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

r-gss | public | A comprehensive package for structural multivariate function estimation using smoothing splines. | 2019-08-20 |

numcosmo | public | NumCosmo is a free software C library whose main purposes are to test cosmological models using observational data and to provide a set of tools to perform cosmological calculations. | 2019-08-20 |

micromagneticmodel | public | Python domain-specific language for defining micromagnetic models. | 2019-08-20 |

ubermagtable | public | Reading and merging of OOMMF .odt and mumax3 .txt files | 2019-08-20 |

r-urca | public | No Summary | 2019-08-20 |

spiceypy | public | The NASA JPL NAIF SPICE toolkit wrapper written in Python | 2019-08-20 |

r-seriation | public | Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT). | 2019-08-20 |

r-tseries | public | Time series analysis and computational finance. | 2019-08-20 |

r-rrcov | public | Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point. | 2019-08-20 |

r-ddalpha | public | Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included. | 2019-08-20 |

r-hmisc | public | Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, and recoding variables. | 2019-08-20 |

r-fields | public | For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research. See the Fields URL for a vignette on using this package and some background on spatial statistics. | 2019-08-20 |

r-vegan | public | Ordination methods, diversity analysis and other functions for community and vegetation ecologists. | 2019-08-20 |

r-quantreg | public | Estimation and inference methods for models of conditional quantiles: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also included. | 2019-08-20 |

root | public | Data Analysis Framework | 2019-08-20 |

r-randomfieldsutils | public | Various utilities are provided that might be used in spatial statistics and elsewhere. It delivers a method for solving linear equations that checks the sparsity of the matrix before any algorithm is used. Furthermore, it includes the Struve functions. | 2019-08-20 |

r-iso | public | Linear order and unimodal order (univariate) isotonic regression; bivariate isotonic regression with linear order on both variables. | 2019-08-20 |

r-logspline | public | Contains routines for logspline density estimation. The function oldlogspline() uses the same algorithm as the logspline package version 1.0.x; i.e. the Kooperberg and Stone (1992) algorithm (with an improved interface). The recommended routine logspline() uses an algorithm from Stone et al (1997) <DOI:10.1214/aos/1031594728>. | 2019-08-20 |

r-lsei | public | It contains functions that solve least squares linear regression problems under linear equality/inequality constraints. Functions for solving quadratic programming problems are also available, which transform such problems into least squares ones first. It is developed based on the 'Fortran' program of Lawson and Hanson (1974, 1995), which is public domain and available at <http | 2019-08-20 |

r-catboost | public | CatBoost is a machine learning algorithm that uses gradient boosting on decision trees. It is available as an open source library. | 2019-08-20 |

mpi4py | public | Python bindings for MPI | 2019-08-20 |

catboost | public | Gradient boosting on decision trees library | 2019-08-20 |

soupsieve | public | A modern CSS selector implementation for BeautifulSoup | 2019-08-20 |

r-amap | public | Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions). | 2019-08-20 |

r-drake | public | A general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website <https://ropensci.github.io/drake/> and the online manual <https://ropenscilabs.github.io/drake-manual/>. | 2019-08-20 |

r-ash | public | David Scott's ASH routines ported from S-PLUS to R. | 2019-08-20 |

r-fastcluster | public | This is a two-in-one package which provides interfaces to both R and Python. It implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines: linkage() in the SciPy package 'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the 'flashClust' package. It provides the same functionality with the benefit of a much faster implementation. Moreover, there are memory-saving routines for clustering of vector data, which go beyond what the existing packages provide. For information on how to install the Python files, see the file INSTALL in the source distribution. | 2019-08-20 |

r-classint | public | Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes. | 2019-08-20 |

cmake | public | CMake is an extensible, open-source system that manages the build process | 2019-08-20 |

pybloom_live | public | Scalable Bloom Filter implemented in Python | 2019-08-20 |

r-sm | public | This is software linked to the book 'Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations' Oxford University Press. | 2019-08-20 |

pyomeca | public | Pyomeca is a python library allowing to carry out a complete biomechanical analysis; in a simple, logical and concise way | 2019-08-20 |

r-statmod | public | A collection of algorithms and functions to aid statistical modeling. Includes growth curve comparisons, limiting dilution analysis (aka ELDA), mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models. Includes advanced generalized linear model functions that implement secure convergence, dispersion modeling and Tweedie power-law families. | 2019-08-20 |

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). | 2019-08-20 |

r-qap | public | Implements heuristics for the Quadratic Assignment Problem (QAP). Currently only a simulated annealing heuristic is available. | 2019-08-20 |

r-pan | public | Multiple imputation for multivariate panel or clustered data. | 2019-08-20 |

r-flashclust | public | Fast implementation of hierarchical clustering | 2019-08-20 |

r-mvtnorm | public | Computes multivariate normal and t probabilities, quantiles, random deviates and densities. | 2019-08-20 |

clang_bootstrap_osx-64 | public | clang compilers for conda-build 3 | 2019-08-20 |

clangxx_osx-64 | public | clang compilers for conda-build 3 | 2019-08-20 |

clang_osx-64 | public | clang compilers for conda-build 3 | 2019-08-20 |

r-lars | public | No Summary | 2019-08-20 |

r-nlme | public | Fit and compare Gaussian linear and nonlinear mixed-effects models. | 2019-08-20 |

r-cluster | public | Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data". | 2019-08-20 |

r-minqa | public | No Summary | 2019-08-20 |

r-kernsmooth | public | No Summary | 2019-08-20 |

r-mnormt | public | No Summary | 2019-08-20 |

r-sparsem | public | Some basic linear algebra functionality for sparse matrices is provided: including Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products. | 2019-08-20 |

r-stanheaders | public | The C++ header files of the Stan project are provided by this package, but it contains no R code or function documentation. There is a shared object containing part of the 'CVODES' library, but it 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. | 2019-08-20 |

r-robustbase | public | "Essential" Robust Statistics. Tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006. | 2019-08-20 |

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