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

r-rngwell | public | It is a dedicated package to WELL pseudo random generators, which were introduced in Panneton et al. (2006), ``Improved Long-Period Generators Based on Linear Recurrences Modulo 2'', ACM Transactions on Mathematical Software. But this package is not intended to be used directly, you are strongly __encouraged__ to use the 'randtoolbox' package, which depends on this package. | 2020-01-21 |

txaio | public | Compatibility API between asyncio/Twisted/Trollius | 2020-01-21 |

r-networkdynamic | public | Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis. | 2020-01-21 |

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. | 2020-01-21 |

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. | 2020-01-21 |

r-amap | public | Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions). | 2020-01-21 |

r-sjstats | public | Collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort (like Cramer's V, Phi, or effect size statistics like Eta or Omega squared), or for which currently no functions available. Second, another focus lies on weighted variants of common statistical measures and tests like weighted standard error, mean, t-test, correlation, and more. | 2020-01-21 |

r-spacetime | public | Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal matching or aggregation, retrieving coordinates, print, summary, etc. | 2020-01-21 |

r-survey | public | Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Principal components, factor analysis. | 2020-01-21 |

r-bio3d | public | Utilities to process, organize and explore protein structure, sequence and dynamics data. Features include the ability to read and write structure, sequence and dynamic trajectory data, perform sequence and structure database searches, data summaries, atom selection, alignment, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, normal mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal mode and molecular dynamics data. In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data. Please refer to the URLs below for more information. | 2020-01-21 |

r-randomforestsrc | public | Fast OpenMP parallel computing of Breiman's random forests for survival, competing risks, regression and classification based on Ishwaran and Kogalur's popular random survival forests (RSF) package. Handles missing data and now includes multivariate, unsupervised forests, quantile regression and solutions for class imbalanced data. New fast interface using subsampling and confidence regions for variable importance. | 2020-01-21 |

r-gstat | public | Variogram modelling; simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions; supports sf and stars. | 2020-01-21 |

calculix | public | 3D Structural Finite Element Program | 2020-01-21 |

opencv | public | Computer vision and machine learning software library. | 2020-01-21 |

tiledb-py | public | Python interface to the TileDB sparse and dense multi-dimensional array storage manager | 2020-01-21 |

deap | public | Distributed Evolutionary Algorithms in Python | 2020-01-21 |

r-desctools | public | A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well. | 2020-01-21 |

ruamel.yaml | public | A YAML package for Python. It is a derivative of Kirill Simonov's PyYAML 3.11 which supports YAML1.1 | 2020-01-20 |

autopep8 | public | A tool that automatically formats Python code to conform to the PEP 8 style guide | 2020-01-20 |

r-blob | public | R's raw vector is useful for storing a single binary object. What if you want to put a vector of them in a data frame? The 'blob' package provides the blob object, a list of raw vectors, suitable for use as a column in data frame. | 2020-01-20 |

r-tree | public | Classification and regression trees. | 2020-01-20 |

r-rmarkdown | public | Convert R Markdown documents into a variety of formats. | 2020-01-20 |

r-depmixs4 | public | Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, <DOI:10.18637/jss.v036.i07>). | 2020-01-20 |

flasgger | public | Easy Swagger UI for your Flask API | 2020-01-20 |

editdistance | public | Fast implementation of the edit distance(Levenshtein distance) | 2020-01-20 |

r-foreign | public | Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files. | 2020-01-20 |

pytest | public | Simple and powerful testing with Python. | 2020-01-20 |

erlang | public | A programming language used to build massively scalable soft real-time systems with requirements on high availability. | 2020-01-20 |

tiledb | public | TileDB sparse and dense multi-dimensional array data management | 2020-01-20 |

r-iswr | public | Data sets and scripts for text examples and exercises in P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534. | 2020-01-20 |

r-xml | public | Many approaches for both reading and creating XML (and HTML) documents (including DTDs), both local and accessible via HTTP or FTP. Also offers access to an 'XPath' "interpreter". | 2020-01-20 |

highfive | public | Header-only C++ HDF5 interface | 2020-01-20 |

line_profiler | public | A module for monitoring memory usage of a python program | 2020-01-20 |

trimesh | public | Import, export, process, analyze and view triangular meshes. | 2020-01-20 |

xwebrtc | public | C++ backend for the jupyter webrtc widget | 2020-01-19 |

gast | public | A generic AST to represent Python2 and Python3's Abstract Syntax Tree(AST). | 2020-01-19 |

setuptools | public | Download, build, install, upgrade, and uninstall Python packages | 2020-01-19 |

numpy | public | Array processing for numbers, strings, records, and objects. | 2020-01-19 |

odfpy | public | Python API and tools to manipulate OpenDocument files | 2020-01-19 |

django-recaptcha | public | Django recaptcha form field/widget app. | 2020-01-19 |

tinyarray | public | Arrays of numbers for Python, optimized for small sizes | 2020-01-19 |

r-stanheaders | public | The C++ header files of the Stan project are provided by this package, but it contains little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is only useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models. | 2020-01-19 |

r-agricolae | public | Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster. | 2020-01-19 |

r-adehabitatlt | public | A collection of tools for the analysis of animal movements. | 2020-01-19 |

r-xts | public | Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability. | 2020-01-19 |

python-jsonrpc-server | public | A Python 2.7 and 3.4+ server implementation of the JSON RPC 2.0 protocol. | 2020-01-19 |

pytables | public | Brings together Python, HDF5 and NumPy to easily handle large amounts of data. | 2020-01-19 |

ipympl | public | Matplotlib Jupyter Extension | 2020-01-19 |

r-rcurl | public | A wrapper for 'libcurl' Provides functions to allow one to compose general HTTP requests and provides convenient functions to fetch URIs, get & post forms, etc. and process the results returned by the Web server. This provides a great deal of control over the HTTP/FTP/... connection and the form of the request while providing a higher-level interface than is available just using R socket connections. Additionally, the underlying implementation is robust and extensive, supporting FTP/FTPS/TFTP (uploads and downloads), SSL/HTTPS, telnet, dict, ldap, and also supports cookies, redirects, authentication, etc. | 2020-01-19 |

elasticsearch | public | Python client for Elasticsearch | 2020-01-19 |

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