Package Name | Access | Summary | Updated |
---|---|---|---|

boost | public | Free peer-reviewed portable C++ source libraries. | 2020-02-19 |

r-openmx | public | Facilitates treatment of statistical model specifications as things that can be generated and manipulated programmatically. Structural equation models may be specified with reticular action model matrices or paths, linear structural relations matrices or paths, or directly in matrix algebra. Fit functions include full information maximum likelihood, maximum likelihood, and weighted least squares. Example models include confirmatory factor, multiple group, mixture distribution, categorical threshold, modern test theory, differential equations, state space, and many others. MacOS users can download the most up-to-date package binaries from <http://openmx.ssri.psu.edu>. See Neale, Hunter, Pritikin, Zahery, Brick, Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016) <doi:10.1007/s11336-014-9435-8>. | 2020-02-19 |

imageio | public | A Python library for reading and writing image data | 2020-02-19 |

chardet | public | Universal character encoding detector | 2020-02-19 |

idna | public | Internationalized Domain Names in Applications (IDNA). | 2020-02-19 |

r-qtlrel | public | This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances. | 2020-02-19 |

r-grpreg | public | Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge. | 2020-02-19 |

r-mcmc | public | Simulates continuous distributions of random vectors using Markov chain Monte Carlo (MCMC). Users specify the distribution by an R function that evaluates the log unnormalized density. Algorithms are random walk Metropolis algorithm (function metrop), simulated tempering (function temper), and morphometric random walk Metropolis (Johnson and Geyer, 2012, <https://doi.org/10.1214/12-AOS1048>, function morph.metrop), which achieves geometric ergodicity by change of variable. | 2020-02-19 |

r-lintr | public | Checks adherence to a given style, syntax errors and possible semantic issues. Supports on the fly checking of R code edited with 'RStudio IDE', 'Emacs', 'Vim', 'Sublime Text' and 'Atom'. | 2020-02-19 |

r-klar | public | Miscellaneous functions for classification and visualization, e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, an interface to 'svmlight' and stepclass() wrapper variable selection for supervised classification, partimat() visualization of classification rules and shardsplot() of cluster results as well as kmodes() clustering for categorical data, corclust() variable clustering, variable extraction from different variable clustering models and weight of evidence preprocessing. | 2020-02-19 |

r-gdistance | public | Calculate distances and routes on geographic grids. | 2020-02-19 |

betse | public | BETSE, the BioElectric Tissue Simulation Engine | 2020-02-19 |

thermo | public | Chemical properties component of Chemical Engineering Design Library (ChEDL) | 2020-02-19 |

singularity | public | Singularity: Application containers for Linux | 2020-02-19 |

deon | public | A command line tool to easily add an ethics checklist to your data science projects. | 2020-02-19 |

hug | public | A Python framework that makes developing APIs as simple as possible, but no simpler. | 2020-02-19 |

fairlearn | public | Simple and easy fairness assessment and unfairness mitigation | 2020-02-19 |

nodejs | public | a platform for easily building fast, scalable network applications | 2020-02-19 |

distributed | public | Distributed computing with Dask | 2020-02-19 |

diffoscope | public | in-depth comparison of files, archives, and directories | 2020-02-19 |

localstack-ext | public | Extensions for LocalStack | 2020-02-19 |

r-rsparse | public | Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines. Also 'rsparse' enhances 'Matrix' package by providing methods for multithreaded <sparse, dense> matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>) 2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>) List of algorithms for matrix factorization and matrix completion: 1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, <doi:10.1145/1102351.1102441>) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, <arXiv:1410.2596>) 4) Linear-Flow matrix factorization, from 'Practical linear models for large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, <https://www.aclweb.org/anthology/D14-1162>) Package is reasonably fast and memory efficient - it allows to work with large datasets - millions of rows and millions of columns. This is particularly useful for practitioners working on recommender systems. | 2020-02-19 |

cupy | public | CuPy is an implementation of a NumPy-compatible multi-dimensional array on CUDA. | 2020-02-19 |

automat | public | self-service finite-state machines for the programmer on the go | 2020-02-19 |

