conda-forge / packages

Package Name Access Summary Updated
betse public BETSE, the BioElectric Tissue Simulation Engine 2020-02-19
thermo public Chemical properties component of Chemical Engineering Design Library (ChEDL) 2020-02-19
singularity public Singularity: Application containers for Linux 2020-02-19
deon public A command line tool to easily add an ethics checklist to your data science projects. 2020-02-19
hug public A Python framework that makes developing APIs as simple as possible, but no simpler. 2020-02-19
fairlearn public Simple and easy fairness assessment and unfairness mitigation 2020-02-19
nodejs public a platform for easily building fast, scalable network applications 2020-02-19
distributed public Distributed computing with Dask 2020-02-19
diffoscope public in-depth comparison of files, archives, and directories 2020-02-19
localstack-ext public Extensions for LocalStack 2020-02-19
r-rsparse public Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines. Also 'rsparse' enhances 'Matrix' package by providing methods for multithreaded <sparse, dense> matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>) 2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>) List of algorithms for matrix factorization and matrix completion: 1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, <doi:10.1145/1102351.1102441>) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, <arXiv:1410.2596>) 4) Linear-Flow matrix factorization, from 'Practical linear models for large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, <https://www.aclweb.org/anthology/D14-1162>) Package is reasonably fast and memory efficient - it allows to work with large datasets - millions of rows and millions of columns. This is particularly useful for practitioners working on recommender systems. 2020-02-19
cupy public CuPy is an implementation of a NumPy-compatible multi-dimensional array on CUDA. 2020-02-19
automat public self-service finite-state machines for the programmer on the go 2020-02-19
r-tidymodels public The tidy modeling "verse" is a collection of packages for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the tidyverse. 2020-02-19
draco public A library for compressing and decompressing 3D geometric meshes and point clouds 2020-02-18
genpy public No Summary 2020-02-18
r-float public R comes with a suite of utilities for linear algebra with "numeric" (double precision) vectors/matrices. However, sometimes single precision (or less!) is more than enough for a particular task. This package extends R's linear algebra facilities to include 32-bit float (single precision) data. Float vectors/matrices have half the precision of their "numeric"-type counterparts but are generally faster to numerically operate on, for a performance vs accuracy trade-off. The internal representation is an S4 class, which allows us to keep the syntax identical to that of base R's. Interaction between floats and base types for binary operators is generally possible; in these cases, type promotion always defaults to the higher precision. The package ships with copies of the single precision 'BLAS' and 'LAPACK', which are automatically built in the event they are not available on the system. 2020-02-18
r-sweep public Tidies up the forecasting modeling and prediction work flow, extends the 'broom' package with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions for various forecasting models, and enables converting 'forecast' objects to "tidy" data frames with 'sw_sweep'. 2020-02-18
psutil public A cross-platform process and system utilities module for Python 2020-02-18
boost public Free peer-reviewed portable C++ source libraries. 2020-02-18
r-lgr public A flexible, feature-rich yet light-weight logging framework based on 'R6' classes. It supports hierarchical loggers, custom log levels, arbitrary data fields in log events, logging to plaintext, 'JSON', (rotating) files, memory buffers, and databases, as well as email and push notifications. For a full list of features with examples please refer to the package vignette. 2020-02-18
requests-ecp public SAML/ECP authentication handler for python-requests 2020-02-18
hs-process public An open-source Python package for geospatial processing of aerial hyperspectral imagery 2020-02-18
pytest-flask-sqlalchemy public A pytest plugin for preserving test isolation in Flask-SQLAlchemy using database transactions. 2020-02-18
r-tune public The ability to tune models is important. 'tune' contains functions and classes to be used in conjunction with other 'tidymodels' packages for finding reasonable values of hyper-parameters in models, pre-processing methods, and post-processing steps. 2020-02-18
google-auth-oauthlib public Google Authentication Library, oauthlib integration with google-auth 2020-02-18
awscli public Universal Command Line Environment for AWS. 2020-02-18
boost-cpp public Free peer-reviewed portable C++ source libraries. 2020-02-18
json_tricks public Extra features for Python's JSON: comments, order, numpy, pandas, datetimes, and many more! Simple but customizable. 2020-02-18
boto3 public Amazon Web Services SDK for Python 2020-02-18
jupyter_core public Core common functionality of Jupyter projects. 2020-02-18
fmrib-unpack public The FMRIB UKBioBank Normalisation, Parsing And Cleaning Kit 2020-02-18
r-tidyquant public Bringing financial analysis to the 'tidyverse'. The 'tidyquant' package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' and 'PerformanceAnalytics' package functions and returns the objects in the tidy 'tibble' format. The main advantage is being able to use quantitative functions with the 'tidyverse' functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See the 'tidyquant' website for more information, documentation and examples. 2020-02-18
xeus-python public Jupyter kernel for the Python programming language based on xeus 2020-02-18
lifetimes public Measure customer lifetime value in Python 2020-02-18
pre-commit public A framework for managing and maintaining multi-language pre-commit hooks. 2020-02-18
scikit-multiflow public A machine learning framework for multi-output/multi-label and stream data. 2020-02-18
botocore public Low-level, data-driven core of boto 3. 2020-02-18
jupytext public Jupyter notebooks as Markdown documents, Julia, Python or R scripts 2020-02-18
paintera public Python command line launcher for Paintera 2020-02-18
act-atmos public Python library for working with atmospheric time-series by n-dimension datasets 2020-02-18
sphinxcontrib-autodoc_doxygen public Doxygen / Sphinx bridge, with autodoc and autosummary 2020-02-18
inquirer public Collection of common interactive command line user interfaces, based on Inquirer.js 2020-02-18
tomopy public Tomographic reconstruction in Python. 2020-02-18
arm_pyart public Python ARM Radar Toolkit 2020-02-18
r-timetk public Get the time series index (date or date-time component), time series signature (feature extraction of date or date-time component for time series machine learning), and time series summary (summary attributes about time series). Create future time series based on properties of existing time series index using logistic regression. Coerce between time-based tibbles ('tbl') and 'xts', 'zoo', and 'ts'. Methods discussed herein are commonplace in machine learning, and have been cited in various literature. Refer to "Calendar Effects" in papers such as Taieb, Souhaib Ben. "Machine learning strategies for multi-step-ahead time series forecasting." Universit Libre de Bruxelles, Belgium (2014): 75-86. <http://souhaib-bentaieb.com/pdf/2014_phd.pdf>. 2020-02-18
cartopy_offlinedata public Override cartopy.config to use offline data stored in the conda env. 2020-02-18
pop-tools public Tools to support analysis of POP2-CESM model solutions with xarray 2020-02-18
readchar public Library to easily read single chars and key strokes. 2020-02-18
imageio-ffmpeg public FFMPEG wrapper for Python 2020-02-18
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