conda-forge / packages

Package Name Access Summary Updated
giflib public Library for reading and writing gif images 2020-02-22
rdkit public RDKit is a collection of cheminformatics and machine-learning software written in C++ and Python. 2020-02-22
mlpack public mlpack a fast, flexible machine learning library 2020-02-22
pyodeint public Python wrapper around odeint (from the boost C++ library) 2020-02-22
r-doby public Contains: 1) Facilities for working with grouped data: 'do' something to data stratified 'by' some variables. 2) LSmeans (least-squares means), general linear contrasts. 3) Miscellaneous other utilities. 2020-02-22
highfive public Header-only C++ HDF5 interface 2020-02-22
openturns public Uncertainty treatment library 2020-02-22
r-fda public These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer. They were ported from earlier versions in Matlab and S-PLUS. An introduction appears in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009) Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions of the code and sample analyses are no longer distributed through CRAN, as they were when the book was published. For those, ftp from <http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/> There you find a set of .zip files containing the functions and sample analyses, as well as two .txt files giving instructions for installation and some additional information. The changes from Version 2.4.1 are fixes of bugs in density.fd and removal of functions create.polynomial.basis, polynompen, and polynomial. These were deleted because the monomial basis does the same thing and because there were errors in the code. 2020-02-22
python-box public Python dictionaries with recursive dot notation access 2020-02-22
r-mice public Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations. 2020-02-22
nodejs public a platform for easily building fast, scalable network applications 2020-02-21
clickhouse-driver public Python driver with native interface for ClickHouse 2020-02-21
awscli public Universal Command Line Environment for AWS. 2020-02-21
boto3 public Amazon Web Services SDK for Python 2020-02-21
botocore public Low-level, data-driven core of boto 3. 2020-02-21
trimesh public Import, export, process, analyze and view triangular meshes. 2020-02-21
pycryptodome public Cryptographic library for Python 2020-02-21
mpi4py public Python bindings for MPI 2020-02-21
r-squarem public Algorithms for accelerating the convergence of slow, monotone sequences from smooth, contraction mapping such as the EM algorithm. It can be used to accelerate any smooth, linearly convergent acceleration scheme. A tutorial style introduction to this package is available in a vignette on the CRAN download page or, when the package is loaded in an R session, with vignette("SQUAREM"). 2020-02-21
pint public Operate and manipulate physical quantities in Python 2020-02-21
r-plm public A set of estimators and tests for panel data econometrics, as described in Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7, Hsiao (2014) Analysis of Panel Data <doi:10.1017/CBO9781139839327> and Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4. 2020-02-21
postgresql-plpython public The plpythonu postgresql extension 2020-02-21
postgresql public PostgreSQL is a powerful, open source object-relational database system. 2020-02-21
libpq public The postgres runtime libraries and utilities (not the server itself) 2020-02-21
statsmodels public Statistical computations and models for use with SciPy 2020-02-21
pysurfer public Cortical surface visualization using Python 2020-02-21
jug public Task based Python workflow system 2020-02-21
r-sp public Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc. 2020-02-21
alpenglow public Open Source Recommender Framework with Time-aware Learning and Evaluation 2020-02-21
scikit-rf public Object Oriented Microwave Engineering. 2020-02-21
iris public Analyse and visualise meteorological and oceanographic data sets. 2020-02-21
starry public Analytic occultation light curves for astronomy 2020-02-21
fenics public FEniCS is a collection of free software for automated, efficient solution of differential equations 2020-02-21
openexr public OpenEXR is a high dynamic-range (HDR) image file format developed by Industrial Light & Magic for use in computer imaging applications. 2020-02-21
pycryptodomex public Cryptographic library for Python 2020-02-21
ilmbase public IlmBase libraries required for OpenEXR. 2020-02-21
r-tsp public Basic infrastructure and some algorithms for the traveling salesperson problem (also traveling salesman problem; TSP). The package provides some simple algorithms and an interface to the Concorde TSP solver and its implementation of the Chained-Lin-Kernighan heuristic. The code for Concorde itself is not included in the package and has to be obtained separately. 2020-02-21
r-varhandle public Variables are the fundamental parts of each programming language but handling them efficiently might be frustrating for programmers. This package contains some functions to help user (especially data explorers) to make more sense of their variables and take the most out of variables and hardware resources. These functions are written, collected and crafted over 7 years of experience in statistical data analysis on high-dimensional data, and for each of them there was a need. Functions in this package are suppose to be efficient and easy to use, hence they will be frequently updated to make them more convenient. 2020-02-21
r-opencpu public A system for embedded scientific computing and reproducible research with R. The OpenCPU server exposes a simple but powerful HTTP api for RPC and data interchange with R. This provides a reliable and scalable foundation for statistical services or building R web applications. The OpenCPU server runs either as a single-user development server within the interactive R session, or as a multi-user Linux stack based on Apache2. The entire system is fully open source and permissively licensed. The OpenCPU website has detailed documentation and example apps. 2020-02-21
bottleneck public Fast NumPy array functions written in Cython. 2020-02-21
trilinos public Sandia's suite of HPC solvers and enabling technologies 2020-02-21
mrjob public Python MapReduce framework 2020-02-21
kubernetes-helm public Helm is a package manager for kubernetes 2020-02-21
fitsio public A python library to read from and write to FITS files. 2020-02-20
blas public Metapackage to select the BLAS variant. Use conda's pinning mechanism in your environment to control which variant you want. 2020-02-20
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. 2020-02-20
r-vctrs public Defines new notions of prototype and size that are used to provide tools for consistent and well-founded type-coercion and size-recycling, and are in turn connected to ideas of type- and size-stability useful for analyzing function interfaces. 2020-02-20
pymapd public A python DB API 2 compatible client for mapd. 2020-02-20
testfixtures public A collection of helpers and mock objects for unit tests and doc tests. 2020-02-20
r-rcsdp public R interface to the CSDP semidefinite programming library. Installs version 6.1.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. See the INSTALL file for further details. 2020-02-20
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