R-universe search: exports:subgraph

Showing 5 of total 5 results (show query)

igraph

igraph: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.

Maintained by Kirill Müller. Last updated 5 hours ago.

complex-networks graph-algorithms graph-theory mathematics network-analysis network-graph fortran libxml2 glpk openblas cpp

584 stars 21.14 score 31k scripts 1.9k dependents

cran

bnlearn: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/>.

Maintained by Marco Scutari. Last updated 3 months ago.

openblas

57 stars 7.72 score 32 dependents

kornl

gMCP:Graph Based Multiple Comparison Procedures

Functions and a graphical user interface for graphical described multiple test procedures.

Maintained by Kornelius Rohmeyer. Last updated 1 years ago.

openjdk

10 stars 7.31 score 105 scripts 2 dependents

merck

gMCPLite:Lightweight Graph Based Multiple Comparison Procedures

A lightweight fork of 'gMCP' with functions for graphical described multiple test procedures introduced in Bretz et al. (2009) <doi:10.1002/sim.3495> and Bretz et al. (2011) <doi:10.1002/bimj.201000239>. Implements a flexible function using 'ggplot2' to create multiplicity graph visualizations. Contains instructions of multiplicity graph and graphical testing for group sequential design, described in Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>, with necessary unit testing using 'testthat'.

Maintained by Nan Xiao. Last updated 1 years ago.

11 stars 5.79 score 14 scripts

mlindsk

ess:Efficient Stepwise Selection in Decomposable Models

An implementation of the ESS algorithm following Amol Deshpande, Minos Garofalakis, Michael I Jordan (2013) <arXiv:1301.2267>. The ESS algorithm is used for model selection in decomposable graphical models.

Maintained by Mads Lindskou. Last updated 3 years ago.

chow-liu-tree decomposable-models graphical-models model-selection cpp

1 stars 3.76 score 38 scripts 1 dependents