conda-forge / packages

Package Name Access Summary Updated
clickhouse-driver public Python driver with native interface for ClickHouse 2019-10-19
sos public Script of Scripts (SoS): an interactive, cross-platform, and cross-language workflow system for reproducible data analysis 2019-10-19
namaster public pseudo-Cl power spectra w/ masking for spin-0 and spin-2 fields 2019-10-19
terraform public Terraform is a tool for building, changing, and combining infrastructure safely and efficiently. 2019-10-18
nss public A set of libraries designed to support cross-platform development of security-enabled client and server applications. 2019-10-18
pseudonetcdf public PseudoNetCDF like NetCDF except for many scientific format backends 2019-10-18
pandas public High-performance, easy-to-use data structures and data analysis tools. 2019-10-18
metpy public MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data. 2019-10-18
awscli public Universal Command Line Environment for AWS. 2019-10-18
boto3 public Amazon Web Services SDK for Python 2019-10-18
django-crispy-forms public The best way to have DRY Django forms. The app provides a tag and filter that lets you quickly render forms in a div format while providing an enormous amount of capability to configure and control the rendered HTML. 2019-10-18
botocore public Low-level, data-driven core of boto 3. 2019-10-18
pywavelets public Discrete Wavelet Transforms in Python 2019-10-18
openpiv public Open Source Particle Image Velocimetry 2019-10-18
r-purrr public A complete and consistent functional programming toolkit for R. 2019-10-18
xmlschema public An XML Schema validator and decoder 2019-10-18
dash public A Python framework for building reactive web-apps. 2019-10-18
ciso8601 public Fast ISO8601 date time parser for Python written in C 2019-10-18
output_viewer public The Output Viewer is designed to provide a framework for viewing arbitrary output from diagnostics scripts, metrics, or any program that produces a ton of different files that you want to look at it in an easy-to-use fashion. 2019-10-18
pyagrum public A wrapper for the Agrum library, to make flexible and scalable probabilistic graphical models. 2019-10-18
xeus-python public Jupyter kernel for the Python programming language based on xeus 2019-10-18
esmf public The Earth System Modeling Framework (ESMF) is software for building and coupling weather, climate, and related models.' 2019-10-18
hdbscan public Clustering based on density with variable density clusters 2019-10-18
pylint public python code static checker 2019-10-18
orekit public An accurate and efficient core layer for space flight dynamics applications 2019-10-18
python-avro public Avro is a serialization and RPC framework. 2019-10-18
mypy_extensions public Experimental type system extensions for programs checked with the mypy typechecker. 2019-10-18
jcc public a C++ code generator for calling Java from C++/Python 2019-10-18
expat public Expat XML parser library in C. 2019-10-18
astroid public A abstract syntax tree for Python with inference support. 2019-10-18
pip public PyPA recommended tool for installing Python packages 2019-10-18
hvplot public A high-level plotting API for the PyData ecosystem built on HoloViews 2019-10-18
sqlalchemy_exasol public SQLAlchemy dialect for EXASOL 2019-10-18
r-tmle public Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments. 2019-10-18
ptscotch public PT-SCOTCH: (Parallel) Static Mapping, Graph, Mesh and Hypergraph Partitioning, and Parallel and Sequential Sparse Matrix Ordering Package 2019-10-18
scotch public SCOTCH: Static Mapping, Graph, Mesh and Hypergraph Partitioning, and Parallel and Sequential Sparse Matrix Ordering Package 2019-10-18
parcels public Probably A Really Computationally Efficient Lagrangian Simulator 2019-10-18
git-annex public A tool for managing large files with git 2019-10-18
metsim public Meteorology Simulator for Python 2019-10-18
ansible public Ansible is a radically simple IT automation platform 2019-10-18
ipaddress public IPv4/IPv6 manipulation library 2019-10-18
dash-core-components public Dash UI core component suite 2019-10-18
google-cloud-bigquery public Python Client for Google BigQuery 2019-10-18
hs_restclient public HydroShare REST API client library 2019-10-18
gmprocess public Fetch and process strong motion waveform/peak amplitude data 2019-10-17
dash-table public A First-Class Interactive DataTable for Dash 2019-10-17
r-taxize public Interacts with a suite of web 'APIs' for taxonomic tasks, such as getting database specific taxonomic identifiers, verifying species names, getting taxonomic hierarchies, fetching downstream and upstream taxonomic names, getting taxonomic synonyms, converting scientific to common names and vice versa, and more. 2019-10-17
r-styler public Pretty-prints R code without changing the user's formatting intent. 2019-10-17
dash-renderer public Front-end component renderer for dash 2019-10-17
multinest public MultiNest is a Bayesian inference tool which calculates the evidence and explores the parameter space which may contain multiple posterior modes and pronounced (curving) degeneracies in moderately high dimensions. 2019-10-17
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