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r-sparsebn
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public |
Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>. Designed to handle mixed experimental and observational data with thousands of variables with either continuous or discrete observations.
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2025-04-22 |
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r-discretecdalgorithm
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public |
Structure learning of Bayesian network using coordinate-descent algorithm. This algorithm is designed for discrete network assuming a multinomial data set, and we use a multi-logit model to do the regression. The algorithm is described in Gu, Fu and Zhou (2016) <arXiv:1403.2310>.
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2025-04-22 |
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r-ccdralgorithm
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Implementation of the CCDr (Concave penalized Coordinate Descent with reparametrization) structure learning algorithm as described in Aragam and Zhou (2015) <http://www.jmlr.org/papers/v16/aragam15a.html>. This is a fast, score-based method for learning Bayesian networks that uses sparse regularization and block-cyclic coordinate descent.
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2025-04-22 |
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r-distr
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public |
S4-classes and methods for distributions.
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2025-04-22 |
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r-fivethirtyeight
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public |
Datasets and code published by the data journalism website 'FiveThirtyEight' available at <https://github.com/fivethirtyeight/data>. Note that while we received guidance from editors at 'FiveThirtyEight', this package is not officially published by 'FiveThirtyEight'.
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2025-04-22 |
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xleaflet
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public |
C++ backend for the jupyter leaflet widget
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2025-04-22 |
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xplot
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public |
C++ backend for the bqplot 2-D plotting library
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2025-04-22 |
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r-sparsebnutils
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public |
A set of tools for representing and estimating sparse Bayesian networks from continuous and discrete data, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>.
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2025-04-22 |
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r-teachingdemos
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public |
Demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming.
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2025-04-22 |
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r-filematrix
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public |
Interface for working with large matrices stored in files, not in computer memory. Supports multiple non-character data types (double, integer, logical and raw) of various sizes (e.g. 8 and 4 byte real values). Access to parts of the matrix is done by indexing, exactly as with usual R matrices. Supports very large matrices. Tested on multi-terabyte matrices. Allows for more than 2^32 rows or columns. Allows for quick addition of extra columns to a filematrix. Cross-platform as the package has R code only.
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2025-04-22 |
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r-reordercluster
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public |
Tools for performing the leaf reordering for the dendrogram that preserves the hierarchical clustering result and at the same time tries to group instances from the same class together.
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2025-04-22 |
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r-startupmsg
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public |
Provides utilities to create or suppress start-up messages.
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2025-04-22 |
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ghc
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public |
Glorious Glasgow Haskell Compilation System
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2025-04-22 |
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r-fpp2
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public |
All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos <https://OTexts.org/fpp2/>. All packages required to run the examples are also loaded.
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2025-04-22 |
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r-rosm
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Download and plot Open Street Map <http://www.openstreetmap.org/>, Bing Maps <http://www.bing.com/maps> and other tiled map sources. Use to create basemaps quickly and add hillshade to vector-based maps.
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2025-04-22 |
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r-extradistr
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public |
Density, distribution function, quantile function and random generation for a number of univariate and multivariate distributions. This package implements the following distributions: Bernoulli, beta-binomial, beta-negative binomial, beta prime, Bhattacharjee, Birnbaum-Saunders, bivariate normal, bivariate Poisson, categorical, Dirichlet, Dirichlet-multinomial, discrete gamma, discrete Laplace, discrete normal, discrete uniform, discrete Weibull, Frechet, gamma-Poisson, generalized extreme value, Gompertz, generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t, Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy, Laplace, location-scale t, logarithmic, Lomax, multivariate hypergeometric, multinomial, negative hypergeometric, non-standard beta, normal mixture, Poisson mixture, Pareto, power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam, slash, triangular, truncated binomial, truncated normal, truncated Poisson, Tukey lambda, Wald, zero-inflated binomial, zero-inflated negative binomial, zero-inflated Poisson.
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2025-04-22 |
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r-prettymapr
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public |
Automates the process of creating a scale bar and north arrow in any package that uses base graphics to plot in R. Bounding box tools help find and manipulate extents. Finally, there is a function to automate the process of setting margins, plotting the map, scale bar, and north arrow, and resetting graphic parameters upon completion.
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2025-04-22 |
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r-rtweet
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An implementation of calls designed to collect and organize Twitter data via Twitter's REST and stream Application Program Interfaces (API), which can be found at the following URL: <https://developer.twitter.com/en/docs>.
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2025-04-22 |
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r-vctrs
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Defines new notions of prototype and size that are used to provide tools for consistent and well-founded type-coercion and size-recycling, and are in turn connected to ideas of type- and size-stability useful for analysing function interfaces.
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2025-04-22 |
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r-lobstr
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A set of tools for inspecting and understanding R data structures inspired by str(). Includes ast() for visualizing abstract syntax trees, ref() for showing shared references, cst() for showing call stack trees, and obj_size() for computing object sizes.
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2025-04-22 |
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r-textrecipes
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public |
Converting text to numerical features requires specifically created procedures, which are implemented as steps according to the 'recipes' package. These steps allows for tokenization, filtering, counting (tf and tfidf) and feature hashing.
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2025-04-22 |
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ldas-tools-filters
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public |
Filters library used by ldas-tools
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2025-04-22 |
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slang
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public |
S-Lang allows developers to create robust multi-platform software
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2025-04-22 |
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r-lightgbm
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public |
Tree based algorithms can be improved by introducing boosting frameworks. 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. This package offers an R interface to work with it. It is designed to be distributed and efficient with the following advantages: 1. Faster training speed and higher efficiency. 2. Lower memory usage. 3. Better accuracy. 4. Parallel learning supported. 5. Capable of handling large-scale data. In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions. Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.
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2025-04-22 |
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r-bezier
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The bezier package is a toolkit for working with Bezier curves and splines. The package provides functions for point generation, arc length estimation, degree elevation and curve fitting.
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2025-04-22 |
