conda-forge / packages

Package Name Access Summary Updated
trimesh public Import, export, process, analyze and view triangular meshes. 2020-01-20
segno public Segno is a QR Code and Micro QR Code encoder which has no further dependencies. 2020-01-20
xwebrtc public C++ backend for the jupyter webrtc widget 2020-01-19
gast public A generic AST to represent Python2 and Python3's Abstract Syntax Tree(AST). 2020-01-19
setuptools public Download, build, install, upgrade, and uninstall Python packages 2020-01-19
numpy public Array processing for numbers, strings, records, and objects. 2020-01-19
odfpy public Python API and tools to manipulate OpenDocument files 2020-01-19
setuptools_scm public The blessed package to manage your versions by scm tags 2020-01-19
django-recaptcha public Django recaptcha form field/widget app. 2020-01-19
tinyarray public Arrays of numbers for Python, optimized for small sizes 2020-01-19
r-stanheaders public The C++ header files of the Stan project are provided by this package, but it contains little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is only useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models. 2020-01-19
r-agricolae public Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster. 2020-01-19
r-adehabitatlt public A collection of tools for the analysis of animal movements. 2020-01-19
r-xts public Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability. 2020-01-19
python-jsonrpc-server public A Python 2.7 and 3.4+ server implementation of the JSON RPC 2.0 protocol. 2020-01-19
pytables public Brings together Python, HDF5 and NumPy to easily handle large amounts of data. 2020-01-19
ipympl public Matplotlib Jupyter Extension 2020-01-19
r-rcurl public A wrapper for 'libcurl' Provides functions to allow one to compose general HTTP requests and provides convenient functions to fetch URIs, get & post forms, etc. and process the results returned by the Web server. This provides a great deal of control over the HTTP/FTP/... connection and the form of the request while providing a higher-level interface than is available just using R socket connections. Additionally, the underlying implementation is robust and extensive, supporting FTP/FTPS/TFTP (uploads and downloads), SSL/HTTPS, telnet, dict, ldap, and also supports cookies, redirects, authentication, etc. 2020-01-19
elasticsearch public Python client for Elasticsearch 2020-01-19
gdk-pixbuf public GdkPixbuf is a library for image loading and manipulation. 2020-01-19
pvlib-python public A set functions and classes for simulating the performance of photovoltaic energy systems. 2020-01-18
libva public Libva is an implementation for VA-API (Video Acceleration API) 2020-01-18
gpxpy public GPX file parser and GPS track manipulation library 2020-01-18
xleaflet public C++ backend for the jupyter leaflet widget 2020-01-18
libsemigroups public C++ library for semigroups and monoids 2020-01-18
r-log4r public The log4r package is meant to provide a fast, lightweight, object-oriented approach to logging in R based on the widely-emulated 'log4j' system and etymology. 2020-01-18
pybind11 public Seamless operability between C++11 and Python 2020-01-18
gidgethub public An async GitHub API library for Python 2020-01-18
cymetric public Cyclus Metrics 2020-01-18
awscli public Universal Command Line Environment for AWS. 2020-01-18
boto3 public Amazon Web Services SDK for Python 2020-01-18
botocore public Low-level, data-driven core of boto 3. 2020-01-18
pivy public python bindings to coin3d. 2020-01-17
r-factominer public Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017). 2020-01-17
pyfmmlib public Python wrappers for FMMLIB2D and FMMLIB3D 2020-01-17
pyside2 public Python bindings for Qt 2020-01-17
gmp public The GNU multiprecision library. 2020-01-17
soqt public SoQt library needed by Coin3d. 2020-01-17
dynesty public A dynamic nested sampling package for computing Bayesian posteriors and evidences. 2020-01-17
coin3d public Coin3D is a c++ high-level 3D graphics toolkit. 2020-01-17
googleapis-common-protos public Common protobufs used in Google APIs 2020-01-17
orekit public An accurate and efficient core layer for space flight dynamics applications 2020-01-17
pycsw public OGC Catalogue Service for the Web (CSW) server implementation written in Python. 2020-01-17
django-ajax-selects public Edit ForeignKey, ManyToManyField and CharField in Django Admin using jQuery UI AutoComplete. 2020-01-17
r-sjlabelled public Collection of functions dealing with labelled data, like reading and writing data between R and other statistical software packages like 'SPSS', 'SAS' or 'Stata', and working with labelled data. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values. 2020-01-17
alpenglow public Open Source Recommender Framework with Time-aware Learning and Evaluation 2020-01-17
pytest public Simple and powerful testing with Python. 2020-01-17
orange3 public component-based data mining framework 2020-01-17
r-rhpcblasctl public Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'OpenBLAS', 'ACML', 'BLIS' and 'MKL'). And possible to control the number of threads in 'OpenMP'. Get a number of logical cores and physical cores if feasible. 2020-01-17
r-fpc public Various methods for clustering and cluster validation. Fixed point clustering. Linear regression clustering. Clustering by merging Gaussian mixture components. Symmetric and asymmetric discriminant projections for visualisation of the separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index. Standardisation of cluster validation statistics by random clusterings and comparison between many clustering methods and numbers of clusters based on this. Cluster-wise cluster stability assessment. Methods for estimation of the number of clusters: Calinski-Harabasz, Tibshirani and Walther's prediction strength, Fang and Wang's bootstrap stability. Gaussian/multinomial mixture fitting for mixed continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures. For an overview see package?fpc. 2020-01-17
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