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bisonpp
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public |
The original bison++ project, brought up to date with modern compilers
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2025-04-22 |
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datacache
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public |
Helpers for transparently downloading datasets
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2025-04-22 |
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r-paco
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public |
Procrustes analyses to infer co-phylogenetic matching between pairs of (ultrametric) phylogenetic trees.
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2025-04-22 |
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python_http_client
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public |
SendGrid's Python HTTP Client for calling APIs
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2025-04-22 |
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w3g
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public |
Access Warcraft 3 replay files from Python 2 or 3
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2025-04-22 |
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trollimage
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public |
Pytroll imaging library
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2025-04-22 |
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memoized-property
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public |
A simple python decorator for defining properties that only run their fget function once
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2025-04-22 |
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aggdraw
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public |
High quality drawing interface for PIL.
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2025-04-22 |
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cykdtree
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public |
Cython based KD-Tree
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2025-04-22 |
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grove
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public |
A collection of quantum algorithms built using pyQuil and Forest
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2025-04-22 |
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bamnostic
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public |
a pure Python, OS-agnositic Binary Alignment Map (BAM) file parser and random access tool.
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2025-04-22 |
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chemdataextractor
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public |
Automatically extract chemical information from scientific documents.
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2025-04-22 |
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sexpdata
|
public |
S-expression parser for Python
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2025-04-22 |
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unittest2
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public |
Recent features of the unittest package backported to Python <= 3.4.
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2025-04-22 |
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typechecks
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public |
Helper functions for runtime type checking
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2025-04-22 |
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tinytimer
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public |
Tiny Python benchmarking library
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2025-04-22 |
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sphinx-releases
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public |
A powerful Sphinx changelog-generating extension
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2025-04-22 |
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tensorly
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public |
Tensor learning in Python
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2025-04-22 |
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r-brms
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public |
Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
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2025-04-22 |
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pyunfold
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public |
PyUnfold: A Python package for iterative unfolding
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2025-04-22 |
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pyulog
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public |
Python library to parse ULog files for PX4 autopilots.
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2025-04-22 |
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numina
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public |
Astronomy data reduction library
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2025-04-22 |
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progressbar33
|
public |
Text progress bar library for Python.
|
2025-04-22 |
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pytest-console-scripts
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public |
Pytest plugin for testing console scripts
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2025-04-22 |
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nose2
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public |
nose2 is the next generation of nicer testing for Python
|
2025-04-22 |
