Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

Found 959 packages in 3.51 seconds

igraph — by Kirill Müller, a month ago

Network Analysis and Visualization

Routines for simple graphs and network analysis. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more.

network — by Carter T. Butts, 5 months ago

Classes for Relational Data

Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.

DiagrammeR — by Richard Iannone, 3 months ago

Graph/Network Visualization

Build graph/network structures using functions for stepwise addition and deletion of nodes and edges. Work with data available in tables for bulk addition of nodes, edges, and associated metadata. Use graph selections and traversals to apply changes to specific nodes or edges. A wide selection of graph algorithms allow for the analysis of graphs. Visualize the graphs and take advantage of any aesthetic properties assigned to nodes and edges.

RCurl — by CRAN Team, 3 months ago

General Network (HTTP/FTP/...) Client Interface for R

A wrapper for 'libcurl' < https://curl.se/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.

ggraph — by Thomas Lin Pedersen, a month ago

An Implementation of Grammar of Graphics for Graphs and Networks

The grammar of graphics as implemented in ggplot2 is a poor fit for graph and network visualizations due to its reliance on tabular data input. ggraph is an extension of the ggplot2 API tailored to graph visualizations and provides the same flexible approach to building up plots layer by layer.

sna — by Carter T. Butts, 4 months ago

Tools for Social Network Analysis

A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.

nnet — by Brian Ripley, a year ago

Feed-Forward Neural Networks and Multinomial Log-Linear Models

Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.

torch — by Daniel Falbel, 4 months ago

Tensors and Neural Networks with 'GPU' Acceleration

Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.

ergm — by Pavel N. Krivitsky, 4 months ago

Fit, Simulate and Diagnose Exponential-Family Models for Networks

An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008) and Krivitsky, Hunter, Morris, and Klumb (2023) .

bnlearn — by Marco Scutari, a month ago

Bayesian Network Structure Learning, Parameter Learning and Inference

Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from < https://www.bnlearn.com/>.