint contributions of each variable in a multivariate data set, to a linear regression by hierarchical decomposition of goodness-of-fit measures of regressions using all subsets of predictors in the data set. (i.e., model (1), (2), ..., (N), (1,2), ..., (1,N), ..., (1,2,3,...,N)). A Z-score based estimate of the 'importance' of each predictor is provided by using a randomisation test.
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2025-04-22 |
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coreapi-cli
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public |
An interactive command line client for Core API.
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2025-04-22 |
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python-coreapi
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public |
Python client library for Core API.
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2025-04-22 |
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rx
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public |
Reactive Extensions (Rx) for Python
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2025-04-22 |
