conda-forge / packages

Package Name Access Summary Updated
tclap public TCLAP is a small, flexible library that provides a simple interface for defining and accessing command line arguments. 2020-07-13
diff-match-patch public Diff Match Patch is a high-performance library in multiple languages that manipulates plain text 2020-07-13
graphene-django public Graphene Django integration 2020-07-13
ubiquerg public Various utility functions. 2020-07-13
m2crypto public M2Crypto: A Python crypto and SSL toolkit 2020-07-13
sos-notebook public Script of Scripts (SoS): an interactive, cross-platform, and cross-language workflow system for reproducible data analysis 2020-07-13
kedro public A Python library that implements software engineering best-practice for data and ML pipelines. 2020-07-13
terraform-provider-opsgenie public The Terraform OpsGenie provider 2020-07-13
r-arsenal public An Arsenal of 'R' functions for large-scale statistical summaries, which are streamlined to work within the latest reporting tools in 'R' and 'RStudio' and which use formulas and versatile summary statistics for summary tables and models. The primary functions include tableby(), a Table-1-like summary of multiple variable types 'by' the levels of one or more categorical variables; paired(), a Table-1-like summary of multiple variable types paired across two time points; modelsum(), which performs simple model fits on one or more endpoints for many variables (univariate or adjusted for covariates); freqlist(), a powerful frequency table across many categorical variables; comparedf(), a function for comparing data.frames; and write2(), a function to output tables to a document. 2020-07-13
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. 2020-07-13
srtm.py public Python parser for the Shuttle Radar Topography Mission elevation data 2020-07-13
libclang-cpp public Development headers and libraries for Clang 2020-07-13
libclang public Development headers and libraries for Clang 2020-07-13
clangxx public Development headers and libraries for Clang 2020-07-13
clangdev public Development headers and libraries for Clang 2020-07-13
clang-tools public Development headers and libraries for Clang 2020-07-13
clang public Development headers and libraries for Clang 2020-07-13
ipytest public Unit tests in IPython notebooks. 2020-07-13
markdown-it-py public Python port of markdown-it. Markdown parsing, done right! 2020-07-13
genutil public General Utitilites for the Community Data Analysys Tools 2020-07-13
r-pkgbuild public Provides functions used to build R packages. Locates compilers needed to build R packages on various platforms and ensures the PATH is configured appropriately so R can use them. 2020-07-13
libclang-cpp10 public Development headers and libraries for Clang 2020-07-13
r-yardstick public Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE). 2020-07-13
r-rlabkey public The 'LabKey' client library for R makes it easy for R users to load live data from a 'LabKey' Server, <http://www.labkey.com/>, into the R environment for analysis, provided users have permissions to read the data. It also enables R users to insert, update, and delete records stored on a 'LabKey' Server, provided they have appropriate permissions to do so. 2020-07-13
python-clang public Development headers and libraries for Clang 2020-07-13
spacy public Industrial-strength Natural Language Processing 2020-07-13
stevedore public Manage dynamic plugins for Python applications 2020-07-13
randomgen public Numpy-compatible bit generators and add some random variate distributions missing from NumPy. 2020-07-13
meson public The Meson Build System 2020-07-13
islpy public Wrapper around isl, an integer set library 2020-07-13
rich public Rich is a Python library for rich text and beautiful formatting in the terminal. 2020-07-13
diagrams public Diagram as Code 2020-07-13
fvcore public Collection of common code shared among different research projects in FAIR computer vision team 2020-07-13
cdsdashboards public A Dashboard publishing solution for Data Science teams to share results with decision makers. 2020-07-13
cdsdashboards-singleuser public A Dashboard publishing solution for Data Science teams to share results with decision makers. 2020-07-13
snakeviz public An in-browser Python profile viewer 2020-07-13
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. 2020-07-13
r-gamlss public Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables. 2020-07-13
r-gamlss.dist public A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a ''log'' or a ''logit' transformation respectively. 2020-07-13
xtl public The QuantStack tools library 2020-07-13
cgal public Computational Geometry Algorithms Library 2020-07-13
libccd public libccd is library for a collision detection between two convex shapes. 2020-07-13
pendulum public Python datetimes made easy 2020-07-13
r-brms public Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>. 2020-07-13
pyfakefs public A fake file system that mocks the Python file system modules. 2020-07-13
cx_oracle public Python interface to Oracle 2020-07-13
pytzdata public Official timezone database for Python 2020-07-13
flask-appbuilder public Simple and rapid application development framework, built on top of Flask. includes detailed security, auto CRUD generation for your models, google charts and much more. 2020-07-13
python-graphviz public Simple Python interface for Graphviz 2020-07-13
mppp public A modern C++ library for multiprecision arithmetic 2020-07-13
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