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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