Found 1089 packages in 0.03 seconds
Construction, Simulation and Analysis of Boolean Networks
Functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks
Fault Tolerant Simple Network of Workstations
Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed. Dynamic cluster size is also available.
Quantile Regression Neural Network
Fit quantile regression neural network models with optional
left censoring, partial monotonicity constraints, generalized additive
model constraints, and the ability to fit multiple non-crossing quantile
functions following Cannon (2011)
Bootstrap Methods for Various Network Estimation Routines
Bootstrap methods to assess accuracy and stability of estimated network structures
and centrality indices
Interactive 3D Scatter Plots, Networks and Globes
Create interactive 3D scatter plots, network plots, and globes using the 'three.js' visualization library (< https://threejs.org>).
Software Tools for the Statistical Analysis of Network Data
Statnet is a collection of packages for statistical network analysis that are designed to work together because they share common data representations and 'API' design. They provide an integrated set of tools for the representation, visualization, analysis, and simulation of many different forms of network data. This package is designed to make it easy to install and load the key 'statnet' packages in a single step. Learn more about 'statnet' at < http://www.statnet.org>. Tutorials for many packages can be found at < https://github.com/statnet/Workshops/wiki>. For an introduction to functions in this package, type help(package='statnet').
Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation
Fork of qgraph - Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012)
Generalized Multipartite Networks
We define generalized multipartite networks as the joint observation of several networks implying some common pre-specified groups of individuals. The aim is to fit an adapted version of the popular stochastic block model to multipartite networks, as described in Bar-hen, Barbillon and Donnet (2020)
Optimal Channel Networks
Generate and analyze Optimal Channel Networks (OCNs):
oriented spanning trees reproducing all scaling features characteristic
of real, natural river networks. As such, they can be used in a variety
of numerical experiments in the fields of hydrology, ecology and
epidemiology. See Carraro et al. (2020)
Clustering on Network of Samples
Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at < https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.