r-tidymodels | public | The tidy modeling "verse" is a collection of packages for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the tidyverse. | 2020-02-19 |

draco | public | A library for compressing and decompressing 3D geometric meshes and point clouds | 2020-02-18 |

genpy | public | No Summary | 2020-02-18 |

r-float | public | R comes with a suite of utilities for linear algebra with "numeric" (double precision) vectors/matrices. However, sometimes single precision (or less!) is more than enough for a particular task. This package extends R's linear algebra facilities to include 32-bit float (single precision) data. Float vectors/matrices have half the precision of their "numeric"-type counterparts but are generally faster to numerically operate on, for a performance vs accuracy trade-off. The internal representation is an S4 class, which allows us to keep the syntax identical to that of base R's. Interaction between floats and base types for binary operators is generally possible; in these cases, type promotion always defaults to the higher precision. The package ships with copies of the single precision 'BLAS' and 'LAPACK', which are automatically built in the event they are not available on the system. | 2020-02-18 |

r-sweep | public | Tidies up the forecasting modeling and prediction work flow, extends the 'broom' package with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions for various forecasting models, and enables converting 'forecast' objects to "tidy" data frames with 'sw_sweep'. | 2020-02-18 |

psutil | public | A cross-platform process and system utilities module for Python | 2020-02-18 |

r-lgr | public | A flexible, feature-rich yet light-weight logging framework based on 'R6' classes. It supports hierarchical loggers, custom log levels, arbitrary data fields in log events, logging to plaintext, 'JSON', (rotating) files, memory buffers, and databases, as well as email and push notifications. For a full list of features with examples please refer to the package vignette. | 2020-02-18 |

requests-ecp | public | SAML/ECP authentication handler for python-requests | 2020-02-18 |

hs-process | public | An open-source Python package for geospatial processing of aerial hyperspectral imagery | 2020-02-18 |

pytest-flask-sqlalchemy | public | A pytest plugin for preserving test isolation in Flask-SQLAlchemy using database transactions. | 2020-02-18 |

r-tune | public | The ability to tune models is important. 'tune' contains functions and classes to be used in conjunction with other 'tidymodels' packages for finding reasonable values of hyper-parameters in models, pre-processing methods, and post-processing steps. | 2020-02-18 |

google-auth-oauthlib | public | Google Authentication Library, oauthlib integration with google-auth | 2020-02-18 |

awscli | public | Universal Command Line Environment for AWS. | 2020-02-18 |

boost-cpp | public | Free peer-reviewed portable C++ source libraries. | 2020-02-18 |

json_tricks | public | Extra features for Python's JSON: comments, order, numpy, pandas, datetimes, and many more! Simple but customizable. | 2020-02-18 |

boto3 | public | Amazon Web Services SDK for Python | 2020-02-18 |

jupyter_core | public | Core common functionality of Jupyter projects. | 2020-02-18 |

fmrib-unpack | public | The FMRIB UKBioBank Normalisation, Parsing And Cleaning Kit | 2020-02-18 |

r-tidyquant | public | Bringing financial analysis to the 'tidyverse'. The 'tidyquant' package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' and 'PerformanceAnalytics' package functions and returns the objects in the tidy 'tibble' format. The main advantage is being able to use quantitative functions with the 'tidyverse' functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See the 'tidyquant' website for more information, documentation and examples. | 2020-02-18 |

xeus-python | public | Jupyter kernel for the Python programming language based on xeus | 2020-02-18 |

lifetimes | public | Measure customer lifetime value in Python | 2020-02-18 |

pre-commit | public | A framework for managing and maintaining multi-language pre-commit hooks. | 2020-02-18 |

scikit-multiflow | public | A machine learning framework for multi-output/multi-label and stream data. | 2020-02-18 |

botocore | public | Low-level, data-driven core of boto 3. | 2020-02-18 |

jupytext | public | Jupyter notebooks as Markdown documents, Julia, Python or R scripts | 2020-02-18 |

paintera | public | Python command line launcher for Paintera | 2020-02-18 |

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