conda-forge / packages

Package Name Access Summary Updated
awkward public Manipulate arrays of complex data structures as easily as Numpy. 2019-05-23
tomopy public Tomographic reconstruction in Python. 2019-05-23
signac-dashboard public signac-dashboard: data visualization for signac 2019-05-23
pyxem public An open-source Python library for crystallographic electron microscopy 2019-05-23
adaptive-scheduler public An asynchronous scheduler using MPI for Adaptive 2019-05-23
parallel public GNU parallel is a shell tool for executing jobs in parallel using one or more computers. 2019-05-23
google-cloud-datastore public Python Client for Google Cloud Datastore 2019-05-23
k3d public Jupyter notebook extension for 3D visualization. 2019-05-23
geant4-data-particlexs public GEANT4 data files for evaluated particle cross-sections on natural composition of elements 2019-05-23
pdal public Point Data Abstraction Library (PDAL) 2019-05-23
google-compute-engine public Google Compute Engine 2019-05-23
omniscidb-cpu public The OmniSci database 2019-05-23
openmesh-python public OpenMesh Python Bindings 2019-05-23
sparsehash-c11 public Experimental C++11 version of sparsehash 2019-05-23
rockhound public Download geophysical models/datasets and load them in Python 2019-05-23
fakeredis public Fake implementation of redis API for testing purposes. 2019-05-23
sqlite public Implements a self-contained, zero-configuration, SQL database engine 2019-05-23
proj4 public Cartographic projection software. 2019-05-23
enaml public Declarative DSL for building rich user interfaces in Python 2019-05-23
google-api-python-client public Google API Client Library for Python 2019-05-23
tableschema public A utility library for working with Table Schema in Python 2019-05-23
questplus public A QUEST+ implementation in Python 2019-05-23
iminuit public Interactive Minimization Tools based on MINUIT 2019-05-23
pymor public pyMOR is a software library for building model order reduction applications with the Python programming language. Implemented algorithms include reduced basis methods for parametric linear and non-linear problems, as well as system-theoretic methods such as balanced truncation or IRKA. All algorithms in pyMOR are formulated in terms of abstract interfaces for seamless integration with external PDE solver packages. Moreover, pure Python implementations of finite element and finite volume discretizations using the NumPy/SciPy scientific computing stack are provided for getting started quickly. 2019-05-23
ocamlbuild public OCamlbuild is a generic build tool, that has built-in rules for building OCaml library and programs. 2019-05-23
hyperspy-gui-traitsui public traitsui GUI elements for HyperSpy. 2019-05-23
scikit-multiflow public A machine learning framework for multi-output/multi-label and stream data. 2019-05-23
qscintilla2 public Qscintilla2 editor for Qt 2019-05-23
geonum public Toolbox for 3D geonumerical calculations 2019-05-23
ensmallen public ensmallen is a C++ header-only library for mathematical optimization. 2019-05-23
r-bayesplot public Plotting functions for posterior analysis, prior and posterior predictive checks, and MCMC diagnostics. The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'. 2019-05-23
mapkit public A Python module with mapping functions for PostGIS enabled PostgreSQL databases. 2019-05-23
r-brms 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: B端rkner (2017) <doi:10.18637/jss.v080.i01>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>. 2019-05-23
mss public A web service based tool to plan atmospheric research flights. 2019-05-23
ptvsd public Python debugger package for use with Visual Studio and Visual Studio Code 2019-05-23
google-cloud-storage public Python Client for Google Cloud Storage 2019-05-23
pymdwizard public A CSDGM Metadata Editor. 2019-05-23
wxpython public Cross platform GUI toolkit for Python, "Phoenix" version 2019-05-23
xonsh public Python-powered, cross-platform, Unix-gazing shell 2019-05-23
pyproj public Python interface to PROJ.4 library 2019-05-22
satpy public Meteorological processing package 2019-05-22
funcargparse public Create an argparse.ArgumentParser from function docstrings 2019-05-22
docrep public Python package for docstring repetition 2019-05-22
pyusid public Framework for storing, visualizing, and processing Universal Spectroscopic and Imaging Data (USID) 2019-05-22
eliot public Action-tracing logging for Python: the logging library that tells you why it happened 2019-05-22
reprounzip-qt public Graphical user interface for reprounzip, using Qt 2019-05-22
starlette public The little ASGI framework that shines. ✨ 2019-05-22
mayavi public The Mayavi scientific data 3-dimensional visualizers 2019-05-22
pyface public pyface - traits-capable windowing framework 2019-05-22
igl public Simple python geometry processing library 2019-05-22
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