Spatial Modeling on Stream Networks

Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., (2010) .) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included.

Bootstrap Methods for Various Network Estimation Routines

Bootstrap methods to assess accuracy and stability of estimated network structures and centrality indices . Allows for flexible specification of any undirected network estimation procedure in R, and offers default sets for various estimation routines.

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>).

Siena - Simulation Investigation for Empirical Network Analysis

The main purpose of this package is to perform simulation-based estimation of stochastic actor-oriented models for longitudinal network data collected as panel data. Dependent variables can be single or multivariate networks, which can be directed, non-directed, or two-mode; and associated actor variables. There are also functions for testing parameters and checking goodness of fit. An overview of these models is given in Snijders (2017), .

Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis

Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 ), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 ), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 ). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.

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) for a presentation of the package; Rinaldo et al. (2014) for a theoretical overview on the OCN concept; Furrer and Sain (2010) for the construct used.

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.

Weighted, Two-Mode, and Longitudinal Networks Analysis

Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.