conda-forge / packages

Package Name Access Summary Updated
moldyn-clustering public Robust and stable clustering of molecular dynamics simulation trajectories 2020-07-03
rdkit public RDKit is a collection of cheminformatics and machine-learning software written in C++ and Python. 2020-07-03
diffoscope public in-depth comparison of files, archives, and directories 2020-07-03
conda-smithy None The tool for managing conda-forge feedstocks 2020-07-03
tvm-py public Open Deep Learning Compiler Stack 2020-07-03
pangeo-notebook public pangeo notebook dependencies 2020-07-03
orange-widget-base public Base Widget for Orange Canvas 2020-07-03
rdkit-dev public RDKit headers and library used in rdkit package 2020-07-03
python-dotenv public Get and set values in your .env file in local and production servers like Heroku does. 2020-07-03
conda-forge-repodata-patches public generate tweaks to index metadata, hosted separately from anaconda.org index 2020-07-03
trimesh public Import, export, process, analyze and view triangular meshes. 2020-07-03
xarray_mongodb public Store xarray objects in MongoDB 2020-07-03
hcrystalball public A time series library that unifies the API for most commonly used libraries and modelling techniques for time-series forecasting in the Python ecosystem. 2020-07-03
toytree public Minimalist tree plotting library using Toyplot. 2020-07-03
r-tidylog public Provides feedback about 'dplyr' and 'tidyr' operations. 2020-07-03
pangeo-dask public pangeo dask dependencies 2020-07-03
ecflow public ECMWF ecFlow 2020-07-03
libtvm public Open Deep Learning Compiler Stack 2020-07-03
ipycytoscape public Python implementation of the graph visualization tool Cytoscape 2020-07-03
flask-socketio public Socket.IO integration for Flask applications 2020-07-03
pandoc-plot public Render and include figures in Pandoc documents using your plotting toolkit of choice 2020-07-03
conda-forge-pinning public The baseline versions of software for the conda-forge ecosystem 2020-07-03
pyarrow public Python libraries for Apache Arrow 2020-07-03
arrow-cpp public C++ libraries for Apache Arrow 2020-07-03
orange3-timeseries public Orange3 add-on for exploring time series and sequential data. 2020-07-03
wradlib public Open Source Library for Weather Radar Data Processing 2020-07-03
sentry-sdk public The new Python SDK for Sentry.io 2020-07-03
r-meta public User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker <DOI:10.1007/978-3-319-21416-0>, "Meta-Analysis with R" (2015): - fixed effect and random effects meta-analysis; - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble); - statistical tests and trim-and-fill method to evaluate bias in meta-analysis; - import data from 'RevMan 5'; - prediction interval, Hartung-Knapp and Paule-Mandel method for random effects model; - cumulative meta-analysis and leave-one-out meta-analysis; - meta-regression; - generalised linear mixed models; - produce forest plot summarising several (subgroup) meta-analyses. 2020-07-03
dask public Parallel PyData with Task Scheduling 2020-07-03
terraform-provider-azurerm public The Terraform Azure provider 2020-07-03
distributed public Distributed computing with Dask 2020-07-03
terraform-provider-heroku public The Terraform Heroku provider 2020-07-03
dask-core public Parallel Python with task scheduling 2020-07-03
pyflyby public pyflyby - Python development productivity tools, in particular automatic import management 2020-07-03
pyadi-iio public Interfaces to stream data from ADI hardware 2020-07-03
umap-learn public Uniform Manifold Approximation and Projection 2020-07-03
terraform-provider-aws public The Terraform AWS provider 2020-07-03
pytmc public Generate EPICS IOCs and records from Beckhoff TwinCAT projects 2020-07-03
deon public A command line tool to easily add an ethics checklist to your data science projects. 2020-07-03
awscli public Universal Command Line Environment for AWS. 2020-07-02
boto3 public Amazon Web Services SDK for Python 2020-07-02
start_jupyter_cm public Add entries to start Jupyter from context menu. 2020-07-02
cdms2 public Community Data Management System 2020-07-02
libxgboost public Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow 2020-07-02
r-xgboost public Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow 2020-07-02
py-xgboost-cpu public Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow 2020-07-02
py-xgboost public Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow 2020-07-02
r-xgboost-cpu public Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow 2020-07-02
xgboost public Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow 2020-07-02
r-dharma public The 'DHARMa' package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Currently supported are linear and generalized linear (mixed) models from 'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB' and 'spaMM', generalized additive models ('gam' from 'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation. 2020-07-02
PRIVACY POLICY  |  EULA (Anaconda Cloud v2.33.29) © 2020 Anaconda, Inc. All Rights Reserved.