conda-forge / packages

Package Name Access Summary Updated
diffoscope public in-depth comparison of files, archives, and directories 2019-09-18
erlang public A programming language used to build massively scalable soft real-time systems with requirements on high availability. 2019-09-18
awscli public Universal Command Line Environment for AWS. 2019-09-17
boto3 public Amazon Web Services SDK for Python 2019-09-17
clang_osx-64 public clang compilers for conda-build 3 2019-09-17
openorb public An open-source orbit-computation package 2019-09-17
xsimd public C++ Wrappers for SIMD Intrinsices 2019-09-17
botocore public Low-level, data-driven core of boto 3. 2019-09-17
xtl public The QuantStack tools library 2019-09-17
ld64 public Darwin Mach-O linker 2019-09-17
cctools public Assembler, archiver, ranlib, libtool, otool et al for Darwin Mach-O files 2019-09-17
pynwb public A Python API for working with Neurodata stored in the NWB Format 2019-09-17
python-ldas-tools-al public Python bindings for the LDAS Tools abstraction toolkit 2019-09-17
r-spam public Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64). Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>; see 'citation("spam")' for details. 2019-09-17
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. 2019-09-17
r-tidypredict public It parses a fitted 'R' model object, and returns a formula in 'Tidy Eval' code that calculates the predictions. It works with several databases back-ends because it leverages 'dplyr' and 'dbplyr' for the final 'SQL' translation of the algorithm. It currently supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(), cubist(), and ctree() models. 2019-09-17
r-semtools public Provides useful tools for structural equation modeling. 2019-09-17
marshmallow public A lightweight library for converting complex datatypes to and from native Python datatypes. 2019-09-17
flask-apispec public Build and document REST APIs with Flask and apispec 2019-09-17
planet public Planet API Client 2019-09-17
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. 2019-09-17
pymonetdb public The Python API for MonetDB 2019-09-17
dateparser public Date parsing library designed to parse dates from HTML pages. 2019-09-17
r-markovchain public Functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. 2019-09-17
r-lpsolveapi public The lpSolveAPI package provides an R interface to 'lp_solve', a Mixed Integer Linear Programming (MILP) solver with support for pure linear, (mixed) integer/binary, semi-continuous and special ordered sets (SOS) models. 2019-09-17
r-lobstr public A set of tools for inspecting and understanding R data structures inspired by str(). Includes ast() for visualizing abstract syntax trees, ref() for showing shared references, cst() for showing call stack trees, and obj_size() for computing object sizes. 2019-09-17
r-emmeans public Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>. 2019-09-17
r-rpostgres public Fully 'DBI'-compliant 'Rcpp'-backed interface to 'PostgreSQL' <>, an open-source relational database. 2019-09-17
tabulator public Consistent interface for stream reading and writing tabular data (csv/xls/json/etc) 2019-09-17
reportlab public Open-source engine for creating complex, data-driven PDF documents and custom vector graphics 2019-09-17
ligo-gracedb public Gravitational Wave Candidate Event Database 2019-09-17
r-tidyr public Tools to help to create tidy data, where each column is a variable, each row is an observation, and each cell contains a single value. 'tidyr' contains tools for changing the shape (pivoting) and hierarchy (nesting and 'unnesting') of a dataset, turning deeply nested lists into rectangular data frames ('rectangling'), and extracting values out of string columns. It also includes tools for working with missing values (both implicit and explicit). 2019-09-17
r-dt public Data objects in R can be rendered as HTML tables using the JavaScript library 'DataTables' (typically via R Markdown or Shiny). The 'DataTables' library has been included in this R package. The package name 'DT' is an abbreviation of 'DataTables'. 2019-09-17
r-curl public The curl() and curl_download() functions provide highly configurable drop-in replacements for base url() and download.file() with better performance, support for encryption (https, ftps), gzip compression, authentication, and other 'libcurl' goodies. The core of the package implements a framework for performing fully customized requests where data can be processed either in memory, on disk, or streaming via the callback or connection interfaces. Some knowledge of 'libcurl' is recommended; for a more-user-friendly web client see the 'httr' package which builds on this package with http specific tools and logic. 2019-09-17
r-sparklyr public R interface to Apache Spark, a fast and general engine for big data processing, see <>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms. 2019-09-17
r-tinytex public Helper functions to install and maintain the 'LaTeX' distribution named 'TinyTeX' (<>), a lightweight, cross-platform, portable, and easy-to-maintain version of 'TeX Live'. This package also contains helper functions to compile 'LaTeX' documents, and install missing 'LaTeX' packages automatically. 2019-09-17
msrestazure public The runtime library "msrestazure" for AutoRest generated Python clients 2019-09-16
conda-smithy None The tool for managing conda-forge feedstocks 2019-09-16
tyssue public A tissue simulation library 2019-09-16
osqp public Python interface for OSQP, the Operator Splitting QP Solver 2019-09-16
dask-glm public Generalized Linear Models in Dask 2019-09-16
libcdms public Climate Data Management System library 2019-09-16
redis-py public Python client for Redis key-value store 2019-09-16
marshmallow-jsonapi public JSON API 1.0 formatting with marshmallow 2019-09-16
iris-sample-data public Iris sample data. 2019-09-16
lz4 public LZ4 Bindings for Python 2019-09-16
zstandard public Zstandard bindings for Python 2019-09-16
r-recipes public An extensible framework to create and preprocess design matrices. Recipes consist of one or more data manipulation and analysis "steps". Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting design matrices can then be used as inputs into statistical or machine learning models. 2019-09-16
geos public Geometry Engine - Open Source. 2019-09-16
pugixml public Light-weight, simple and fast XML parser for C++ with XPath support 2019-09-16
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